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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 October 2026 | Viewed by 3798

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Special Issue Editors

Dr. Sheng Xu

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Guest Editor

College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Interests: remote sensing image processing; computer vision; 3D reconstruction; deep learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Qiaolin Ye

E-Mail Website1 Website2
Guest Editor

College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Interests: pattern recognition; machine learning; remote sensing image processing; forest management
Special Issues, Collections and Topics in MDPI journals

Dr. Yu Shen

E-Mail Website
Guest Editor

1. College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
2. College of Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China
Interests: remote sensing image processing; computer vision

Special Issue Information

Dear Colleagues,

The rapid growth of satellite, aerial, and UAV remote sensing technologies has produced massive volumes of multi-source and high-resolution imagery. Effectively interpreting these data is crucial for understanding environmental dynamics, supporting sustainable development, and responding to natural disasters. In recent years, deep learning, together with the emergence of foundation models, large pre-trained architectures, and vision language models, has revolutionized remote sensing image processing by enabling more powerful data-driven feature extraction, semantic understanding, and automated interpretation. These advances are reshaping applications across environmental monitoring, urban analysis, agriculture, forestry, and climate research, while driving a new era of intelligent and scalable geospatial analytics.

This Special Issue aims to showcase innovative research and emerging methodologies in deep learning-based remote sensing image interpretation and processing, including recent developments related to foundation models, pre-training models, vision language model, multi-modal learning, and other advanced AI architectures. We invite contributions that bridge theory and practice, enhance model robustness and interpretability, and advance intelligent systems for geospatial data understanding. The topics align closely with the journal’s scope, emphasizing interdisciplinary research combining remote sensing, computer vision, artificial intelligence, and environmental science.

Suggested themes and article types for submission.

Image classification, detection, and segmentation;
Forest fire, smoke, and disaster detection;
Forestry pest and disease detection;
Change detection and spatiotemporal analysis;
Object extraction and scene understanding;
Hyperspectral, multispectral, SAR, and LiDAR data analysis;
Foundation models, pre-training models, vision language models, and other advanced AI architectures;
Self-supervised, transfer, and federated learning methods;
Benchmark datasets and evaluation protocols.

Dr. Sheng Xu
Prof. Dr. Qiaolin Ye
Dr. Yu Shen
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 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.

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Published Papers (5 papers)

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Research

23 pages, 6046 KB

Open AccessArticle

DDS-DeeplabV3+: A Lightweight Deformable Convolutional Network for Cloud Detection in Remote Sensing Imagery

by Jiafeng Wang, Min Wang, Qixiang Liao, Huaihai Guo, Hanfei Xie, Yun Jiang and Qiang Huang

Remote Sens. 2026, 18(4), 621; https://doi.org/10.3390/rs18040621 - 16 Feb 2026

Viewed by 519

Abstract

Cloud detection in remote sensing imagery is a research hotspot in the field of image processing, and accurately detecting and segmenting cloud regions is crucial for improving the utilization efficiency of remote sensing data. However, standard convolutional neural networks face limitations in modeling the complex spatial structures of clouds. To address these challenges, this paper proposes a cloud detection method based on DDS-DeeplabV3+. First, a lightweight design of the Xception network is adopted to control model complexity, and part of its standard convolutional layers are replaced with Deformable Convolutional Networks (DCN), which enhances the capability of the model to capture geometric features of irregular cloud formations. Second, a Dual-Branch Collaborative Mechanism (DCM) that integrates global context modeling with local detail perception is designed to reconstruct the Atrous Spatial Pyramid Pooling (ASPP) module, thereby improving performance in handling complex scenes and fine boundary delineation. Finally, the SimAM (Simple, Parameter-Free Attention Module) is incorporated into the decoder module, enhancing thin cloud detection capability. Experimental results on the Landsat-8 and GF-1 datasets show that the proposed model achieves Mean Intersection over Union (MIoU) values of 92.61% and 94.04%, respectively, outperforming other comparative methods and demonstrating its superior performance in cloud detection tasks. Full article

(This article belongs to the Special Issue Deep Learning-Based Interpretation and Processing of Remote Sensing Images)

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24 pages, 30825 KB

Open AccessArticle

MA-Net: Multi-Granularity Attention Network for Fine-Grained Classification of Ship Targets in Remote Sensing Images

by Jiamin Qi, Peifeng Li, Guangyao Zhou, Ben Niu, Feng Wang, Qiantong Wang, Yuxin Hu and Xiantai Xiang

Remote Sens. 2026, 18(3), 462; https://doi.org/10.3390/rs18030462 - 1 Feb 2026

Viewed by 571

Abstract

The classification of ship targets in remote sensing images holds significant application value in fields such as marine monitoring and national defence. Although existing research has yielded considerable achievements in ship classification, current methods struggle to distinguish highly similar ship categories for fine-grained classification tasks due to a lack of targeted design. Specifically, they exhibit the following shortcomings: limited ability to extract locally discriminative features; inadequate fusion of features at high and low levels of representation granularity; and sensitivity of model performance to background noise. To address this issue, this paper proposes a fine-grained classification framework for ship targets in remote sensing images based on Multi-Granularity Attention Network (MA-Net), specifically designed to tackle the aforementioned three major challenges encountered in fine-grained classification tasks for ship targets in remote sensing. This framework first performs multi-level feature extraction through a backbone network, subsequently introducing an Adaptive Local Feature Attention (ALFA) module. This module employs dynamic overlapping region segmentation techniques to assist the network in learning spatial structural combinations, thereby optimising the representation of local features. Secondly, a Dynamic Multi-Granularity Feature Fusion (DMGFF) module is designed to dynamically fuse feature maps of varying representational granularities and select key attribute features. Finally, a Feature-Based Data Augmentation (FBDA) method is developed to effectively highlight target detail features, thereby enhancing feature expression capabilities. On the public FGSC-23 and FGSCR-42 datasets, MA-Net attains top-performing accuracies of 93.12% and 98.40%, surpassing the previous best methods and establishing a new state of the art for fine-grained classification of ship targets in remote sensing images. Full article

(This article belongs to the Special Issue Deep Learning-Based Interpretation and Processing of Remote Sensing Images)

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30 pages, 95448 KB

Open AccessArticle

DOMino-YOLO: A Deformable Occlusion-Aware Framework for Vehicle Detection in Aerial Imagery

by Tianyi Fu, Hongbin Dong, Benyi Yang and Baosong Deng

Remote Sens. 2026, 18(1), 66; https://doi.org/10.3390/rs18010066 - 25 Dec 2025

Cited by 1 | Viewed by 880

Abstract

Occlusion-aware vehicle detection in UAV imagery is challenging due to partial visibility from varied viewpoints, dense scenes, and limited features. To address this, we introduce two contributions. First, VOD-UAV, the first UAV-based vehicle detection dataset focused on occlusion, containing 712 synthetic and 1219 real-world images, each annotated with five discrete occlusion levels. These fine-grained labels enable structured supervision and detailed analysis under varying visibility conditions. Second, DOMino-YOLO, a YOLOv11-based detection framework, enhances occlusion robustness via three components: the Deformable Convolution Enhanced Module (DCEM) for spatial alignment, the Visibility-Aware Structural Aggregation (VASA) module for multi-scale feature extraction from partially visible regions, and the Context-Suppressed Implicit Modulation Head (CSIM-Head) for reducing false activations by adaptive channel reweighting. An Occlusion-Aware Repulsion Loss (OAR-Loss) combines Repulsion Loss and Visibility-Weighted Classification Loss to suppress redundant predictions and emphasize heavily occluded objects. Extensive experiments on VOD-UAV demonstrate that DOMino-YOLO significantly improves detection accuracy and robustness under occlusion. The dataset and code will publicly available to support future research. Full article

(This article belongs to the Special Issue Deep Learning-Based Interpretation and Processing of Remote Sensing Images)

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23 pages, 7039 KB

Open AccessArticle

Background Suppression by Multivariate Gaussian Denoising Diffusion Model for Hyperspectral Target Detection

by Weile Han, Yuteng Huang, Jiaqi Feng, Rongting Zhang and Guangyun Zhang

Remote Sens. 2026, 18(1), 64; https://doi.org/10.3390/rs18010064 - 25 Dec 2025

Viewed by 584

Abstract

Hyperspectral image (HSI) target detection plays a critical role in both military and civilian applications, including military reconnaissance, environmental monitoring, and precision agriculture. However, the complex background of the scene severely restricts the further improvement of hyperspectral target detection performance. To address this challenge, we propose a diffusion model hyperspectral target detection method based on multivariate Gaussian background noise. The method constructs multivariate Gaussian-distributed background noise samples and introduces them into the forward diffusion process of the diffusion model. Subsequently, the denoising network is trained, the conditional probability distribution is parameterised, and a designed loss function is used to optimise the denoising performance and achieve effective suppression of the background, thus improving the detection performance. Moreover, in order to obtain accurate background noise, we propose a background noise extraction strategy based on spatial–spectral centre weighting. This strategy combines with the superpixel segmentation technique to effectively fuse the local spatial neighbourhood information of HSI. Experiments conducted on four publicly available HSI datasets demonstrate that the proposed method achieves state-of-the-art background suppression and competitive detection performance. The evaluation using ROC curves and AUC-family metrics demonstrates the effectiveness of the proposed background-suppression-guided diffusion framework. Full article

(This article belongs to the Special Issue Deep Learning-Based Interpretation and Processing of Remote Sensing Images)

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24 pages, 5637 KB

Open AccessArticle

RSSRGAN: A Residual Separable Generative Adversarial Network for Remote Sensing Image Super-Resolution Reconstruction

by Xiangyu Fu, Dongyang Wu and Shanshan Xu

Remote Sens. 2026, 18(1), 44; https://doi.org/10.3390/rs18010044 - 23 Dec 2025

Cited by 1 | Viewed by 782

Abstract

With the advancement of remote sensing technology, high-resolution images are widely used in the field of computer vision. However, image quality is often degraded due to hardware limitations and environmental interference. This paper proposes a Residual Separable Super-Resolution Reconstruction Generative Adversarial Network (RSSRGAN) for remote sensing image super-resolution. The model aims to enhance the resolution and edge information of low-resolution images without hardware improvements. The main contributions include (1) designing an optimized generator network by improving the residual dense network and introducing depthwise separable convolutions to remove BN layers, thereby increasing training efficiency—two PatchGAN discriminators are designed to enhance multi-scale detail capture—and (2) introducing content loss and joint perceptual loss on top of adversarial loss to improve global feature representation. Experimental results show that compared to the widely used SRGAN model in remote sensing (exemplified by the satellite-specific SRGAN in this study), this model improves PSNR by approximately 18.8%, SSIM by 8.0%, reduces MSE by 3.6%, and enhances the PI metric by 13.6%. It effectively enhances object information, color, and brightness in images, making it more suitable for remote sensing image super-resolution. Full article

(This article belongs to the Special Issue Deep Learning-Based Interpretation and Processing of Remote Sensing Images)

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