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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: 15 May 2026 | Viewed by 12362

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Special Issue Editors

Dr. Chenxiao Zhang

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Guest Editor

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Interests: remote sensing image

Dr. Zhuo Zheng

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Guest Editor

Department of Computer Science, Stanford University, Stanford, CA 94305, USA
Interests: earth vision remote sensing; computational sustainability; change detection
Special Issues, Collections and Topics in MDPI journals

Dr. Nour Aburaed

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Guest Editor

College of Engineering and IT, University of Dubai, Dubai, United Arab Emirates
Interests: remote sensing; neural networks; image processing; super resolution; classification; segmentation; fusion

Special Issue Information

Dear Colleagues,

The advent of advanced sensors and imaging techniques has led to an exponential growth in the volume of high-resolution remote sensing images. The rich information contained in these images has made them indispensable in domains such as urban planning, precision agriculture, environmental monitoring, and disaster risk reduction. However, to fully harness their potential, there is a critical need for efficient and robust image processing and analysis techniques.

As remote sensing data continue to grow in complexity and scale, the development of innovative methods for image classification, segmentation, and change detection becomes essential. These advances will not only enhance decision-making processes but also support the sustainable management of resources, climate monitoring, and rapid response to natural disasters. The integration of machine learning, artificial intelligence, and big data analytics is increasingly shaping the landscape of remote sensing research and pushing the boundaries of what is possible.

This Special Issue aims to highlight the latest developments and innovations in the field of high-resolution satellite or aerial images, covering a broad spectrum of topics on deep learning-based image processing and analysis methods. The contributions will focus on advanced classification, detection, and fusion techniques that enable precise scene-level, object-level, and pixel-level land surface understanding. The integration of recent deep learning techniques, for example, large pre-trained multi-modality models and earth observation tasks, such as land cover classification, change detection, and object detection, will be a key highlight, showcasing how these techniques are revolutionizing the analysis of high-resolution remote sensing images in the realm of geosciences. Furthermore, the Issue will also explore novel applications demonstrating the practical impact of recent advances in high-resolution image analysis, underscoring the transformative potential of these technologies in addressing real-world challenges.

The scope includes, but is not limited to, the following:

Large multi-modality models for earth observation tasks;
generalizable deep learning methods for high-resolution image classification, object detection, and change detection;
the transfer of computer vision models for robust high-resolution image interpretation;
efficient deep learning architectures for large-scale high-resolution image analysis;
semi-supervised and unsupervised methods for image segmentation and change detection;
multi-source remote sensing image fusion and image registration methods;
remote sensing foundation models and their applications;
large vision language models for the understanding of remote sensing images;
large-scale high-resolution remote sensing image datasets;
generative models in remote sensing;
remote sensing image super-resolution;
image denoising;
explainable AI in remote sensing.

Dr. Chenxiao Zhang
Dr. Zhuo Zheng
Dr. Nour Aburaed
Guest Editors

Hongruixuan Chen
Guest Editor Assistant
Affiliation: Graduate School of Frontier Sciences, The University of Tokyo
Email: Qschrx@g.ecc.u-tokyo.ac.jp
Webpage: https://chrx97.com/
Interests: Remote Sensing, Deep Learning, Domain Adaptation, Change Detection, Damage Assessment

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

high-resolution images
deep learning
image classification
change detection
object detection
image processing and analysis
foundation model
image generation

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Published Papers (6 papers)

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Research

29 pages, 4367 KB

Open AccessArticle

Contrastive Masked Feature Modeling for Self-Supervised Representation Learning of High-Resolution Remote Sensing Images

by Shiyan Pang, Jianwu Xiang, Zhiqi Zuo, Hanchun Hu and Huiwei Jiang

Remote Sens. 2026, 18(4), 626; https://doi.org/10.3390/rs18040626 - 17 Feb 2026

Cited by 1 | Viewed by 712

Abstract

As an emerging learning paradigm, self-supervised learning (SSL) has attracted extensive attention due to its ability to mine features with effective representation from massive unlabeled data. In particular, SSL, driven by contrastive learning and masked modeling, shows great potential in general visual tasks. However, because of the diversity of ground target types, the complexity of spectral radiation characteristics, and changes in environmental conditions, existing SSL frameworks exhibit limited feature extraction accuracy and generalization ability when applied to complex remote sensing scenarios. To address this issue, we propose a hybrid SSL framework that integrates the advantages of contrastive learning and masked modeling to extract more robust and reliable features from remote sensing images. The proposed framework includes two parallel branches: one branch uses a contrastive learning strategy to strengthen global feature representation and capture image structural information by constructing positive and negative sample pairs; the other branch adopts a masked modeling strategy, focusing on the fine analysis of local details and predicting the features of masked areas, thereby establishing connections between global and local features. Additionally, to better integrate local and global features, we adopt a hybrid CNN+Transformer architecture, which is particularly suitable for intensive downstream tasks such as semantic segmentation. Extensive experimental results demonstrate that the proposed framework not only exhibits superior feature extraction ability and higher accuracy in small-sample scenarios but also outperforms state-of-the-art mainstream SSL frameworks on large-scale datasets. Full article

(This article belongs to the Special Issue Recent Advances in Deep Learning-Based High-Resolution Image Processing and Analysis)

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27 pages, 37118 KB

Open AccessArticle

FDFENet: Cropland Change Detection in Remote Sensing Images Based on Frequency Domain Feature Exchange and Multiscale Feature Enhancement

by Yujiang He, Yurong Qian, Xin Wang, Lu Bai, Yuanxu Wang, Hanming Wei, Xingke Huang, Junyi Lv, Xin Yang, Min Duan, Weijun Gong and Madina Mansurova

Remote Sens. 2026, 18(1), 128; https://doi.org/10.3390/rs18010128 - 30 Dec 2025

Viewed by 780

Abstract

Cropland change detection (CD) in high-resolution remote sensing images is critical for cropland protection and food security. However, style differences caused by inconsistent imaging conditions (such as season and illumination) and ground object scale differences often lead to high numbers of false and missed detections. Existing approaches, predominantly relying on spatial domain features and a multiscale framework, struggle to address these issues effectively. Therefore, we propose FDFENet, incorporating a Frequency Domain Feature Exchange Module (FFEM) that unifies image styles by swapping the low-frequency components of bitemporal features. A Frequency Domain Aggregation Distribution Module (FDADM) is also introduced as a comparative alternative for handling style discrepancies. Subsequently, a Multiscale Feature Enhancement Module (MSFEM) strengthens feature representation, while a Multiscale Change Perception Module (MSCPM) suppresses non-change information, and the two modules work cooperatively to improve detection sensitivity to multiscale ground objects. Compared with the FDADM, the FFEM exhibits superior parameter efficiency and engineering stability, making it more suitable as the primary solution for long-term deployment. Evaluations on four CD datasets (CLCD, GFSWCLCD, LuojiaSETCLCD, and HRCUSCD) demonstrate that FDFENet outperformed 13 state-of-the-art methods, achieving F1 and IOU scores of 77.09% and 62.72%, 81.81% and 73.63%, 74.47% and 59.32%, and 75.95% and 61.23%, respectively. This demonstrates FDFENet’s effectiveness in addressing style differences and ground object scale differences, enabling high-precision cropland monitoring to support food security and sustainable cropland management. Full article

(This article belongs to the Special Issue Recent Advances in Deep Learning-Based High-Resolution Image Processing and Analysis)

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27 pages, 15418 KB

Open AccessArticle

AGFNet: Adaptive Guided Scanning and Frequency-Enhanced Network for High-Resolution Remote Sensing Building Change Detection

by Xingchao Liu, Liang Tian, Zheng Wang, Yonggang Wang, Runze Gao, Heng Zhang and Yvjuan Deng

Remote Sens. 2025, 17(23), 3844; https://doi.org/10.3390/rs17233844 - 27 Nov 2025

Viewed by 839

Abstract

Change detection in high-resolution remote sensing imagery is vital for applications such as urban expansion monitoring, land-use analysis, and disaster assessment. However, existing methods often underutilize the differential features of bi-temporal images and struggle with complex backgrounds, illumination variations, and pseudo-changes, which hinder accurate identification of true changes. To address these challenges, this paper proposes a Siamese change detection network that integrates an adaptive scanning state-space model with frequency-domain enhancement. The backbone is constructed using Visual State Space (VSS) Blocks, and a Cross-Spatial Guidance Attention (CSGA) module is designed to explicitly guide cross-temporal feature alignment, thereby enhancing the reliability of differential feature representation. Furthermore, a Frequency-guided Adaptive Difference Module (FADM) is developed to apply adaptive low-pass filtering, effectively suppressing textures, noise, illumination variations, and sensor discrepancies while reinforcing spatial-domain differences to emphasize true changes. Finally, a Dual-Stage Multi-Scale Residual Integrator (DS-MRI) is introduced, incorporating both VSS Blocks and the newly designed Attention-Guided State Space (AGSS) Blocks. Unlike fixed scanning mechanisms, AGSS dynamically generates scanning sequences guided by CSGA, enabling a task-adaptive and context-aware decoding strategy. Extensive experiments on three public datasets (LEVIR-CD, WHU-CD, and SYSU-CD) demonstrate that the proposed method surpasses mainstream approaches in both accuracy and efficiency, exhibiting superior robustness under complex backgrounds and in weak-change scenarios. Full article

(This article belongs to the Special Issue Recent Advances in Deep Learning-Based High-Resolution Image Processing and Analysis)

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35 pages, 125255 KB

Open AccessArticle

VideoARD: An Analysis-Ready Multi-Level Data Model for Remote Sensing Video

by Yang Wu, Chenxiao Zhang, Yang Lu, Yaofeng Su, Xuping Jiang, Zhigang Xiang and Zilong Li

Remote Sens. 2025, 17(22), 3746; https://doi.org/10.3390/rs17223746 - 18 Nov 2025

Viewed by 1404

Abstract

Remote sensing video (RSV) provides continuous, high spatiotemporal earth observations that are increasingly important for environmental monitoring, disaster response, infrastructure inspection and urban management. Despite this potential, operational use of video streams is hindered by very large data volumes, heterogeneous acquisition platforms, inconsistent preprocessing practices, and the absence of standardized formats that deliver data ready for immediate analysis. These shortcomings force repeated low-level computation, complicate semantic extraction, and limit reproducibility and cross-sensor integration. This manuscript presents a principled multi-level analysis-ready data (ARD) model for remote sensing video, named VideoARD, along with VideoCube, a spatiotemporal management and query infrastructure that implements and operationalizes the model. VideoARD formalizes semantic abstraction at scene, object, and event levels and defines minimum and optimal readiness configurations for each level. The proposed pipeline applies stabilization, georeferencing, key frame selection, object detection, trajectory tracking, event inference, and entity materialization. VideoCube places the resulting entities into a five-dimensional structure indexed by spatial, temporal, product, quality, and semantic dimension, and supports earth observation OLAP-style operations to enable efficient slicing, aggregation, and drill down. Benchmark experiments and three application studies, covering vessel speed monitoring, wildfire detection, and near-real-time three-dimensional reconstruction, quantify system performance and operational utility. Results show that the proposed approach achieves multi-gigabyte-per-second ingestion under parallel feeds, sub-second scene retrieval for typical queries, and second-scale trajectory reconstruction for short tracks. Case studies demonstrate faster alert generation, improved detection consistency, and substantial reductions in preprocessing and manual selection work compared with on-demand baselines. The principal trade-off is an upfront cost for materialization and storage that becomes economical when queries are repeated or entities are reused. The contribution of this work lies in extending the analysis-ready data concept from static imagery to continuous video streams and in delivering a practical, scalable architecture that links semantic abstraction to high-performance spatiotemporal management, thereby improving responsiveness, reproducibility, and cross-sensor analysis for Earth observation. Full article

(This article belongs to the Special Issue Recent Advances in Deep Learning-Based High-Resolution Image Processing and Analysis)

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29 pages, 23397 KB

Open AccessArticle

Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution

by Wangyou Chen, Shenming Qu, Laigan Luo and Yongyong Lu

Remote Sens. 2025, 17(6), 1078; https://doi.org/10.3390/rs17061078 - 19 Mar 2025

Cited by 4 | Viewed by 2380

Abstract

In the field of remote sensing, super-resolution methods based on deep learning have made significant progress. However, redundant feature extraction and inefficient feature fusion can, respectively, result in excessive parameters and restrict the precise reconstruction of features, making the model difficult to deploy in practical remote-sensing tasks. To address this issue, we propose a lightweight Dual Attention Fusion Enhancement Network (DAFEN) for remote-sensing image super-resolution. Firstly, we design a lightweight Channel-Spatial Lattice Block (CSLB), which consists of Group Residual Shuffle Blocks (GRSB) and a Channel-Spatial Attention Interaction Module (CSAIM). The GRSB improves the efficiency of redundant convolution operations, while the CSAIM enhances interactive learning. Secondly, to achieve superior feature fusion and enhancement, we design a Forward Fusion Enhancement Module (FFEM). Through the forward fusion strategy, more high-level feature details are retained for better adaptation to remote-sensing tasks. In addition, the fused features are further refined and rescaled by Self-Calibrated Group Convolution (SCGC) and Contrast-aware Channel Attention (CCA), respectively. Extensive experiments demonstrate that DAFEN achieves better or comparable performance compared with state-of-the-art lightweight super-resolution models while reducing complexity by approximately 10∼48%. Full article

(This article belongs to the Special Issue Recent Advances in Deep Learning-Based High-Resolution Image Processing and Analysis)

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20 pages, 5636 KB

Open AccessArticle

Beyond the Remote Sensing Ecological Index: A Comprehensive Ecological Quality Evaluation Using a Deep-Learning-Based Remote Sensing Ecological Index

by Xi Gong, Tianqi Li, Run Wang, Sheng Hu and Shuai Yuan

Remote Sens. 2025, 17(3), 558; https://doi.org/10.3390/rs17030558 - 6 Feb 2025

Cited by 15 | Viewed by 4526

Abstract

Ecological integrity is fundamental to human survival and development. However, rapid urbanization and population growth have significantly disrupted ecosystems. Despite the focus of the International Geosphere-Biosphere Programme (IGBP) on terrestrial ecosystems and land use/cover changes, existing ecological indices, such as the Remote Sensing Ecological Index (RSEI), have limitations, including an overreliance on single indicators and inability to fully encapsulate the ecological conditions of urban areas. This study addresses these gaps by proposing a Deep-learning-based Remote Sensing Ecological Index (DRSEI) that integrates human economic activities and leverages an autoencoder neural network with long short-term memory (LSTM) modules to account for nonlinearity in ecological quality assessments. The DRSEI model utilizes multi-temporal remote sensing data from the Landsat series, WorldPop, and NPP-VIIRS and was applied to evaluate the ecological conditions of Hubei Province, China, over the past two decades. The key findings indicate that ecological environmental quality gradually improved, particularly from 2000 to 2010, with the rate of improvement subsequently slowing. The DRSEI outperformed the traditional RSEI and had a significantly higher Pearson correlation coefficient than the Ecological Index (EI), thus demonstrating enhanced accuracy and predictive performance. This study presents an innovative approach to ecological assessment that offers a more comprehensive, accurate, and nuanced understanding of ecological changes over time. Integrating socioeconomic factors with deep learning techniques contributes significantly to the field and has implications for ecological risk control and sustainable development. Full article

(This article belongs to the Special Issue Recent Advances in Deep Learning-Based High-Resolution Image Processing and Analysis)

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