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Remote Sensing
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Multi-Task Remote Sensing Image Analysis: Classification, Segmentation, and Change Detection
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Multi-Task Remote Sensing Image Analysis: Classification, Segmentation, and Change Detection

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

Deadline for manuscript submissions: 15 March 2026 | Viewed by 2827

Special Issue Editor

Dr. Liguo Weng

E-Mail Website
Guest Editor
School of Automation, Nanjing University of Information Science & Technology, Nanjing, China
Interests: image segmentation; big data; machine learning; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advancements in remote sensing technologies have opened new possibilities for multi-task image analysis, offering opportunities for solving complex challenges in classification, segmentation, and change detection. This Special Issue, Multi-Task Remote Sensing Image Analysis: Classification, Segmentation, and Change Detection, aims to bring together innovative research addressing the integration of these tasks to improve accuracy and efficiency in remote sensing applications. These multi-task approaches are crucial for various domains such as environmental monitoring, land use analysis, disaster management, and urban planning.

The goal of this Special Issue is to present state-of-the-art methods that unify or enhance different image analysis tasks, such as scene classification, semantic segmentation, and temporal change detection, within the remote sensing domain. We invite researchers to contribute novel algorithms, models, and frameworks that advance the field of multi-task learning and facilitate comprehensive and scalable remote sensing image analysis.

Topics of interest include, but are not limited to, the following:

  • Multi-task learning for remote sensing image analysis;
  • Joint classification and segmentation techniques;
  • Change detection in multi-temporal remote sensing imagery;
  • Deep learning methods for multi-task image processing;
  • Data fusion for improved classification and change detection;
  • Transfer learning in multi-task remote sensing applications;
  • Applications of AI in multi-task remote sensing;
  • High-resolution and large-scale remote sensing analysis;
  • Challenges in imbalanced data and multi-class segmentation.

This Special Issue encourages submissions that address both theoretical innovations and practical applications, providing a platform for researchers and professionals to discuss emerging trends and future directions in multi-task remote sensing image analysis.

Dr. Liguo Weng
Guest Editor

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

  • multi-task learning
  • remote sensing image classification
  • semantic segmentation
  • change detection
  • deep learning
  • multi-temporal analysis
  • data fusion
  • transfer learning
  • environmental monitoring
  • high-resolution remote sensing

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

Published Papers (3 papers)

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Research

26 pages, 15436 KiB  
Article
AGCD: An Attention-Guided Graph Convolution Network for Change Detection of Remote Sensing Images
by Heng Li, Xin Lyu, Xin Li, Yiwei Fang, Zhennan Xu, Xinyuan Wang, Chengming Zhang, Chun Xu, Shaochuan Chen and Chengxin Lu
Remote Sens. 2025, 17(8), 1367; https://doi.org/10.3390/rs17081367 - 11 Apr 2025
Viewed by 202
Abstract
Change detection is a crucial field in remote sensing image analysis for tracking environmental dynamics. Although convolutional neural networks (CNNs) have made impressive strides in this field, their grid-based processing structures struggle to capture abundant semantics and complex spatial-temporal correlations of bitemporal features, [...] Read more.
Change detection is a crucial field in remote sensing image analysis for tracking environmental dynamics. Although convolutional neural networks (CNNs) have made impressive strides in this field, their grid-based processing structures struggle to capture abundant semantics and complex spatial-temporal correlations of bitemporal features, leading to high uncertainty in distinguishing true changes from pseudo changes. To overcome these limitations, we propose the Attention-guided Graph convolution network for Change Detection (AGCD), a novel framework that integrates a graph convolutional network (GCN) and an attention mechanism to enhance change-detection performance. AGCD introduces three novel modules, including Graph-level Feature Difference Module (GFDM) for enhanced feature interaction, Multi-scale Feature Fusion Module (MFFM) for detailed semantic representation and Spatial-Temporal Attention Module (STAM) for refined spatial-temporal dependency modeling. These modules enable AGCD to reduce pseudo changes triggered by seasonal variations and varying imaging conditions, thereby improving the accuracy and reliability of change-detection results. Extensive experiments on three benchmark datasets demonstrate that AGCD’s superior performance, achieving the best F1-score of 90.34% and IoU of 82.38% on the LEVIR-CD dataset and outperforming existing state-of-the-art methods by a notable margin. Full article
(This article belongs to the Special Issue Multi-Task Remote Sensing Image Analysis: Classification, Segmentation, and Change Detection)
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24 pages, 2879 KiB  
Article
A Frequency Attention-Enhanced Network for Semantic Segmentation of High-Resolution Remote Sensing Images
by Jianyi Zhong, Tao Zeng, Zhennan Xu, Caifeng Wu, Shangtuo Qian, Nan Xu, Ziqi Chen, Xin Lyu and Xin Li
Remote Sens. 2025, 17(3), 402; https://doi.org/10.3390/rs17030402 - 24 Jan 2025
Viewed by 1123
Abstract
Semantic segmentation of high-resolution remote sensing images (HRRSIs) presents unique challenges due to the intricate spatial and spectral characteristics of these images. Traditional methods often prioritize spatial information while underutilizing the rich spectral context, leading to limited feature discrimination capabilities. To address these [...] Read more.
Semantic segmentation of high-resolution remote sensing images (HRRSIs) presents unique challenges due to the intricate spatial and spectral characteristics of these images. Traditional methods often prioritize spatial information while underutilizing the rich spectral context, leading to limited feature discrimination capabilities. To address these issues, we propose a novel frequency attention-enhanced network (FAENet), which incorporates a frequency attention model (FreqA) to jointly model spectral and spatial contexts. FreqA leverages discrete wavelet transformation (DWT) to decompose input images into distinct frequency components, followed by a two-stage attention mechanism comprising inner-component channel attention (ICCA) and cross-component channel attention (CCCA). These mechanisms enhance spectral representation, which is further refined through a self-attention (SA) module to capture long-range dependencies before transforming back into the spatial domain. FAENet’s encoder–decoder architecture facilitates multiscale feature refinement, enabling effective segmentation. Extensive experiments on the ISPRS Potsdam and LoveDA benchmarks demonstrate that FAENet outperforms state-of-the-art models, achieving superior segmentation accuracy. Ablation studies further validate the contributions of ICCA and CCCA. Moreover, efficiency comparisons confirm the superiority of the proposed FAENet over other models. Full article
(This article belongs to the Special Issue Multi-Task Remote Sensing Image Analysis: Classification, Segmentation, and Change Detection)
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32 pages, 10548 KiB  
Article
An Unsupervised Remote Sensing Image Change Detection Method Based on RVMamba and Posterior Probability Space Change Vector
by Jiaxin Song, Shuwen Yang, Yikun Li and Xiaojun Li
Remote Sens. 2024, 16(24), 4656; https://doi.org/10.3390/rs16244656 - 12 Dec 2024
Cited by 1 | Viewed by 987
Abstract
Change vector analysis in posterior probability space (CVAPS) is an effective change detection (CD) framework that does not require sound radiometric correction and is robust against accumulated classification errors. Based on training samples within target images, CVAPS can generate a uniformly scaled change-magnitude [...] Read more.
Change vector analysis in posterior probability space (CVAPS) is an effective change detection (CD) framework that does not require sound radiometric correction and is robust against accumulated classification errors. Based on training samples within target images, CVAPS can generate a uniformly scaled change-magnitude map that is suitable for a global threshold. However, vigorous user intervention is required to achieve optimal performance. Therefore, to eliminate user intervention and retain the merit of CVAPS, an unsupervised CVAPS (UCVAPS) CD method, RFCC, which does not require rigorous user training, is proposed in this study. In the RFCC, we propose an unsupervised remote sensing image segmentation algorithm based on the Mamba model, i.e., RVMamba differentiable feature clustering, which introduces two loss functions as constraints to ensure that RVMamba achieves accurate segmentation results and to supply the CSBN module with high-quality training samples. In the CD module, the fuzzy C-means clustering (FCM) algorithm decomposes mixed pixels into multiple signal classes, thereby alleviating cumulative clustering errors. Then, a context-sensitive Bayesian network (CSBN) model is introduced to incorporate spatial information at the pixel level to estimate the corresponding posterior probability vector. Thus, it is suitable for high-resolution remote sensing (HRRS) imagery. Finally, the UCVAPS framework can generate a uniformly scaled change-magnitude map that is suitable for the global threshold and can produce accurate CD results. The experimental results on seven change detection datasets confirmed that the proposed method outperforms five state-of-the-art competitive CD methods. Full article
(This article belongs to the Special Issue Multi-Task Remote Sensing Image Analysis: Classification, Segmentation, and Change Detection)
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