Remote Sensing

20 pages, 1247 KiB

Open AccessArticle

Wavelength Extension of the Optimized Asymmetric-Order Vegetation Isoline Equation to Cover the Range from Visible to Near-Infrared

by Munenori Miura, Kenta Obata and Hiroki Yoshioka

Remote Sens. 2022, 14(9), 2289; https://doi.org/10.3390/rs14092289 - 9 May 2022

Viewed by 1883

Abstract

Vegetation isoline equations describe analytical relationships between two reflectances of different wavelengths. Their applications range from retrievals of biophysical parameters to the derivation of the inter-sensor relationships of spectral vegetation indexes. Among the three variants of vegetation isoline equations introduced thus far, the [...] Read more.

Vegetation isoline equations describe analytical relationships between two reflectances of different wavelengths. Their applications range from retrievals of biophysical parameters to the derivation of the inter-sensor relationships of spectral vegetation indexes. Among the three variants of vegetation isoline equations introduced thus far, the optimized asymmetric-order vegetation isoline equation is the newest and is known to be the most accurate. This accuracy assessment, however, has been performed only for the wavelength pair of red and near-infrared (NIR) bands fixed at ∼655 nm and ∼865 nm, respectively. The objective of this study is to extend this wavelength limitation. An accuracy assessment was therefore performed over a wider range of wavelengths, from 400 to 1200 nm. The optimized asymmetric-order vegetation isoline equation was confirmed to demonstrate the highest accuracy among the three isolines for all the investigated wavelength pairs. The second-best equation, the asymmetric-order isoline equation, which does not include an optimization factor, was not superior to the least-accurate equation (i.e., the first-order isoline equation) in some cases. This tendency was prominent when the reflectances of the two wavelengths were similar. By contrast, the optimized asymmetric-order vegetation isoline showed stable performance throughout this study. A single factor introduced into the optimized asymmetric-order isoline equation was concluded to effectively reduce errors in the isoline for all the wavelength combinations examined in this study. Full article

(This article belongs to the Section Biogeosciences Remote Sensing)

► Show Figures

Figure 1

18 pages, 6047 KiB

Open AccessArticle

UAV-Borne Imagery Can Supplement Airborne Lidar in the Precise Description of Dynamically Changing Shrubland Woody Vegetation

by Tomáš Klouček, Petr Klápště, Jana Marešová and Jan Komárek

Remote Sens. 2022, 14(9), 2287; https://doi.org/10.3390/rs14092287 - 9 May 2022

Cited by 4 | Viewed by 2789

Abstract

Airborne laser scanning (ALS) is increasingly used for detailed vegetation structure mapping; however, there are many local-scale applications where it is economically ineffective or unfeasible from the temporal perspective. Unmanned aerial vehicles (UAVs) or airborne imagery (AImg) appear to be promising alternatives, but [...] Read more.

Airborne laser scanning (ALS) is increasingly used for detailed vegetation structure mapping; however, there are many local-scale applications where it is economically ineffective or unfeasible from the temporal perspective. Unmanned aerial vehicles (UAVs) or airborne imagery (AImg) appear to be promising alternatives, but only a few studies have examined this assumption outside economically exploited areas (forests, orchards, etc.). The main aim of this study was to compare the usability of normalized digital surface models (nDSMs) photogrammetrically derived from UAV-borne and airborne imagery to those derived from low- (1–2 pts/m²) and high-density (ca. 20 pts/m²) ALS-scanning for the precise local-scale modelling of woody vegetation structures (the number and height of trees/shrubs) across six dynamically changing shrubland sites. The success of the detection of woody plant tops was initially almost 100% for UAV-based models; however, deeper analysis revealed that this was due to the fact that omission and commission errors were approximately equal and the real accuracy was approx. 70% for UAV-based models compared to 95.8% for the high-density ALS model. The percentage mean absolute errors (%MAE) of shrub/tree heights derived from UAV data ranged between 12.2 and 23.7%, and AImg height accuracy was relatively lower (%MAE: 21.4–47.4). Combining UAV-borne or AImg-based digital surface models (DSM) with ALS-based digital terrain models (DTMs) significantly improved the nDSM height accuracy (%MAE: 9.4–13.5 and 12.2–25.0, respectively) but failed to significantly improve the detection of the number of individual shrubs/trees. The height accuracy and detection success using low- or high-density ALS did not differ. Therefore, we conclude that UAV-borne imagery has the potential to replace custom ALS in specific local-scale applications, especially at dynamically changing sites where repeated ALS is costly, and the combination of such data with (albeit outdated and sparse) ALS-based digital terrain models can further improve the success of the use of such data. Full article

► Show Figures

Figure 1

20 pages, 63893 KiB

Open AccessArticle

Integration and Comparison Methods for Multitemporal Image-Based 2D Annotations in Linked 3D Building Documentation

by Jakob Taraben and Guido Morgenthal

Remote Sens. 2022, 14(9), 2286; https://doi.org/10.3390/rs14092286 - 9 May 2022

Cited by 4 | Viewed by 2269

Abstract

Data acquisition systems and methods to capture high-resolution images or reconstruct 3D point clouds of existing structures are an effective way to document their as-is condition. These methods enable a detailed analysis of building surfaces, providing precise 3D representations. However, for the condition [...] Read more.

Data acquisition systems and methods to capture high-resolution images or reconstruct 3D point clouds of existing structures are an effective way to document their as-is condition. These methods enable a detailed analysis of building surfaces, providing precise 3D representations. However, for the condition assessment and documentation, damages are mainly annotated in 2D representations, such as images, orthophotos, or technical drawings, which do not allow for the application of a 3D workflow or automated comparisons of multitemporal datasets. In the available software for building heritage data management and analysis, a wide range of annotation and evaluation functions are available, but they also lack integrated post-processing methods and systematic workflows. The article presents novel methods developed to facilitate such automated 3D workflows and validates them on a small historic church building in Thuringia, Germany. Post-processing steps using photogrammetric 3D reconstruction data along with imagery were implemented, which show the possibilities of integrating 2D annotations into 3D documentations. Further, the application of voxel-based methods on the dataset enables the evaluation of geometrical changes of multitemporal annotations in different states and the assignment to elements of scans or building models. The proposed workflow also highlights the potential of these methods for condition assessment and planning of restoration work, as well as the possibility to represent the analysis results in standardised building model formats. Full article

(This article belongs to the Special Issue Applications of Laser Scanning and Photogrammetry in Civil Engineering and Architecture: Beyond 3D Modeling)

► Show Figures

Graphical abstract

19 pages, 13367 KiB

Open AccessTechnical Note

An Algebraic Comparison of Synthetic Aperture Interferometry and Digital Beam Forming in Imaging Radiometry

by Eric Anterrieu, Pierre Lafuma and Nicolas Jeannin

Remote Sens. 2022, 14(9), 2285; https://doi.org/10.3390/rs14092285 - 9 May 2022

Cited by 5 | Viewed by 2153

Abstract

Digital beam forming (DBF) and synthetic aperture interferometry (SAI) are signal processing techniques that mix the signals collected by an antenna array to obtain high-resolution images with the aid of a computer. This note aims at comparing these two approaches from an algebraic [...] Read more.

Digital beam forming (DBF) and synthetic aperture interferometry (SAI) are signal processing techniques that mix the signals collected by an antenna array to obtain high-resolution images with the aid of a computer. This note aims at comparing these two approaches from an algebraic perspective with the illustrations of simulations conducted at microwaves frequencies within the frame of the Soil Moisture and Ocean Salinity (SMOS) mission. Although the two techniques are using the same signals and sharing the same goal, there are several differences that deserve attention. From the algebraic point of view, it is the case for the singular values distributions of the respective modeling matrices which are both rank-deficient but do not have the same sensitivity to the diversity of the array’s elementary antennas radiation patterns. As a consequence of this difference, the level and the angular signature of the reconstruction floor error are significantly lower with the DBF paradigm than with the SAI one. Full article

► Show Figures

Figure 1

23 pages, 8412 KiB

Open AccessArticle

A Novel Ultra−High Resolution Imaging Algorithm Based on the Accurate High−Order 2−D Spectrum for Space−Borne SAR

by Tao He, Lei Cui, Pengbo Wang, Yanan Guo and Lei Zhuang

Remote Sens. 2022, 14(9), 2284; https://doi.org/10.3390/rs14092284 - 9 May 2022

Viewed by 2156

Abstract

Ultra−high spatial resolution, which can bring more detail to ground observation, is a constant pursuit of the modern space−borne synthetic aperture radar. However, the exact imaging in this case has always been a complex technical problem due to its complicated imaging geometry and [...] Read more.

Ultra−high spatial resolution, which can bring more detail to ground observation, is a constant pursuit of the modern space−borne synthetic aperture radar. However, the exact imaging in this case has always been a complex technical problem due to its complicated imaging geometry and signal structure. To achieve those applications’ strict requirements, a novel ultra−high resolution imaging algorithm based on an accurate high−order 2−D spectrum is presented in this paper. The only first two Doppler parameters needed as range models in the defective spectrum are replaced by a polynomial range model, which can derive coefficients from the relative motion between the radar and the targets. Then, the new spectrum is calculated through the Lagrange inversion formula. Based on this, the novel imaging algorithm is elaborated in detail as follows: The range high−order term of the spectrum is compensated completely, and the range chirp rate space variance is eliminated by the cubic phase term. Two steps of range cell migration correct are applied in this algorithm before and after the range compression; one is the traditional linear chirp scaling method, and another is the interpolation to correct the quadratic range cell migration introduced by the range chirp rate equalization. The simulation results illustrate that the proposed algorithm can handle the exact imaging processing with a 0.25 m resolution around the azimuth and range in 2 km

\times

6 km, which validates the feasibility of the proposed algorithm. Full article

(This article belongs to the Topic Advances in Environmental Remote Sensing)

► Show Figures

Graphical abstract

22 pages, 6763 KiB

Open AccessArticle

A Framework for Survey Planning Using Portable Unmanned Aerial Vehicles (pUAVs) in Coastal Hydro-Environment

by Ha Linh Trinh, Hieu Trung Kieu, Hui Ying Pak, Dawn Sok Cheng Pang, Angel Anisa Cokro and Adrian Wing-Keung Law

Remote Sens. 2022, 14(9), 2283; https://doi.org/10.3390/rs14092283 - 9 May 2022

Cited by 7 | Viewed by 3219

Abstract

Recently, remote sensing using survey-grade UAVs has been gaining tremendous momentum in applications for the coastal hydro-environment. UAV-based remote sensing provides high spatial and temporal resolutions and flexible operational availability compared to other means, such as satellite imagery or point-based in situ measurements. [...] Read more.

Recently, remote sensing using survey-grade UAVs has been gaining tremendous momentum in applications for the coastal hydro-environment. UAV-based remote sensing provides high spatial and temporal resolutions and flexible operational availability compared to other means, such as satellite imagery or point-based in situ measurements. As strict requirements and government regulations are imposed for every UAV survey, detailed survey planning is essential to ensure safe operations and seamless coordination with other activities. This study established a comprehensive framework for the planning of efficient UAV deployments in coastal areas, which was based on recent on-site survey experiences with a portable unmanned aerial vehicle (pUAV) that was carrying a heavyweight spectral sensor. The framework was classified into three main categories: (i) pre-survey considerations (i.e., administrative preparation and UAV airframe details); (ii) execution strategies (i.e., parameters and contingency planning); and (iii) environmental effects (i.e., weather and marine conditions). The implementation and verification of the framework were performed using a UAV–airborne spectral sensing exercise for water quality monitoring in Singapore. The encountered challenges and the mitigation practices that were developed from the actual field experiences were integrated into the framework to advance the ease of UAV deployment for coastal monitoring and improve the acquisition process of high-quality remote sensing images. Full article

(This article belongs to the Topic Autonomy for Enabling the Next Generation of UAVs)

► Show Figures

Figure 1

20 pages, 14921 KiB

Open AccessArticle

INDMF Based Regularity Calculation Method and Its Application in the Recognition of Typical Loess Landforms

by Sheng Jiang, Xiaoli Huang and Ling Jiang

Remote Sens. 2022, 14(9), 2282; https://doi.org/10.3390/rs14092282 - 9 May 2022

Cited by 1 | Viewed by 1820

Abstract

The topographical morphology of the loess landform on the Loess Plateau exhibits remarkable textural features at different spatial scales. However, existing topographic texture analysis studies on the Loess Plateau are usually dominated by statistical characteristics and are missing structural characteristics. At the same [...] Read more.

The topographical morphology of the loess landform on the Loess Plateau exhibits remarkable textural features at different spatial scales. However, existing topographic texture analysis studies on the Loess Plateau are usually dominated by statistical characteristics and are missing structural characteristics. At the same time, there is a lack of regularity calculation methods for DEM digital terrain analysis. Taking the Loess Plateau as the study area, a regularity calculation method based on the improved normalized distance matching function (INDMF) is proposed and applied to the classification of a loess landform. The regularity calculation method used in this study (INDMF regularity) mainly includes two key steps. Step 1 calculates the INDMF sequence value and the peak and valley values for the terrain data. Step 2 calculates the significant peak and valley, constructs the significant peak and valley sequences, and then obtains the regularity using the normalised ratio value. The experimental results show that the proposed method has good anti-interference ability and can effectively extract the regularity of the main landform unit. Compared with previous methods, adding structural features (i.e., INDMF regularity) can effectively distinguish loess hill and loess ridge in the hilly and gully region. For the loess hill and loess ridge, the recognition rates of the proposed method are 84.62% and 92.86%, respectively. Combined with the existing topographic characteristics, the proposed INDMF regularity is a topographic structure feature extraction method that can effectively discriminate between loess hill and loess ridge areas on the Loess Plateau. Full article

(This article belongs to the Special Issue Quantifying Geomorphological Processes Using Remote Sensing Techniques)

► Show Figures

Figure 1

13 pages, 34286 KiB

Open AccessCommunication

Augmentation-Based Methodology for Enhancement of Trees Map Detalization on a Large Scale

by Svetlana Illarionova, Dmitrii Shadrin, Vladimir Ignatiev, Sergey Shayakhmetov, Alexey Trekin and Ivan Oseledets

Remote Sens. 2022, 14(9), 2281; https://doi.org/10.3390/rs14092281 - 9 May 2022

Cited by 14 | Viewed by 2434

Abstract

Remote sensing tasks play a very important role in the domain of sensing and measuring, and can be very specific. Advances in computer vision techniques allow for the extraction of various information from remote sensing satellite imagery. This information is crucial in making [...] Read more.

Remote sensing tasks play a very important role in the domain of sensing and measuring, and can be very specific. Advances in computer vision techniques allow for the extraction of various information from remote sensing satellite imagery. This information is crucial in making quantitative and qualitative assessments for monitoring of forest clearing in protected areas for power lines, as well as for environmental analysis, in particular for making assessments of carbon footprint, which is a highly relevant task. Solving these problems requires precise segmentation of the forest mask. Although forest mask extraction from satellite data has been considered previously, no open-access applications are able to provide the high-detailed forest mask. Detailed forest masks are usually obtained using unmanned aerial vehicles (UAV) that set particular limitations such as cost and inapplicability for vast territories. In this study, we propose a novel neural network-based approach for high-detailed forest mask creation. We implement an object-based augmentation technique for a minimum amount of labeled high-detailed data. Using this augmented data we fine-tune the models, which are trained on a large forest dataset with less precise labeled masks. The provided algorithm is tested for multiple territories in Russia. The

F 1

-

s c o r e

, for small details (such as individual trees) was improved to

0.929

compared to the baseline score of

0.856

. The developed model is available in an SAAS platform. The developed model allows a detailed and precise forest mask to be easily created, which then be used for solving various applied problems. Full article

(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing)

► Show Figures

Figure 1

21 pages, 5105 KiB

Open AccessArticle

A Continuous Change Tracker Model for Remote Sensing Time Series Reconstruction

by Yangjian Zhang, Li Wang, Yuanhuizi He, Ni Huang, Wang Li, Shiguang Xu, Quan Zhou, Wanjuan Song, Wensheng Duan, Xiaoyue Wang, Shakir Muhammad, Biswajit Nath, Luying Zhu, Feng Tang, Huilin Du, Lei Wang and Zheng Niu

Remote Sens. 2022, 14(9), 2280; https://doi.org/10.3390/rs14092280 - 9 May 2022

Cited by 1 | Viewed by 2443

Abstract

It is hard for current time series reconstruction methods to achieve the balance of high-precision time series reconstruction and explanation of the model mechanism. The goal of this paper is to improve the reconstruction accuracy with a well-explained time series model. Thus, we [...] Read more.

It is hard for current time series reconstruction methods to achieve the balance of high-precision time series reconstruction and explanation of the model mechanism. The goal of this paper is to improve the reconstruction accuracy with a well-explained time series model. Thus, we developed a function-based model, the CCTM (Continuous Change Tracker Model) model, that can achieve high precision in time series reconstruction by tracking the time series variation rate. The goal of this paper is to provide a new solution for high-precision time series reconstruction and related applications. To test the reconstruction effects, the model was applied to four types of datasets: normalized difference vegetation index (NDVI), gross primary productivity (GPP), leaf area index (LAI), and MODIS surface reflectance (MSR). Several new observations are as follows. First, the CCTM model is well explained and based on the second-order derivative theorem, which divides the yearly time series into four variation types including uniform variations, decelerated variations, accelerated variations, and short-periodical variations, and each variation type is represented by a designed function. Second, the CCTM model provides much better reconstruction results than the Harmonic model on the NDVI, GPP, MSR, and LAI datasets for the seasonal segment reconstruction. The combined use of the Savitzky–Golay filter and the CCTM model is better than the combinations of the Savitzky–Golay filter with other models. Third, the Harmonic model has the best trend-fitting ability on the yearly time series dataset, with the highest R-Square and the lowest RMSE among the four function fitting models. However, with seasonal piecewise fitting, the four models all achieved high accuracy, and the CCTM performs the best. Fourth, the CCTM model should also be applied to time series image compression, two compression patterns with 24 coefficients and 6 coefficients respectively are proposed. The daily MSR dataset can achieve a compression ratio of 15 by using the 6-coefficients method. Finally, the CCTM model also has the potential to be applied to change detection, trend analysis, and phenology and seasonal characteristics extractions. Full article

(This article belongs to the Section Environmental Remote Sensing)

► Show Figures

Graphical abstract

48 pages, 16390 KiB

Open AccessEditor’s ChoiceReview

Remote Sensing of Geomorphodiversity Linked to Biodiversity—Part III: Traits, Processes and Remote Sensing Characteristics

by Angela Lausch, Michael E. Schaepman, Andrew K. Skidmore, Eusebiu Catana, Lutz Bannehr, Olaf Bastian, Erik Borg, Jan Bumberger, Peter Dietrich, Cornelia Glässer, Jorg M. Hacker, Rene Höfer, Thomas Jagdhuber, Sven Jany, András Jung, Arnon Karnieli, Reinhard Klenke, Toralf Kirsten, Uta Ködel, Wolfgang Kresse, Ulf Mallast, Carsten Montzka, Markus Möller, Hannes Mollenhauer, Marion Pause, Minhaz Rahman, Franziska Schrodt, Christiane Schmullius, Claudia Schütze, Peter Selsam, Ralf-Uwe Syrbe, Sina Truckenbrodt, Michael Vohland, Martin Volk, Thilo Wellmann, Steffen Zacharias and Roland Baatz Show full author list Hide full author list

Remote Sens. 2022, 14(9), 2279; https://doi.org/10.3390/rs14092279 - 9 May 2022

Cited by 20 | Viewed by 5838

Abstract

Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in [...] Read more.

Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed. Full article

(This article belongs to the Special Issue Monitoring of Status and Disturbances of Bio- and Geodiversity, Their Traits and Interactions Using Remote Sensing)

► Show Figures

Figure 1

21 pages, 9410 KiB

Open AccessArticle

Estimates of Hyperspectral Surface and Underwater UV Planar and Scalar Irradiances from OMI Measurements and Radiative Transfer Computations

by Alexander Vasilkov, Nickolay Krotkov, David Haffner, Zachary Fasnacht and Joanna Joiner

Remote Sens. 2022, 14(9), 2278; https://doi.org/10.3390/rs14092278 - 9 May 2022

Cited by 2 | Viewed by 2702

Abstract

Quantitative assessment of the UV effects on aquatic ecosystems requires an estimate of the in-water hyperspectral radiation field. Solar UV radiation in ocean waters is estimated on a global scale by combining extraterrestrial solar irradiance from the Total and Spectral Solar Irradiance Sensor [...] Read more.

Quantitative assessment of the UV effects on aquatic ecosystems requires an estimate of the in-water hyperspectral radiation field. Solar UV radiation in ocean waters is estimated on a global scale by combining extraterrestrial solar irradiance from the Total and Spectral Solar Irradiance Sensor (TSIS-1), satellite estimates of cloud/surface reflectivity, ozone from the Ozone Monitoring Instrument (OMI) and in-water chlorophyll concentration from the Moderate Resolution Imaging Spectroradiometer (MODIS) with radiative transfer computations in the ocean-atmosphere system. A comparison of the estimates of collocated OMI-derived surface irradiance with Marine Optical Buoy (MOBY) measurements shows a good agreement within 5% for different seasons. To estimate scalar irradiance at the ocean surface and in water, we propose scaling the planar irradiance, calculated from satellite observation, on the basis of Hydrolight computations. Hydrolight calculations show that the diffuse attenuation coefficients of scalar and planar irradiance with depth are quite close to each other. That is why the differences between the planar penetration and scalar penetration depths are small and do not exceed a couple of meters. A dominant factor defining the UV penetration depths is chlorophyll concentration. There are other constituents in water that absorb in addition to chlorophyll; the absorption from these constituents can be related to that of chlorophyll in Case I waters using an inherent optical properties (IOP) model. Other input parameters are less significant. The DNA damage penetration depths vary from a few meters in areas of productive waters to about 30–35 m in the clearest waters. A machine learning approach (an artificial neural network, NN) was developed based on the full physical algorithm for computational efficiency. The NN shows a very good performance in predicting the penetration depths (within 2%). Full article

(This article belongs to the Topic Advances in Environmental Remote Sensing)

► Show Figures

Figure 1

26 pages, 9706 KiB

Open AccessArticle

Identification of Coupling Relationship between Ecosystem Services and Urbanization for Supporting Ecological Management: A Case Study on Areas along the Yellow River of Henan Province

by Hejie Wei, Dong Xue, Junchang Huang, Mengxue Liu and Ling Li

Remote Sens. 2022, 14(9), 2277; https://doi.org/10.3390/rs14092277 - 9 May 2022

Cited by 25 | Viewed by 3192

Abstract

Urbanization has an important effect on ecosystem services (ESs) and identifying the relationship between urbanization and ESs can provide a decision-making reference for regional ecological protection and management. Taking the areas along the Yellow River of Henan Province (AYRHP) as a research area, [...] Read more.

Urbanization has an important effect on ecosystem services (ESs) and identifying the relationship between urbanization and ESs can provide a decision-making reference for regional ecological protection and management. Taking the areas along the Yellow River of Henan Province (AYRHP) as a research area, a coupling system of ESs and urbanization is established in this study to reveal the coupling relationship between the two. ESs are estimated by using Carnegie–Ames–Stanford approach, revision universal soil loss equation, and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models. The urbanization level is evaluated from three dimensions, namely, population, economy, and land. The coupling coordination relationship between various ESs and urbanization in AYRHP is quantified from 2000 to 2018 on the county scale based on the coupling coordination degree (CCD) model. The lead–lag relationship between ESs and urbanization is identified by using the relative development degree model, and ecological management zoning is conducted. Results show that in the study period, net primary production (NPP), soil conservation, and food production are increased, whereas water yield is decreased. In the study period, population, economy, and land urbanization level are increasing, and the comprehensive urbanization level is increased by 51.63%. The total CCD between NPP, food production, and water yield and comprehensive urbanization is basic or moderate coordination, whereas that between soil conservation and comprehensive urbanization is moderate maladjustment. In the research period, the coupling coordination between NPP and food production and comprehensive urbanization is increasing; that between water yield and comprehensive urbanization is fluctuated; and that between soil conservation and comprehensive urbanization is decreasing. The result of the research into the relative development degree in 2018 showed that food production, water yield, and soil conservation lag behind the urbanization level in most regions and counties along the Yellow River of Henan Province. On the basis of the lead–lag relationship between different ESs and urbanization level, the AYRHP are divided into ecological reconstruction area, ecological and agricultural improvement area, and ecological conservation area. CCD and relative development degree models can be used to evaluate the coordination relationship between ESs and urbanization, which provides scientific support for regional ES management. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Urban Ecosystem Services)

► Show Figures

Graphical abstract

19 pages, 9695 KiB

Open AccessArticle

A Context Feature Enhancement Network for Building Extraction from High-Resolution Remote Sensing Imagery

by Jinzhi Chen, Dejun Zhang, Yiqi Wu, Yilin Chen and Xiaohu Yan

Remote Sens. 2022, 14(9), 2276; https://doi.org/10.3390/rs14092276 - 9 May 2022

Cited by 28 | Viewed by 5193

Abstract

The complexity and diversity of buildings make it challenging to extract low-level and high-level features with strong feature representation by using deep neural networks in building extraction tasks. Meanwhile, deep neural network-based methods have many network parameters, which take up a lot of [...] Read more.

The complexity and diversity of buildings make it challenging to extract low-level and high-level features with strong feature representation by using deep neural networks in building extraction tasks. Meanwhile, deep neural network-based methods have many network parameters, which take up a lot of memory and time in training and testing. We propose a novel fully convolutional neural network called the Context Feature Enhancement Network (CFENet) to address these issues. CFENet comprises three modules: the spatial fusion module, the focus enhancement module, and the feature decoder module. First, the spatial fusion module aggregates the spatial information of low-level features to obtain buildings’ outline and edge information. Secondly, the focus enhancement module fully aggregates the semantic information of high-level features to filter the information of building-related attribute categories. Finally, the feature decoder module decodes the output of the above two modules to segment the buildings more accurately. In a series of experiments on the WHU Building Dataset and the Massachusetts Building Dataset, our CFENet balances efficiency and accuracy compared to the other four methods we compared, and achieves optimality on all five evaluation metrics: PA, PC, F1, IoU, and FWIoU. This indicates that CFENet can effectively enhance and fuse buildings’ low-level and high-level features, improving building extraction accuracy. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection)

► Show Figures

Figure 1

16 pages, 7429 KiB

Open AccessArticle

Research on Generalized RQD of Rock Mass Based on 3D Slope Model Established by Digital Close-Range Photogrammetry

by Qing Ding, Fengyan Wang, Jianping Chen, Mingchang Wang and Xuqing Zhang

Remote Sens. 2022, 14(9), 2275; https://doi.org/10.3390/rs14092275 - 9 May 2022

Cited by 6 | Viewed by 2240

Abstract

The traditional method of obtaining rock quality designation (RQD) cannot fully reflect the anisotropy of the rock mass and thus cannot accurately reflect its quality. In the method of calculating RQD based on three-dimensional network simulation of discontinuities, due to the limited number [...] Read more.

The traditional method of obtaining rock quality designation (RQD) cannot fully reflect the anisotropy of the rock mass and thus cannot accurately reflect its quality. In the method of calculating RQD based on three-dimensional network simulation of discontinuities, due to the limited number of samples and low accuracy of discontinuity data obtained by manual contact measurement, a certain deviation in the network is generated based on the data, which has an impact on the calculation result. Taking a typical slope in Dongsheng quarry in Changchun City as an example, in this study, we obtained the discontinuity data of the slope based on digital close-range photogrammetry, which greatly enlarged the sample size of discontinuity data and improved the data quality. Based on the heterogeneity of the rock mass, the optimum threshold of discontinuity spacing was determined when surveying lines were laid parallel to different coordinate axes to calculate the generalized RQD, and the influence of measuring blank areas on the slope caused by vegetation coverage or gravel accumulation was eliminated. The real generalized RQD of the rock mass after eliminating the influence of blank areas was obtained. Experiments showed that, after eliminating the influence of blank areas, the generalized RQD of the slope rock mass more truly represented the complete quality of rock mass and offers a new idea for the quality evaluation of engineering rock mass. Full article

(This article belongs to the Special Issue Comprehensive Risk Assessment Methods and Hazards Early Warning System of Geological Hazards in the Mountain Area)

► Show Figures

Figure 1

13 pages, 2998 KiB

Open AccessCommunication

Adaptive Subspace Signal Detection in Structured Interference Plus Compound Gaussian Sea Clutter

by Zeyu Wang, Jun Liu, Yachao Li, Hongmeng Chen and Mugen Peng

Remote Sens. 2022, 14(9), 2274; https://doi.org/10.3390/rs14092274 - 8 May 2022

Cited by 6 | Viewed by 2010

Abstract

This paper discusses the problem of detecting subspace signals in structured interference plus compound Gaussian sea clutter with persymmetric structure. The sea clutter is represented by a compound Gaussian process wherein the texture obeys the inverse Gaussian distribution. The structured interference lies in [...] Read more.

This paper discusses the problem of detecting subspace signals in structured interference plus compound Gaussian sea clutter with persymmetric structure. The sea clutter is represented by a compound Gaussian process wherein the texture obeys the inverse Gaussian distribution. The structured interference lies in a known subspace, which is independent with the target signal subspace. By resorting to the two-step generalized likelihood ratio test, two-step Rao, and two-step Wald design criteria, three adaptive subspace signal detectors are proposed. Moreover, the constant false-alarm rate property of the proposed detectors is proved. The experimental results based on IPIX real sea clutter data and simulated data illustrate that the proposed detectors outperform their counterparts. Full article

(This article belongs to the Special Issue Target Detection and Information Extraction in Radar Images)

► Show Figures

Graphical abstract

17 pages, 31734 KiB

Open AccessTechnical Note

Adaptive Kalman Filter for Real-Time Precise Orbit Determination of Low Earth Orbit Satellites Based on Pseudorange and Epoch-Differenced Carrier-Phase Measurements

by Min Li, Tianhe Xu, Yali Shi, Kai Wei, Xianming Fei and Dixing Wang

Remote Sens. 2022, 14(9), 2273; https://doi.org/10.3390/rs14092273 - 8 May 2022

Cited by 6 | Viewed by 3590

Abstract

Real-time precise orbit determination (POD) of low earth orbiters (LEOs) is crucial for orbit maintenance as well as autonomous operation for space missions. The Global Positioning System (GPS) has become the dominant technique for real-time precise orbit determination (POD) of LEOs. However, the [...] Read more.

Real-time precise orbit determination (POD) of low earth orbiters (LEOs) is crucial for orbit maintenance as well as autonomous operation for space missions. The Global Positioning System (GPS) has become the dominant technique for real-time precise orbit determination (POD) of LEOs. However, the observation conditions of near-earth space are more critical than those on the ground. Real-time POD accuracy can be seriously affected when the observation environment suffers from strong space events, i.e., a heavy solar storm. In this study, we proposed a reliable adaptive Kalman filter based on pseudorange and epoch-differenced carrier-phase measurements. This approach uses the epoch-differenced carrier phase to eliminate the ambiguities and thus reduces the significant number of unknown parameters. Real calculations demonstrate that four to five observed GPS satellites is sufficient to solve reliable position parameters. Furthermore, with accurate pseudorange and epoch-differenced carrier-phase-based reference orbits, orbital dynamic disturbance can be detected precisely and reliably with an adaptive Kalman filter. Analyses of Swarm-A POD show that sub-meter level real-time orbit solutions can be obtained when the observation conditions are good. For poor observation conditions such as the GRACE-A satellite on 8 September 2017, when fewer than five GPS satellites were observed for 14% of the observation time, 1–2 m orbital accuracy can still be achieved with the proposed approach. Full article

(This article belongs to the Special Issue Precision Orbit Determination of Satellites)

► Show Figures

Figure 1

17 pages, 4326 KiB

Open AccessArticle

Local Persistent Ionospheric Positive Responses to the Geomagnetic Storm in August 2018 Using BDS-GEO Satellites over Low-Latitude Regions in Eastern Hemisphere

by Jun Tang, Xin Gao, Dengpan Yang, Zhengyu Zhong, Xingliang Huo and Xuequn Wu

Remote Sens. 2022, 14(9), 2272; https://doi.org/10.3390/rs14092272 - 8 May 2022

Cited by 9 | Viewed by 2347

Abstract

We present the ionospheric disturbance responses over low-latitude regions by using total electron content from Geostationary Earth Orbit (GEO) satellites of the BeiDou Navigation Satellite System (BDS), ionosonde data and Swarm satellite data, during the geomagnetic storm in August 2018. The results show [...] Read more.

We present the ionospheric disturbance responses over low-latitude regions by using total electron content from Geostationary Earth Orbit (GEO) satellites of the BeiDou Navigation Satellite System (BDS), ionosonde data and Swarm satellite data, during the geomagnetic storm in August 2018. The results show that a prominent total electron content (TEC) enhancement over low-latitude regions is observed during the main phase of the storm. There is a persistent TEC increase lasting for about 1–2 days and a moderately positive disturbance response during the recovery phase on 27–28 August, which distinguishes from the general performance of ionospheric TEC in the previous storms. We also find that this phenomenon is a unique local-area disturbance of the ionosphere during the recovery phase of the storm. The enhanced

f_{o} F_{2}

and

h_{m} F_{2}

of the ionospheric F2 layer is observed by SANYA and LEARMONTH ionosonde stations during the recovery phase. The electron density from Swarm satellites shows a strong equatorial ionization anomaly (EIA) crest over the low-latitude area during the main phase of storm, which is simultaneous with the uplift of the ionospheric F2 layer from the SANYA ionosonde. Meanwhile, the thermosphere O/N₂ ratio shows a local increase on 27–28 August over low-latitude regions. From the above results, this study suggests that the uplift of F layer height and the enhanced O/N₂ ratio are possibly main factors causing the local-area positive disturbance responses during the recovery phase of the storm in August 2018. Full article

(This article belongs to the Special Issue Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data)

► Show Figures

Figure 1

20 pages, 3364 KiB

Open AccessArticle

Combining Different Transformations of Ground Hyperspectral Data with Unmanned Aerial Vehicle (UAV) Images for Anthocyanin Estimation in Tree Peony Leaves

by Lili Luo, Qinrui Chang, Yifan Gao, Danyao Jiang and Fenling Li

Remote Sens. 2022, 14(9), 2271; https://doi.org/10.3390/rs14092271 - 8 May 2022

Cited by 18 | Viewed by 3364

Abstract

To explore rapid anthocyanin (Anth) detection technology based on remote sensing (RS) in tree peony leaves, we considered 30 species of tree peonies located in Shaanxi Province, China. We used an SVC HR~1024i portable ground object spectrometer and mini-unmanned aerial vehicle (UAV)-borne RS [...] Read more.

To explore rapid anthocyanin (Anth) detection technology based on remote sensing (RS) in tree peony leaves, we considered 30 species of tree peonies located in Shaanxi Province, China. We used an SVC HR~1024i portable ground object spectrometer and mini-unmanned aerial vehicle (UAV)-borne RS systems to obtain hyperspectral (HS) reflectance and images of canopy leaves. First, we performed principal component analysis (PCA), first-order differential (FD), and continuum removal (CR) transformations on the original ground-based spectra; commonly used spectral parameters were implemented to estimate Anth content using multiple stepwise regression (MSR), partial least squares (PLS), back-propagation neural network (BPNN), and random forest (RF) models. The spectral transformation highlighted the characteristics of spectral curves and improved the relationship between spectral reflectance and Anth, and the RF model based on the FD spectrum portrayed the best estimation accuracy (R²_c = 0.91; R²_v = 0.51). Then, the RGB (red-green-blue) gray vegetation index (VI) and the texture parameters were constructed using UAV images, and an Anth estimation model was constructed using UAV parameters. Finally, the UAV image was fused with the ground spectral data, and a multisource RS model of Anth estimation was constructed, based on PCA + UAV, FD + UAV, and CR + UAV, using MSR, PLS, BPNN, and RF methods. The RF model based on FD+UAV portrayed the best modeling and verification effect (R²_c = 0.93; R²_v = 0.76); compared with the FD-RF model, R²_c increased only slightly, but R²_v increased greatly from 0.51 to 0.76, indicating improved modeling and testing accuracy. The optimal spectral transformation for the Anth estimation of tree peony leaves was obtained, and a high-precision Anth multisource RS model was constructed. Our results can be used for the selection of ground-based HS transformation in future plant Anth estimation, and as a theoretical basis for plant growth monitoring based on ground and UAV multisource RS. Full article

(This article belongs to the Special Issue Structure and Trend Monitoring of Forest Vegetation and Savanna Based on UAS Platform)

► Show Figures

Graphical abstract

23 pages, 7852 KiB

Open AccessArticle

An Integrated Model of Summer and Winter for Chlorophyll-a Retrieval in the Pearl River Estuary Based on Hyperspectral Data

by Haitao Li, Xuetong Xie, Xiankun Yang, Bowen Cao and Xuening Xia

Remote Sens. 2022, 14(9), 2270; https://doi.org/10.3390/rs14092270 - 8 May 2022

Cited by 7 | Viewed by 2668

Abstract

Chlorophyll-a (Chla) is an important parameter for water quality. For remote sensing-based methods for the measurement of Chla, in-situ hyperspectral data is crucial for building retrieval models. In the Pearl River Estuary, we used 61 groups of in-situ hyperspectral data and [...] Read more.

Chlorophyll-a (Chla) is an important parameter for water quality. For remote sensing-based methods for the measurement of Chla, in-situ hyperspectral data is crucial for building retrieval models. In the Pearl River Estuary, we used 61 groups of in-situ hyperspectral data and corresponding Chla concentrations collected in July and December 2020 to build a Chla retrieval model that takes the two different seasons and the turbidity of water into consideration. The following results were obtained. (1) Based on the pre-processing techniques for hyperspectral data, it was shown that the first-derivative of 680 nm is the optimal band for the estimation of Chla in the Pearl River Estuary, with R² > 0.8 and MAPE of 26.03%. (2) To overcome the spectral resolution problem in satellite image retrieval, based on the simulated reflectance from the Sentinel-2 satellite and the shape of the discrete spectral curve, we constructed a multispectral model using the slope difference index method, which reached a R² of 0.78 and MAPE of 35.21% and can integrate the summer and winter data. (3) The slope difference method applied to the Sentinel-2 image shows better performance than the red-NIR ratio method. Therefore, the method proposed in this paper is practicable for Chla monitoring of coastal waters based on both in-situ data and images. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Technology in Water Quality Evaluation)

► Show Figures

Graphical abstract

16 pages, 4688 KiB

Open AccessArticle

A Neural Network Method for Retrieving Sea Surface Wind Speed for C-Band SAR

by Peng Yu, Wenxiang Xu, Xiaojing Zhong, Johnny A. Johannessen, Xiao-Hai Yan, Xupu Geng, Yuanrong He and Wenfang Lu

Remote Sens. 2022, 14(9), 2269; https://doi.org/10.3390/rs14092269 - 8 May 2022

Cited by 10 | Viewed by 2968

Abstract

Based on the Ocean Projection and Extension neural Network (OPEN) method, a novel approach is proposed to retrieve sea surface wind speed for C-band synthetic aperture radar (SAR). In order to prove the methodology with a robust dataset, five-year normalized radar cross section [...] Read more.

Based on the Ocean Projection and Extension neural Network (OPEN) method, a novel approach is proposed to retrieve sea surface wind speed for C-band synthetic aperture radar (SAR). In order to prove the methodology with a robust dataset, five-year normalized radar cross section (NRCS) measurements from the advanced scatterometer (ASCAT), a well-known side-looking radar sensor, are used to train the model. In situ wind data from direct buoy observations, instead of reanalysis wind data or model results, are used as the ground truth in the OPEN model. The model is applied to retrieve sea surface winds from two independent data sets, ASCAT and Sentinel-1 SAR data, and has been well-validated using buoy measurements from the National Oceanic and Atmospheric Administration (NOAA) and China Meteorological Administration (CMA), and the ASCAT coastal wind product. The comparison between the OPEN model and four C-band model (CMOD) versions (CMOD4, CMOD-IFR2, CMOD5.N, and CMOD7) further indicates the good performance of the proposed model for C-band SAR sensors. It is anticipated that the use of high-resolution SAR data together with the new wind speed retrieval method can provide continuous and accurate ocean wind products in the future. Full article

(This article belongs to the Special Issue Synthetic Aperture Radar Observations of Marine Coastal Environments-II)

► Show Figures

Figure 1

22 pages, 4953 KiB

Open AccessArticle

Urbanization Level in Chinese Counties: Imbalance Pattern and Driving Force

by Baifa Zhang, Jing Zhang and Changhong Miao

Remote Sens. 2022, 14(9), 2268; https://doi.org/10.3390/rs14092268 - 8 May 2022

Cited by 33 | Viewed by 4083

Abstract

Urbanization level is a key indicator for socioeconomic development and policy making, but the measurement data and methods need to be discussed further due to the limitation of a single index and the availability and accuracy of statistical data. China is urbanizing rapidly, [...] Read more.

Urbanization level is a key indicator for socioeconomic development and policy making, but the measurement data and methods need to be discussed further due to the limitation of a single index and the availability and accuracy of statistical data. China is urbanizing rapidly, but the urbanization level at the county scale remains a mystery due to its complexity and lack of unified and effective measurement indicators. In this paper, we proposed a new urbanization index to measure the Chinese urbanization level at the county scale by integrating population, land, and economic factors; by fusing remote sensing data and traditional demographic data, we investigated the multi-dimensional unbalanced development patterns and the driving mechanism from 1995 to 2015. Results indicate that: The average comprehensive urbanization level at the Chinese county scale has increased from 31.06% in 1995 to 45.23% in 2015, and the urbanization level in the permanent population may overestimate China’s urbanization process. There were significant but different spatial and temporal dynamic patterns in population, land, and economic levels as well as at a comprehensive urbanization level. The comprehensive urbanization level shows the pattern of being high in the south-east and low in the north-west, divided by “Hu line”. The urbanization of registered populations presents high in the northern border and the eastern coastal areas, which is further strengthened over time. Economic urbanization based on lighting data presents high in the east and low in the west. Land urbanization based on remote sensing data shows high in the south and low in the north. The registered population urbanization level is lower than economic and land urbanization. County urbanization was driven by large population size, reasonable industrial structure, and strong government capacity; 38% and 59% of urbanization levels can be regarded as the key nodes of the urbanization process. When the urbanization rate is lower than 38%, the secondary industry plays a strong role in powering urbanization; when the urbanization rate is higher than 38% but less than 59%, the promotion effect of the tertiary industry is more obvious, and the secondary industry is gradually weakened. When the urbanization rate exceeds 59%, the tertiary industry becomes the major driver. Full article

(This article belongs to the Special Issue Remote Sensing of Night-Time Light)

► Show Figures

Figure 1

19 pages, 32130 KiB

Open AccessArticle

Optical Turbulence Profile in Marine Environment with Artificial Neural Network Model

by Cuicui Bi, Chun Qing, Pengfei Wu, Xiaomei Jin, Qing Liu, Xianmei Qian, Wenyue Zhu and Ningquan Weng

Remote Sens. 2022, 14(9), 2267; https://doi.org/10.3390/rs14092267 - 8 May 2022

Cited by 12 | Viewed by 2269

Abstract

Optical turbulence strongly affects different types of optoelectronic and adaptive optics systems. Systematic direct measurements of optical turbulence profiles [

C_{n}^{2} (h)

] are lacking for many climates and seasons, particularly in marine environments, because it is impractical and [...] Read more.

Optical turbulence strongly affects different types of optoelectronic and adaptive optics systems. Systematic direct measurements of optical turbulence profiles [

C_{n}^{2} (h)

] are lacking for many climates and seasons, particularly in marine environments, because it is impractical and expensive to deploy instrumentation. Here, a backpropagation neural network optimized using a genetic algorithm (GA-BP) is developed to estimate atmospheric turbulence profiles in marine environments which is validated against corresponding [

C_{n}^{2} (h)

] profile datasets from a field campaign of balloon-borne microthermal measurements at the Haikou marine environment site. Overall, the trend and magnitude of the GA-BP model and measurements agree. The [

C_{n}^{2} (h)

] profiles from the GA-BP model are generally superior to those obtained by BP and the physically-based (HMNSP99) models. Several statistical operators were used to quantify the GA-BP model performance on reconstructing the optical turbulence profiles in marine environments. The characterization of vertical distributions of optical turbulence profiles and the main integral parameters derived from [

C_{n}^{2} (h)

] profiles are presented. The median Fried parameter, isoplanatic angle, and coherence time are 9.94 cm,

{0.69}^{″}

, and 2.85 ms, respectively, providing independent optical turbulence parameters for adaptive optics systems. The proposed approach exhibits potential for implementation in ground-based optical applications in marine environments. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

► Show Figures

Figure 1

20 pages, 12409 KiB

Open AccessArticle

Hyperspectral Image Classification via Deep Structure Dictionary Learning

by Wenzheng Wang, Yuqi Han, Chenwei Deng and Zhen Li

Remote Sens. 2022, 14(9), 2266; https://doi.org/10.3390/rs14092266 - 8 May 2022

Cited by 17 | Viewed by 2731

Abstract

The construction of diverse dictionaries for sparse representation of hyperspectral image (HSI) classification has been a hot topic over the past few years. However, compared with convolutional neural network (CNN) models, dictionary-based models cannot extract deeper spectral information, which will reduce their performance [...] Read more.

The construction of diverse dictionaries for sparse representation of hyperspectral image (HSI) classification has been a hot topic over the past few years. However, compared with convolutional neural network (CNN) models, dictionary-based models cannot extract deeper spectral information, which will reduce their performance for HSI classification. Moreover, dictionary-based methods have low discriminative capability, which leads to less accurate classification. To solve the above problems, we propose a deep learning-based structure dictionary for HSI classification in this paper. The core ideas are threefold, as follows: (1) To extract the abundant spectral information, we incorporate deep residual neural networks in dictionary learning and represent input signals in the deep feature domain. (2) To enhance the discriminative ability of the proposed model, we optimize the structure of the dictionary and design sharing constraint in terms of sub-dictionaries. Thus, the general and specific feature of HSI samples can be learned separately. (3) To further enhance classification performance, we design two kinds of loss functions, including coding loss and discriminating loss. The coding loss is used to realize the group sparsity of code coefficients, in which within-class spectral samples can be represented intensively and effectively. The Fisher discriminating loss is used to enforce the sparse representation coefficients with large between-class scatter. Extensive tests performed on hyperspectral dataset with bright prospects prove the developed method to be effective and outperform other existing methods. Full article

(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing)

► Show Figures

Figure 1

27 pages, 4381 KiB

Open AccessArticle

One-Shot Dense Network with Polarized Attention for Hyperspectral Image Classification

by Haizhu Pan, Moqi Liu, Haimiao Ge and Liguo Wang

Remote Sens. 2022, 14(9), 2265; https://doi.org/10.3390/rs14092265 - 8 May 2022

Cited by 14 | Viewed by 2324

Abstract

In recent years, hyperspectral image (HSI) classification has become a hot research direction in remote sensing image processing. Benefiting from the development of deep learning, convolutional neural networks (CNNs) have shown extraordinary achievements in HSI classification. Numerous methods combining CNNs and attention mechanisms [...] Read more.

In recent years, hyperspectral image (HSI) classification has become a hot research direction in remote sensing image processing. Benefiting from the development of deep learning, convolutional neural networks (CNNs) have shown extraordinary achievements in HSI classification. Numerous methods combining CNNs and attention mechanisms (AMs) have been proposed for HSI classification. However, to fully mine the features of HSI, some of the previous methods apply dense connections to enhance the feature transfer between each convolution layer. Although dense connections allow these methods to fully extract features in a few training samples, it decreases the model efficiency and increases the computational cost. Furthermore, to balance model performance against complexity, the AMs in these methods compress a large number of channels or spatial resolutions during the training process, which results in a large amount of useful information being discarded. To tackle these issues, in this article, a novel one-shot dense network with polarized attention, namely, OSDN, was proposed for HSI classification. More precisely, since HSI contains rich spectral and spatial information, the OSDN has two independent branches to extract spectral and spatial features, respectively. Similarly, the polarized AMs contain two components: channel-only AMs and spatial-only AMs. Both polarized AMs can use a specially designed filtering method to reduce the complexity of the model while maintaining high internal resolution in both the channel and spatial dimensions. To verify the effectiveness and lightness of OSDN, extensive experiments were carried out on five benchmark HSI datasets, namely, Pavia University (PU), Kennedy Space Center (KSC), Botswana (BS), Houston 2013 (HS), and Salinas Valley (SV). Experimental results consistently showed that the OSDN can greatly reduce computational cost and parameters while maintaining high accuracy in a few training samples. Full article

(This article belongs to the Special Issue Recent Advances in Processing Mixed Pixels for Hyperspectral Image)

► Show Figures

Figure 1

17 pages, 8064 KiB

Open AccessArticle

Experimental Study on the Exploration of Camera Scanning Reflective Fourier Ptychography Technology for Far-Field Imaging

by Mingyang Yang, Xuewu Fan, Yuming Wang and Hui Zhao

Remote Sens. 2022, 14(9), 2264; https://doi.org/10.3390/rs14092264 - 8 May 2022

Cited by 3 | Viewed by 2698

Abstract

Fourier ptychography imaging is a powerful phase retrieval method that can be used to realize super-resolution. In this study, we establish a mathematical model of long-distance camera scanning based on reflective Fourier ptychography imaging. In order to guarantee the effective recovery of a [...] Read more.

Fourier ptychography imaging is a powerful phase retrieval method that can be used to realize super-resolution. In this study, we establish a mathematical model of long-distance camera scanning based on reflective Fourier ptychography imaging. In order to guarantee the effective recovery of a high-resolution image in the experiment, we analyze the influence of laser coherence in different modes and the surface properties of diverse materials for diffused targets. For the analysis, we choose a single-mode fiber laser as the illumination source and metal materials with high diffused reflectivity as the experimental targets to ensure the validity of the experimental results. Based on the above, we emulate camera scanning with a single camera attached to an X-Y translation stage, and an experimental system with a working distance of 3310 mm is used as an example to image a fifty-cent coin. We also perform speckle analysis for rough targets and calculate the average speckle size using a normalized autocorrelation function in different positions. The method of calculating the average speckle size for everyday objects provides the premise for subsequent research on image quality evaluation; meanwhile, the coherence of the light field and the targets with high reflectivity under this experiment provide an application direction for the further development of the technique, such as computer vision, surveillance and remote sensing. Full article

► Show Figures

Graphical abstract

23 pages, 7370 KiB

Open AccessArticle

Remote Sensing Mapping of Build-Up Land with Noisy Label via Fault-Tolerant Learning

by Gang Xu, Yongjun Fang, Min Deng, Geng Sun and Jie Chen

Remote Sens. 2022, 14(9), 2263; https://doi.org/10.3390/rs14092263 - 8 May 2022

Cited by 2 | Viewed by 2728

Abstract

China’s urbanization has dramatically accelerated in recent decades. Land for urban build-up has changed not only in large cities but also in small counties. Land cover mapping is one of the fundamental tasks in the field of remote sensing and has received great [...] Read more.

China’s urbanization has dramatically accelerated in recent decades. Land for urban build-up has changed not only in large cities but also in small counties. Land cover mapping is one of the fundamental tasks in the field of remote sensing and has received great attention. However, most current mapping requires a significant manual effort for labeling or classification. It is of great practical value to use the existing low-resolution label data for the classification of higher resolution images. In this regard, this work proposes a method based on noise-label learning for fine-grained mapping of urban build-up land in a county in central China. Specifically, this work produces a build-up land map with a resolution of 10 m based on a land cover map with a resolution of 30 m. Experimental results show that the accuracy of the results is improved by 5.5% compared with that of the baseline method. This notion indicates that the time required to produce a fine land cover map can be significantly reduced using existing coarse-grained data. Full article

(This article belongs to the Special Issue Remote Sensing Interpretation Systematic Engineering for Natural Resources Monitoring and Management)

► Show Figures

Figure 1

25 pages, 6287 KiB

Open AccessArticle

A Machine Learning Approach to Waterbody Segmentation in Thermal Infrared Imagery in Support of Tactical Wildfire Mapping

by Jacqueline A. Oliver, Frédérique C. Pivot, Qing Tan, Alan S. Cantin, Martin J. Wooster and Joshua M. Johnston

Remote Sens. 2022, 14(9), 2262; https://doi.org/10.3390/rs14092262 - 8 May 2022

Cited by 4 | Viewed by 2373

Abstract

Wildfire research is working toward near real-time tactical wildfire mapping through the application of computer vision techniques to airborne thermal infrared (IR) imagery. One issue hindering automation is the potential for waterbodies to be marked as areas of combustion due to their relative [...] Read more.

Wildfire research is working toward near real-time tactical wildfire mapping through the application of computer vision techniques to airborne thermal infrared (IR) imagery. One issue hindering automation is the potential for waterbodies to be marked as areas of combustion due to their relative warmth in nighttime thermal imagery. Segmentation and masking of waterbodies could help resolve this issue, but the reliance on data captured exclusively in the thermal IR and the presence of real areas of combustion in some of the images introduces unique challenges. This study explores the use of the random forest (RF) classifier for the segmentation of waterbodies in thermal IR images containing a heterogenous wildfire. Features for classification are generated through the application of contextual and textural filters, as well as normalization techniques. The classifier’s outputs are compared against static GIS-based data on waterbody extent as well as the outputs of two unsupervised segmentation techniques, based on entropy and variance, respectively. Our results show that the RF classifier achieves very high balanced accuracy (>98.6%) for thermal imagery with and without wildfire pixels, with an overall F1 score of 0.98. The RF method surpassed the accuracy of all others tested, even with heterogenous training sets as small as 20 images. In addition to assisting automation of wildfire mapping, the efficiency and accuracy of this approach to segmentation can facilitate the creation of larger training data sets, which are necessary for invoking more complex deep learning approaches. Full article

(This article belongs to the Section Remote Sensing Image Processing)

► Show Figures

Figure 1

21 pages, 14617 KiB

Open AccessArticle

Detection of Flood Extent Using Sentinel-1A/B Synthetic Aperture Radar: An Application for Hurricane Harvey, Houston, TX

by Kristy F. Tiampo, Lingcao Huang, Conor Simmons, Clay Woods and Margaret T. Glasscoe

Remote Sens. 2022, 14(9), 2261; https://doi.org/10.3390/rs14092261 - 8 May 2022

Cited by 11 | Viewed by 3309

Abstract

The increasing number of flood events combined with coastal urbanization has contributed to significant economic losses and damage to buildings and infrastructure. Development of higher resolution SAR flood mapping that accurately identifies flood features at all scales can be incorporated into operational flood [...] Read more.

The increasing number of flood events combined with coastal urbanization has contributed to significant economic losses and damage to buildings and infrastructure. Development of higher resolution SAR flood mapping that accurately identifies flood features at all scales can be incorporated into operational flood forecasting tools, improving response and resilience to large flood events. Here, we present a comparison of several methods for characterizing flood inundation using a combination of synthetic aperture radar (SAR) remote sensing data and machine learning methods. We implement two applications with SAR GRD data, an amplitude thresholding technique applied, for the first time, to Sentinel-1A/B SAR data, and a machine learning technique, DeepLabv3+. We also apply DeepLabv3+ to a false color RGB characterization of dual polarization SAR data. Analyses at 10 m pixel spacing are performed for the major flood event associated with Hurricane Harvey and associated inundation in Houston, TX in August of 2017. We compare these results with high-resolution aerial optical images over this time period, acquired by the NOAA Remote Sensing Division. We compare the results with NDWI produced from Sentinel-2 images, also at 10 m pixel spacing, and statistical testing suggests that the amplitude thresholding technique is the most effective, although the machine learning analysis is successful at reproducing the inundation shape and extent. These results demonstrate the effectiveness of flood inundation mapping at unprecedented resolutions and its potential for use in operational emergency hazard response to large flood events. Full article

(This article belongs to the Special Issue Remote Sensing for Near-Real-Time Disaster Monitoring)

► Show Figures

Figure 1

21 pages, 5627 KiB

Open AccessArticle

Comparative Study of the 60 GHz and 118 GHz Oxygen Absorption Bands for Sounding Sea Surface Barometric Pressure

by Qiurui He, Jiaoyang Li, Zhenzhan Wang and Lanjie Zhang

Remote Sens. 2022, 14(9), 2260; https://doi.org/10.3390/rs14092260 - 8 May 2022

Cited by 1 | Viewed by 2508

Abstract

The 60 GHz and 118 GHz oxygen absorption bands are prominent in the passive microwave remote sensing of atmospheric temperature, and also can be used for sounding sea surface barometric pressure (SSP). Microwave Temperature Sounder II (MWTS-II) has 13 channels in the 60 [...] Read more.

The 60 GHz and 118 GHz oxygen absorption bands are prominent in the passive microwave remote sensing of atmospheric temperature, and also can be used for sounding sea surface barometric pressure (SSP). Microwave Temperature Sounder II (MWTS-II) has 13 channels in the 60 GHz band, and Microwave Humidity and Temperature Sounder (MWHTS) has 8 channels in the 118 GHz band. They are both carried on Fengyun-3C Satellite (FY-3C) and Fengyun-3D Satellite (FY-3D), which provide measurements for comparing the retrieval accuracies of SSP using 60 GHz and 118 GHz bands. In this study, based on the weighting functions for MWHTS and MWTS-II, the 60 GHz and 118 GHz channel combinations representing 60 GHz and 118 GHz are established, respectively, and the retrieval accuracies of SSP from these two channel combinations are compared in different weather conditions. The experimental results show that the retrieval accuracy of SSP at 60 GHz is higher than that of 118 GHz in clear, cloudy, and rainy sky conditions. In addition, the retrieval experiments of SSP from MWTS-II and MWHTS are also carried out, and the experimental results show that the retrieval accuracy of SSP from MWTS-II is higher. The comparative study of the 60 GHz and 118 GHz for sounding SSP can provide support for the theoretical study of microwave remote sensing of SSP with practical measurements, and further contribute to understand the performance of 60 GHz and 118 GHz in atmospheric sounding. Full article

► Show Figures

Figure 1

20 pages, 8426 KiB

Open AccessArticle

Effect of the Shadow Pixels on Evapotranspiration Inversion of Vineyard: A High-Resolution UAV-Based and Ground-Based Remote Sensing Measurements

by Saihong Lu, Junjie Xuan, Tong Zhang, Xueer Bai, Fei Tian and Samuel Ortega-Farias

Remote Sens. 2022, 14(9), 2259; https://doi.org/10.3390/rs14092259 - 7 May 2022

Cited by 13 | Viewed by 2522

Abstract

Due to the proliferation of precision agriculture, the obstacle of estimating evapotranspiration (ET) and its components from shadow pixels acquired from remote sensing technology should not be neglected. To accurately detect shaded soil and leaf pixels and quantify the implications of shadow pixels [...] Read more.

Due to the proliferation of precision agriculture, the obstacle of estimating evapotranspiration (ET) and its components from shadow pixels acquired from remote sensing technology should not be neglected. To accurately detect shaded soil and leaf pixels and quantify the implications of shadow pixels on ET inversion, a two-year field-scale observation was carried out in the growing season for a pinot noir vineyard. Based on high-resolution remote sensing sensors covering visible light, thermal infrared, and multispectral light, the supervised classification was applied to detect shadow pixels. Then, we innovatively combined the normalized difference vegetation index with the three-temperature model to quantify the proportion of plant transpiration (T) and soil evaporation (E) in the vineyard ecosystem. Finally, evaluated with the eddy covariance system, we clarified the implications of the shadow pixels on the ET estimation and the spatiotemporal patterns of ET in a vineyard system by considering where shadow pixels were presented. Results indicated that the shadow detection process significantly improved reliable assessment of ET and its components. (1) The shaded soil pixels misled the land cover classification, with the mean canopy cover ignoring shadows 1.68–1.70 times more often than that of shaded area removal; the estimation accuracy of ET can be improved by 4.59–6.82% after considering the effect of shaded soil pixels; and the accuracy can be improved by 0.28–0.89% after multispectral correction. (2) There was a 2 °C canopy temperature discrepancy between sunlit leaves and shaded leaves, meaning that the estimation accuracy of T can be improved by 1.38–7.16% after considering the effect of shaded canopy pixels. (3) Simultaneously, the characteristics showed that there was heterogeneity of ET in the vineyard spatially and that E and T fluxes accounted for 238.05 and 208.79 W·m⁻², respectively; the diurnal variation represented a single-peak curve, with a mean of 0.26 mm/h. Our findings provide a better understanding of the influences of shadow pixels on ET estimation using remote sensing techniques. Full article

(This article belongs to the Special Issue Remote Sensing for Eco-Hydro-Environment)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Remote Sens., Volume 14, Issue 9 (May-1 2022) – 335 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI