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Intelligent Remote Sensing: AI-Powered Techniques for Enhanced Data Analysis and Interpretation

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

Deadline for manuscript submissions: 30 January 2025 | Viewed by 2147

Special Issue Editors

Prof. Dr. Hai Wang

E-Mail Website
Guest Editor
School of Aerospace Science and Technology, Xidian University, Xi’an 710126, China
Interests: hyperspectral image; remote sensing image processing; artificial intelligence; hyperspectral anomaly detection; object detection; radar signal processing
Dr. Nianyin Zeng

E-Mail Website
Guest Editor
Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen 61102, China
Interests: intelligent data analysis; machine learning and computer vision; computational intelligent; artificial intelligence in biomedical and industry
Special Issues, Collections and Topics in MDPI journals
Dr. Shou Feng

E-Mail Website
Guest Editor
College of Information and Comminication Engineering, Harbin Engineering University, Harbin 150001, China
Interests: remote sensing image processing; data mining; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent Remote Sensing harnesses the power of Artificial Intelligence to revolutionize data analysis and interpretation in remote sensing. As remote sensing data deluge in from satellites and drones, AI techniques offer unprecedented capabilities for processing, classifying, and extracting insights from this vast trove of information. By automating complex tasks and revealing patterns unseen to the naked eye, this research area is crucial for advancing precision agriculture, environmental monitoring, disaster response, and urban planning, among others.

This Special Issue is highly relevant to the scope of remote sensing, as it addresses a key trend in the field: the integration of AI with remote sensing technologies. By highlighting the potential and applications of AI-driven techniques, it contributes to advancing the state of the art in remote sensing research and practice, fostering interdisciplinary collaboration and innovation.

Articles may include, but are not limited to, the following topics:

  • General remote sensing image processing, including object detection, classification, segmentation, anomaly detection, change detection, denoising, fusion, etc.
  • Real-world application with remote sensing data, such as optical images, SAR images, multispectral/hyperspectral images, multi-source data, and so on.
  • Methodology: deep learning models, traditional models, interpretable models, etc.

Prof. Dr. Hai Wang
Dr. Nianyin Zeng
Dr. Shou Feng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing image processing
  • remote sensing semantic analysis
  • remote sensing applications
  • big data analysis
  • data fusion
  • artificial intelligence
  • machine learning
  • deep learning

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

27 pages, 7948 KiB  
Article
SSUM: Spatial–Spectral Unified Mamba for Hyperspectral Image Classification
by Song Lu, Min Zhang, Yu Huo, Chenhao Wang, Jingwen Wang and Chenyu Gao
Remote Sens. 2024, 16(24), 4653; https://doi.org/10.3390/rs16244653 (registering DOI) - 12 Dec 2024
Viewed by 451
Abstract
How to effectively extract spectral and spatial information and apply it to hyperspectral image classification (HSIC) has been a hot research topic. In recent years, the transformer-based HSIC models have attracted much interest due to their advantages in long-distance modeling of spatial and [...] Read more.
How to effectively extract spectral and spatial information and apply it to hyperspectral image classification (HSIC) has been a hot research topic. In recent years, the transformer-based HSIC models have attracted much interest due to their advantages in long-distance modeling of spatial and spectral features in hyperspectral images (HSIs). However, the transformer-based method suffers from high computational complexity, especially in HSIC tasks that require processing large amounts of data. In addition, the spatial variability inherent in HSIs limits the performance improvement of HSIC. To handle these challenges, a novel Spectral–Spatial Unified Mamba (SSUM) model is proposed, which introduces the State Space Model (SSM) into HSIC tasks to reduce computational complexity and improve model performance. The SSUM model is composed of two branches, i.e., the Spectral Mamba branch and the Spatial Mamba branch, designed to extract the features of HSIs from both spectral and spatial perspectives. Specifically, in the Spectral Mamba branch, a nearest-neighbor spectrum fusion (NSF) strategy is proposed to alleviate the interference caused by the spatial variability (i.e., same object having different spectra). In addition, a novel sub-spectrum scanning (SS) mechanism is proposed, which scans along the sub-spectrum dimension to enhance the model’s perception of subtle spectral details. In the Spatial Mamba branch, a Spatial Mamba (SM) module is designed by combining a 2D Selective Scan Module (SS2D) and Spatial Attention (SA) into a unified network to sufficiently extract the spatial features of HSIs. Finally, the classification results are derived by uniting the output feature of the Spectral Mamba and Spatial Mamba branch, thus improving the comprehensive performance of HSIC. The ablation studies verify the effectiveness of the proposed NSF, SS, and SM. Comparison experiments on four public HSI datasets show the superior of the proposed SSUM. Full article
(This article belongs to the Special Issue Intelligent Remote Sensing: AI-Powered Techniques for Enhanced Data Analysis and Interpretation)
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20 pages, 6585 KiB  
Article
Remote Sensing Image Denoising Based on Feature Interaction Complementary Learning
by Shaobo Zhao, Youqiang Dong, Xi Cheng, Yu Huo, Min Zhang and Hai Wang
Remote Sens. 2024, 16(20), 3820; https://doi.org/10.3390/rs16203820 - 14 Oct 2024
Viewed by 967
Abstract
Optical remote sensing images are of considerable significance in a plethora of applications, including feature recognition and scene semantic segmentation. However, the quality of remote sensing images is compromised by the influence of various types of noise, which has a detrimental impact on [...] Read more.
Optical remote sensing images are of considerable significance in a plethora of applications, including feature recognition and scene semantic segmentation. However, the quality of remote sensing images is compromised by the influence of various types of noise, which has a detrimental impact on their practical applications in the aforementioned fields. Furthermore, the intricate texture characteristics inherent to remote sensing images present a significant hurdle in the removal of noise and the restoration of image texture details. In order to address these challenges, we propose a feature interaction complementary learning (FICL) strategy for remote sensing image denoising. In practical terms, the network is comprised of four main components: noise predictor (NP), reconstructed image predictor (RIP), feature interaction module (FIM), and fusion module. The combination of these modules serves to not only complete the fusion of the prediction results of NP and RIP, but also to achieve a deep coupling of the characteristics of the two predictors. Consequently, the advantages of noise prediction and reconstructed image prediction can be combined, thereby enhancing the denoising capability of the model. Furthermore, comprehensive experimentation on both synthetic Gaussian noise datasets and real-world denoising datasets has demonstrated that FICL has achieved favorable outcomes, emphasizing the efficacy and robustness of the proposed framework. Full article
(This article belongs to the Special Issue Intelligent Remote Sensing: AI-Powered Techniques for Enhanced Data Analysis and Interpretation)
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