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Classification and Feature Extraction from Remote Sensing Imagery and Point Cloud Data

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 15060

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Special Issue Editors

Prof. Dr. Guoqing Zhou

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Guest Editor

Guangxi Key Laboratory for Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, China
Interests: remote sensing; image process; point cloud data process; photogrammetry

Dr. Yuefeng Wang

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Guest Editor

Guangxi Key Laboratory for Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, China
Interests: image process; intelligent interpretation; sematic segmentation

Prof. Dr. Oktay Baysal

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Guest Editor

Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, VA, USA
Interests: Intelligent interpretation; sematic segmentation; 3D reconstruction

Special Issue Information

Dear Colleagues,

Classification is an essential part of information deconstruction in remote sensing, a core process of feature extraction. Technical feature designs need to be robust and have fidelity; classifiers are devoted to fitting a sample as close as possible through its features. In recent years, further classification optimization is driven by the data needed for artificial intelligence, such as deep learning. The most recent emergence of platform type, spectrum quantity and resolution, advanced sensors, etc., have been applied to yield a vast amount of remote sensing images and point cloud data. New data usher in new challenges of classification and feature extraction. Mining new physical features and integrating multi-source data features are needed to improve classification accuracy. Moreover, the development and fusion of classifiers can further promote more refined remote sensing applications, such as the 3D reconstruction of large scenes, long-time series change detection, etc.

This Special Issue aims to provide more advanced feature extraction methods and classification techniques for multi-source and multi-modal remote sensing data. Furthermore, with these new data and technologies, the application of remote sensing data can also be improved and expanded. Authors are invited to contribute to the most research results in cutting-edge technology, novel applications, and evaluation methods on remote sensing classification and feature extraction, including but not limited to the following topics:

Breakthrough idea for image classification and feature extraction;
Cutting-edge technologies for image classification and feature extraction;
Artificial intelligence theory, method and algorithm for image classification and feature extraction;
Classified image and point cloud data for various applications;
Classification quality evaluation.

Prof. Dr. Guoqing Zhou
Dr. Yuefeng Wang
Prof. Dr. Oktay Baysal
Guest Editors

Manuscript Submission Information

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Keywords

classification
feature extraction
remote sensing image
point cloud data
data and/or feature fusion
artificial intelligent method
applications

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Published Papers (6 papers)

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Research

20 pages, 4242 KiB

Open AccessArticle

Dynamic Simulation and Prediction of Carbon Storage Based on Land Use/Land Cover Change from 2000 to 2040: A Case Study of the Nanchang Urban Agglomeration

by Yuheng Fu, Min Huang, Daohong Gong, Hui Lin, Yewen Fan and Wenying Du

Remote Sens. 2023, 15(19), 4645; https://doi.org/10.3390/rs15194645 - 22 Sep 2023

Cited by 16 | Viewed by 2076

Abstract

Land use/land cover change (LUCC) constitutes a significant contributor to variations in the storage of carbon within ecosystems and holds substantial significance within the context of the carbon cycling process. This study analyzed land use data from the Nanchang urban agglomeration in 2000 and 2020 to investigate changes in land use and carbon storage using the PLUS model and GIS. The results show the following: (1) From 2000 to 2020, the Nanchang urban agglomeration experienced reductions in the extents of croplands, woodlands, grasslands, and unused lands. The predominant trend in land transformation involved the conversion of cropland into built-up land. (2) Between 2000 and 2020, there was a declining trajectory observed in carbon storage for the Nanchang urban agglomeration, with an overall decrease of 1.13 × 10⁷ t. The space is characterized by a high-altitude perimeter and a low-altitude center. Urbanization’s encroachment on cropland is the main reason for declining carbon storage. (3) The predictive outcomes reveal that, in 2040, carbon storage in the Nanchang urban agglomeration will be reduced by 1.00 × 10⁷ t under the natural development scenario, and reduced by 3.90 × 10⁶ t and increased by 2.29 × 10⁵ t, respectively, under the cropland protection and ecological protection scenarios. The risk of carbon loss is significantly reduced by ecological protection policy interventions. Our analysis of the land use patterns and carbon storage distribution in the Nanchang urban agglomeration over the past 20 years and our exploration of the land use change trend over the next 20 years under the conservation policy provide a reference basis for increasing the carbon sink in the core area of the ecological city cluster of Poyang Lake and realizing the sustainable development of the city. Full article

(This article belongs to the Special Issue Classification and Feature Extraction from Remote Sensing Imagery and Point Cloud Data)

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27 pages, 31248 KiB

Open AccessArticle

A Triangular Grid Filter Method Based on the Slope Filter

by Chuanli Kang, Zitao Lin, Siyi Wu, Yiling Lan, Chongming Geng and Sai Zhang

Remote Sens. 2023, 15(11), 2930; https://doi.org/10.3390/rs15112930 - 4 Jun 2023

Cited by 4 | Viewed by 1759

Abstract

High-precision ground point cloud data has a wide range of applications in various fields, and the separation of ground points from non-ground points is a crucial preprocessing step. Therefore, designing an efficient, accurate, and stable ground extraction algorithm is highly significant for improving the processing efficiency and analysis accuracy of point cloud data. The study area in this article was a park in Guilin, Guangxi, China. The point cloud was obtained by utilizing the UAV platform. In order to improve the stability and accuracy of the filter algorithm, this article proposed a triangular grid filter based on the Slope Filter, found violation points by the spatial position relationship within each point in the triangulation network, improved KD-Tree-Based Euclidean Clustering, and applied it to the non-ground point extraction. This method is accurate, stable, and achieves the separation of ground points from non-ground points. Firstly, the Slope Filter is used to remove some non-ground points and reduce the error of taking ground points as non-ground points. Secondly, a triangular grid based on the triangular relationship between each point is established, and the violation triangle is determined through the grid; thus, the corresponding violation points are found in the violation triangle. Thirdly, according to the three-point collinear method to extract the regular points, these points are used to extract the regular landmarks by the KD-Tree-Based Euclidean Clustering and Convex Hull Algorithm. Finally, the dispersed points and irregular landmarks are removed by the Clustering Algorithm. In order to confirm the superiority of this algorithm, this article compared the filter effects of various algorithms on the study area and filtered the 15 data samples provided by ISPRS, obtaining an average error of 3.46%. The results show that the algorithm presented in this article has high processing efficiency and accuracy, which can significantly improve the processing efficiency of point cloud data in practical applications. Full article

(This article belongs to the Special Issue Classification and Feature Extraction from Remote Sensing Imagery and Point Cloud Data)

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17 pages, 5072 KiB

Open AccessArticle

Multilevel Feature Aggregated Network with Instance Contrastive Learning Constraint for Building Extraction

by Shiming Li, Tingrui Bao, Hui Liu, Rongxin Deng and Hui Zhang

Remote Sens. 2023, 15(10), 2585; https://doi.org/10.3390/rs15102585 - 15 May 2023

Cited by 6 | Viewed by 1718

Abstract

Building footprint extraction from remotely sensed imagery is a critical task in the field of illegal building discovery, urban dynamic monitoring, and disaster emergency response. Recent research has made significant progress in this area by utilizing deep learning techniques. However, it remains difficult to efficiently balance the spatial detail and rich semantic features. In particular, the extracted building edge is often inaccurate, especially in areas where the buildings are densely distributed, and the boundary of adjacent building instances is difficult to distinguish accurately. Additionally, identifying buildings with varying scales remains a challenging problem. To address the above problems, we designed a novel framework that aggregated multilevel contextual information extracted from multiple encoders. Furthermore, we introduced an instance constraint into contrastive learning to enhance the robustness of the feature representation. Experimental results demonstrated that our proposed method achieved 91.07% and 74.58% on the intersection over union metric on the WHU and Massachusetts datasets, respectively, outperforming the most recent related methods. Notably, our method significantly improved the accuracy of building boundaries, especially at the building instance level, and the integrity of multi-scale buildings. Full article

(This article belongs to the Special Issue Classification and Feature Extraction from Remote Sensing Imagery and Point Cloud Data)

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21 pages, 14629 KiB

Open AccessArticle

An Automatic Hierarchical Clustering Method for the LiDAR Point Cloud Segmentation of Buildings via Shape Classification and Outliers Reassignment

by Feng Wang, Guoqing Zhou, Jiali Xie, Bolin Fu, Haotian You, Jianjun Chen, Xue Shi and Bowen Zhou

Remote Sens. 2023, 15(9), 2432; https://doi.org/10.3390/rs15092432 - 5 May 2023

Cited by 6 | Viewed by 2858

Abstract

Shape segmentation in urban environments forms the foundation for tasks such as classification and reconstruction. Most artificial buildings with complex structures are composed of multiple simple geometric primitives. Based on this assumption, this paper proposes a divisive hierarchical clustering algorithm that uses shape classification and outliers reassignment to segment LiDAR point clouds in order to effectively identify the various shapes of structures that make up buildings. The proposed method adopts a coarse-to-fine strategy. Firstly, based on the geometric properties of different primitives in a Gaussian sphere space, coarse extraction is performed using Gaussian mapping and the DBSCAN algorithm to identify the primary structure of various shapes. Then, the error functions are constructed after parameterizing the recognized shapes. Finally, a minimum energy loss function is built by combining the error functions and binary integer programming (BIP) to redistribute the outlier points. Thereby, the accurate extraction of geometric primitives is achieved. Experimental evaluations on real point cloud datasets show that the indicators of precision, accuracy, and F1 score of our method are 0.98, 0.95, and 0.96 (point assignment) and 0.97, 0.95, and 0.95 (shape recognition), respectively. Compared with other state-of-the-art methods, the proposed method can efficiently segment planar and non-planar structures with higher quality from building point clouds. Full article

(This article belongs to the Special Issue Classification and Feature Extraction from Remote Sensing Imagery and Point Cloud Data)

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17 pages, 10248 KiB

Open AccessArticle

Efficient Point Cloud Object Classifications with GhostMLP

by Hawking Lai and K. L. Eddie Law

Remote Sens. 2023, 15(9), 2254; https://doi.org/10.3390/rs15092254 - 24 Apr 2023

Viewed by 2085

Abstract

Efficient models capable of handling large numbers of data points in point cloud research are in high demand in computer vision. Despite recent advancements in 3D classification and segmentation tasks in point cloud processing, the deep learning PointNeXt and PointMLP models are plagued with heavy computation requirements with limited efficiencies. In this paper, a novel GhostMLP model for point clouds is thus introduced. It takes the advantages of the GhostNet design modules and uses them to replace the MLP layers in the existing PointMLP model. The resulting GhostMLP architecture achieves superior classification performance with lower computation requirements. Compared to the PointMLP, GhostMLP maintains sustainable performance with fewer parameters and lower FLOPs computations. Indeed, it outperforms PointMLP on the ScanObjectNN dataset, achieving 88.7% overall accuracy and 87.6% mean accuracy with only 6 million parameters and 7.2 GFLOPs—about half the resources required by PointMLP. At the same time, GhostMLP-S is introduced as a lightweight version which also outperforms PointMLP in performance. GhostMLP completes faster training and inference with GPU and is the best-performing method that does not require any extra training data in the ScanObjectNN benchmark. Efficient point cloud analysis is essential in computer vision, and we believe that GhostMLP has the potential to become a powerful tool for large-scale point cloud analysis. Full article

(This article belongs to the Special Issue Classification and Feature Extraction from Remote Sensing Imagery and Point Cloud Data)

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23 pages, 28261 KiB

Open AccessArticle

Spatio-Temporal Changes of Mangrove-Covered Tidal Flats over 35 Years Using Satellite Remote Sensing Imageries: A Case Study of Beibu Gulf, China

by Ertao Gao and Guoqing Zhou

Remote Sens. 2023, 15(7), 1928; https://doi.org/10.3390/rs15071928 - 3 Apr 2023

Cited by 4 | Viewed by 2952

Abstract

Tidal flats provide ecosystem services to billions of people worldwide; however, their changing status is largely unknown. Several challenges in the fine extraction of tidal flats using remote sensing techniques, including tide-level and water-edge line changes, exist at present, especially regarding the spatial and temporal distribution of mangroves. This study proposed a tidal flats extraction method using a combination of threshold segmentation and tidal-level correction, considering the influence of mangrove changes. We extracted the spatial distribution of tidal flats in Beibu Gulf, Southwest China, from 1987 to 2021 using time-series Landsat and Sentinel-2 images, and further analyzed the dynamic variation characteristics of the total tidal flats, each coastal segment, and the range of erosion and silting. To quantitatively investigate the interaction between tidal flats and mangroves, this study established a regression model based on multi-temporal tidal flats and mangrove data. The results indicated that the overall accuracy of the tidal flat extraction results was 93.9%, and the kappa coefficient was 0.82. The total area of tidal flats in Beibu Gulf decreased by 130 km² from 1987 to 2021, with an average annual change of −3.7 km²/a. In addition, a negative correlation between the tidal flat change area and mangrove change area in Shankou, Maowei Sea, and Pearl Bay was observed, with correlation coefficients of −0.28, −0.30 and −0.64, respectively. These results demonstrate that the distribution of tidal flats provides a good environment and expansion space for the rapid growth of mangroves. These results can provide references for tidal flats’ resource conservation, ecological health assessment, and vegetation changes in coastal wetlands in China and other countries in Southeast Asia. Full article

(This article belongs to the Special Issue Classification and Feature Extraction from Remote Sensing Imagery and Point Cloud Data)

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