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Remote Sensing Image Change Detection and Feature Enhancement Based on Deep Learning

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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: 31 August 2026 | Viewed by 5529

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Special Issue Editors

Prof. Dr. Kaimin Sun

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Guest Editor

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: intelligent interpretation of remote sensing images; quantitative remote sensing and time-series analysis; multi-source remote sensing image change detection; UAV intelligent application

Dr. Wenzhuo Li

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Guest Editor

School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430072, China
Interests: image processing; change detection and ecological remote sensing monitoring

Dr. Xin Tao

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Guest Editor

Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Interests: geospatial information science and remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Ting Bai

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Guest Editor Assistant

School of Computer Science, Hubei University of Technology, Wuhan 430068, China
Interests: remote sensing change detection; land use/cover change detection; deep learning

Dr. Wangbin Li

E-Mail Website
Guest Editor Assistant

School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
Interests: multi-modal and multi-temporal remote sensing image analysis

Special Issue Information

Dear Colleagues,

Change detection using deep learning has become essential for understanding various environmental changes, including land use and cover transformations, biodiversity shifts, urbanization, and disaster response. A critical aspect of this process is feature enhancement, which improves the learning of image features. By innovating deep learning techniques for change detection and enhancing features, we can achieve more efficient and interpretable results, ultimately facilitating informed decision-making for both environmental and socio-economic challenges.

This Special Issue seeks to compile advanced research on deep learning-based remote sensing image change detection and feature enhancement, emphasizing innovative algorithms, architectures, and applications that improve accuracy, efficiency, and interpretability.

Submissions may address, but are not limited to, the following topics:

Remote sensing image change detection.
Knowledge-guided, data-driven change detection.
Remote sensing intelligent interpretation.
Multi-source remote sensing image analysis.
Multi-modal image analysis.
Multi-temporal remote sensing image analysis.
Deep learning for time-series analysis.
Object detection.
Semantic segmentation.
Image matching.
Scene association.
Land cover/land use change detection.
Feature fusion.
Feature enhancement.
Multi-scale feature extraction.
Ecological remote sensing monitoring.

Prof. Dr. Kaimin Sun
Dr. Wenzhuo Li
Dr. Xin Tao
Guest Editors

Dr. Ting Bai
Dr. Wangbin Li
Guest Editor Assistants

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 250 words) can be sent to the Editorial Office for assessment.

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 change detection
feature enhancement
feature fusion
semantic segmentation
deep learning
multi-model image analysis

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Published Papers (6 papers)

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Research

26 pages, 26133 KB

Open AccessArticle

DFS-YOLO: A Dynamic Feature Collaboration and State Space Framework for UAV-Based Infrared Object Detection

by Ziyan Wang, Wangbin Li and Kaimin Sun

Remote Sens. 2026, 18(9), 1422; https://doi.org/10.3390/rs18091422 - 3 May 2026

Viewed by 451

Abstract

UAV-based infrared target detection presents inherent challenges, including low signal-to-noise ratios, texture degradation, and severe scale variations. To address these issues, we propose DFS-YOLO, an approach based on dynamic feature collaboration and efficient state-space modeling. We introduce a Dynamic Range-Calibrated Area Attention (DRCAA) module in the backbone to stabilize feature activations under strong thermal clutter. Within the neck architecture, an Efficient Attentional Scale-Sequence Fusion (EASF) strategy reduces cross-scale semantic misalignment and ensures precise spatial coherence. Additionally, an EfficientViM-based state-space module captures global contextual dependencies while maintaining linear computational complexity. Finally, the Content-Guided Triple-Attention Fusion (CGTAFusion) module maximizes feature discriminability by calibrating fusion representations across the channel, spatial, and pixel dimensions. Extensive experiments on the HIT-UAV and IRSTD-1k benchmarks validate the efficacy of the DFS-YOLO framework. Compared to the baseline YOLOv12, DFS-YOLO’s performance has been significantly improved, increasing mAP@50 and mAP@50-95 by 10.16% and 7.55% on HIT-UAV, and by 1.84% and 3.18% on IRSTD-1k, respectively. These quantitative gains establish DFS-YOLO as a highly robust and state-of-the-art solution for complex infrared aerial surveillance. Full article

(This article belongs to the Special Issue Remote Sensing Image Change Detection and Feature Enhancement Based on Deep Learning)

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26 pages, 12515 KB

Open AccessArticle

DAFSDet: Dual-Attention Guided Few-Shot Object Detection in Remote Sensing Images

by Guangshuai Gao, Zhilin Zhang, Wei Zhang, Yunqi Shang, Yan Dong and Jiangtao Xi

Remote Sens. 2026, 18(9), 1345; https://doi.org/10.3390/rs18091345 - 28 Apr 2026

Viewed by 340

Abstract

Few-shot object detection aims to accurately identify and localize novel categories using only a small number of labeled samples. In remote sensing images, however, this task faces significant challenges due to substantial variations in target scale and complex backgrounds. To address these issues, this paper proposes a dual-attention guided few-shot object detection framework, DAFSDet. Specifically, a dual-attention strategy is implemented across the feature modeling and proposal generation stages. For feature fusion, the Content-Aware Strip Pyramid (CASP) is designed to enhance multi-scale feature representation by modeling spatial and contextual information. In the detection stage, a Deformable Attention RPN (DA-RPN) is proposed to improve the localization quality of candidate regions. With these designs, the proposed method effectively mitigates the challenges posed by multi-scale variations and complex backgrounds. Experimental results on the DIOR and NWPU VHR-10 datasets demonstrate consistent improvements over baseline methods, with notable gains of 7.54 mAP on DIOR Split 2 under the 10-shot setting and 2.09 mAP on NWPU VHR-10 under the 3-shot setting. These results indicate that the proposed method offers an effective solution for few-shot object detection in complex remote sensing scenarios. Full article

(This article belongs to the Special Issue Remote Sensing Image Change Detection and Feature Enhancement Based on Deep Learning)

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24 pages, 4653 KB

Open AccessArticle

AEDNet: Anisotropic Enhanced Dual-Branch Network for Infrared Small Target Detection

by Zhiqi Zhang, Jiayu Zhao, Shenggu Yuan and Yueqin Zhu

Remote Sens. 2026, 18(5), 817; https://doi.org/10.3390/rs18050817 - 6 Mar 2026

Viewed by 484

Abstract

Infrared small target detection (IRSTD) aims to extract weak small targets from complex backgrounds, and has significant practical application value in safety-critical fields such as traffic monitoring and maritime search and rescue. However, due to the inherently weak features of infrared small targets and the difficulty in effectively separating target from complex backgrounds, existing methods are prone to losing detail during the process. To aid this situation, this study proposes an anisotropic enhanced dual-branch network (AEDNet), including two core modules: the anisotropic dual-branch enhancement module (ADBE) and the multi-scale receptive field and attention collaboration module (MSRF-AC). To better separate target features from the background, the ADBE module employs independent branches of anisotropic operators to enhance target edge contrast and employs an adaptive dual-branch feature fusion to dynamically adjust feature weights, thereby improving the discriminability between the target and the background. Furthermore, to better preserve the details of the target, the MSRF-AC module employs multi-branch separable convolutions and is equipped with dual attention to capture small target features, thereby enhancing the ability to preserve target details. We conducted experiments on the NUDT-SIRST and MDvsFA datasets. Experimental results show that AEDNet outperforms existing SOTA methods across key metrics. Particularly, on the MDvsFA dataset, AEDNet achieves an IoU of 90.10%, an nIoU of 89.29%, a PD of 91.59%, and an FA of 6.25 × 10⁻⁶. Full article

(This article belongs to the Special Issue Remote Sensing Image Change Detection and Feature Enhancement Based on Deep Learning)

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22 pages, 10194 KB

Open AccessArticle

MBFI-Net: Multi-Branch Feature Interaction Network for Semantic Change Detection

by Qing Ding, Fengyan Wang, Kaiyuan Sun, Weilong Chen, Mingchang Wang and Gui Cheng

Remote Sens. 2026, 18(1), 179; https://doi.org/10.3390/rs18010179 - 5 Jan 2026

Viewed by 885

Abstract

Semantic change detection (SCD) effectively captures ground object transition information within change regions, delivering more comprehensive and detailed results than binary change detection (BCD) tasks. The existing multi-task SCD models enable parallel processing of segmentation and BCD of bi-temporal remote sensing images, but they still have shortcomings in feature mining, interaction, and cross-task transfer. To address these limitations, a multi-branch feature interaction network (MBFI-Net) is proposed. MBFI-Net designs parallel encoding branches with attention mechanisms that enhance semantic change perception by jointly modeling global contextual patterns and local details. In addition, MBFI-Net proposes bi-temporal feature interaction (BTFI) and cross-task feature transfer (CTFT) modules to improve feature diversity and representativeness, and combines with prior logical relationship constraints to improve SCD performance. Comparative and ablation studies on the SECOND and Landsat-SCD datasets highlight the superiority and robustness of MBFI-Net, which achieves SeKs of 0.2117 and 0.5543, respectively. Furthermore, MBFI-Net strikes a balance between SCD results and model complexity and has superior detection performance for semantic change categories with a small proportion. Full article

(This article belongs to the Special Issue Remote Sensing Image Change Detection and Feature Enhancement Based on Deep Learning)

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36 pages, 22245 KB

Open AccessArticle

CMSNet: A SAM-Enhanced CNN–Mamba Framework for Damaged Building Change Detection in Remote Sensing Imagery

by Jianli Zhang, Liwei Tao, Wenbo Wei, Pengfei Ma and Mengdi Shi

Remote Sens. 2025, 17(23), 3913; https://doi.org/10.3390/rs17233913 - 3 Dec 2025

Viewed by 1469

Abstract

In war and explosion scenarios, buildings often suffer varying degrees of damage characterized by complex, irregular, and fragmented spatial patterns, posing significant challenges for remote sensing–based change detection. Additionally, the scarcity of high-quality datasets limits the development and generalization of deep learning approaches. To overcome these issues, we propose CMSNet, an end-to-end framework that integrates the structural priors of the Segment Anything Model (SAM) with the efficient temporal modeling and fine-grained representation capabilities of CNN–Mamba. Specifically, CMSNet adopts CNN–Mamba as the backbone to extract multi-scale semantic features from bi-temporal images, while SAM-derived visual priors guide the network to focus on building boundaries and structural variations. A Pre-trained Visual Prior-Guided Feature Fusion Module (PVPF-FM) is introduced to align and fuse these priors with change features, enhancing robustness against local damage, non-rigid deformations, and complex background interference. Furthermore, we construct a new RWSBD (Real-world War Scene Building Damage) dataset based on Gaza war scenes, comprising 42,732 annotated building damage instances across diverse scales, offering a strong benchmark for real-world scenarios. Extensive experiments on RWSBD and three public datasets (CWBD, WHU-CD, and LEVIR-CD+) demonstrate that CMSNet consistently outperforms eight state-of-the-art methods in both quantitative metrics (F1, IoU, Precision, Recall) and qualitative evaluations, especially in fine-grained boundary preservation, small-scale change detection, and complex scene adaptability. Overall, this work introduces a novel detection framework that combines foundation model priors with efficient change modeling, along with a new large-scale war damage dataset, contributing valuable advances to both research and practical applications in remote sensing change detection. Additionally, the strong generalization ability and efficient architecture of CMSNet highlight its potential for scalable deployment and practical use in large-area post-disaster assessment. Full article

(This article belongs to the Special Issue Remote Sensing Image Change Detection and Feature Enhancement Based on Deep Learning)

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27 pages, 10767 KB

Open AccessArticle

HCTANet: Hierarchical Cross-Temporal Attention Network for Semantic Change Detection in Complex Remote Sensing Scenes

by Zhuli Xie, Gang Wan, Zhanji Wei, Nan Li and Guangde Sun

Remote Sens. 2025, 17(23), 3906; https://doi.org/10.3390/rs17233906 - 2 Dec 2025

Viewed by 805

Abstract

Semantic change detection has become a key technology for monitoring the evolution of land cover and land use categories at the semantic level. However, existing methods often lack effective information interaction and fail to capture changes at multiple granularities using single-scale features, resulting in inconsistent outcomes and frequent missed or false detections. To address these challenges, we propose a three-branch model HCTANet, which enhances spatial and semantic feature representations at each time stage and models semantic correlations and differences between multi-temporal images through three innovative modules. First, the multi-scale change mapping association module extracts and fuses multi-resolution dual-temporal difference features in parallel, explicitly constraining semantic segmentation results with the change area output. Second, an adaptive collaborative semantic attention mechanism is introduced, modeling the semantic correlations of dual-temporal features via dynamic weight fusion and cross-temporal cross-attention. Third, the spatial semantic residual aggregation module aggregates global context and high-resolution shallow features through residual connections to restore pixel-level boundary details. HCTANet is evaluated on the SECOND, SenseEarth 2020 and AirFC datasets, and the results show that it outperforms existing methods in metrics such as mIoU and SeK, demonstrating its superior capability and effectiveness in accurately detecting semantic changes in complex remote sensing scenarios. Full article

(This article belongs to the Special Issue Remote Sensing Image Change Detection and Feature Enhancement Based on Deep Learning)

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