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Remote Sensing
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Object Detection and Information Extraction Based on Remote Sensing Imagery...
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Object Detection and Information Extraction Based on Remote Sensing Imagery (Second Edition)

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 August 2025 | Viewed by 6038

Special Issue Editors

Prof. Dr. Jie Feng

E-Mail Website
Guest Editor
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an 710071, China
Interests: deep learning; object detection and tracking; reinforcement learning; hyperspectral image processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gui-Song Xia

E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: mathematical models for visual information; graph matching problem and its applications; computer vision and machine learning; large-scale 3D reconstruction of visual scenes; information processing, fusion, and scene understanding in unmanned intelligent systems; interpretation and information mining of remote sensing images
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiangrong Zhang

E-Mail Website
Guest Editor
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an 710071, China
Interests: remote sensing image processing; hyperspectral remote sensing; deep learning in remote sensing; change detection in remote sensing; remote sensing applications in urban planning; geospatial data analysis and modeling; SAR remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gong Cheng

grade E-Mail Website1 Website2
Guest Editor
School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: computer vision; pattern recognition; image processing; machine learning; deep learning; object detection and tracking; video analysis; remote sensing applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lichao Mou

E-Mail Website
Guest Editor
1. International AI Future Lab on AI4EO, TUM, Munich, Germany
2. Visual Learning and Reasoning Team, Department EO Data Science, DLR-IMF, Oberpfaffenhofen, Germany
Interests: natural language and earth observation; UAV video understanding; 3D structure inference from monocular optical/SAR imagery; recognition in remote sensing imagery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are launching the second Special Issue of Remote Sensing to be released under the title “Object Detection and Information Extraction Based on Remote Sensing Imagery”.

Remote sensing technology has become a fundamental means by which humans might observe the Earth, and has driven progress in many applicative fields, such as environmental surveillance, disaster monitoring, ocean situational awareness, traffic management, and modern military, etc. However, the intelligent interpretation of remote sensing data poses unique challenges due to limited imaging capabilities, extremely high annotation costs, and insufficient multimodal data fusion. In recent years, deep learning techniques, represented by convolutional neural networks (CNNs) and transformers, have shown remarkable success in computer vision tasks due to their powerful feature extraction and representation capabilities. However, their application in remote sensing imagery is still relatively limited. In this Special Issue, we aim to compile state-of-the-art research pertaining to the application of machine learning methods for object detection and information extraction based on remote sensing imagery.

This Special Issue aims to present the latest advancements and emerging trends in the field of object detection and information extraction in remote sensing imagery. Specifically, the topics of interest include, but are not limited, to the following suggested themes:

  • Object detection and tracking in remote sensing images/videos;
  • Scene recognition, road extraction, and semantic segmentation;
  • Anomaly detection and quality evaluation of remote sensing data;
  • Multimodal remote sensing information extraction and fusion;
  • Few/zero-shot learning in remote sensing data.

Prof. Dr. Jie Feng
Prof. Dr. Gui-Song Xia
Prof. Dr. Xiangrong Zhang
Prof. Dr. Gong Cheng
Prof. Dr. Lichao Mou
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

  • object detection of remote sensing images
  • object detection and tracking of remote sensing videos
  • few/zero-shot learning
  • multi-source data fusion
  • weakly supervised learning
  • semantic segmentation
  • remote sensing image classification

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

Related Special Issue

Published Papers (4 papers)

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Research

23 pages, 21858 KiB  
Article
High-Order Temporal Context-Aware Aerial Tracking with Heterogeneous Visual Experts
by Shichao Zhou, Xiangpan Fan, Zhuowei Wang, Wenzheng Wang and Yunpu Zhang
Remote Sens. 2025, 17(13), 2237; https://doi.org/10.3390/rs17132237 - 29 Jun 2025
Abstract
Visual tracking from the unmanned aerial vehicle (UAV) perspective has been at the core of many low-altitude remote sensing applications. Most of the aerial trackers follow “tracking-by-detection” paradigms or their temporal-context-embedded variants, where the only visual appearance cue is encompassed for representation learning [...] Read more.
Visual tracking from the unmanned aerial vehicle (UAV) perspective has been at the core of many low-altitude remote sensing applications. Most of the aerial trackers follow “tracking-by-detection” paradigms or their temporal-context-embedded variants, where the only visual appearance cue is encompassed for representation learning and estimating the spatial likelihood of the target. However, the variation of the target appearance among consecutive frames is inherently unpredictable, which degrades the robustness of the temporal context-aware representation. To address this concern, we advocate extra visual motion exhibiting predictable temporal continuity for complete temporal context-aware representation and introduce a dual-stream tracker involving explicit heterogeneous visual tracking experts. Our technical contributions involve three-folds: (1) high-order temporal context-aware representation integrates motion and appearance cues over a temporal context queue, (2) bidirectional cross-domain refinement enhances feature representation through cross-attention based mutual guidance, and (3) consistent decision-making allows for anti-drifting localization via dynamic gating and failure-aware recovery. Extensive experiments on four UAV benchmarks (UAV123, UAV123@10fps, UAV20L, and DTB70) illustrate that our method outperforms existing aerial trackers in terms of success rate and precision, particularly in occlusion and fast motion scenarios. Such superior tracking stability highlights its potential for real-world UAV applications. Full article
(This article belongs to the Special Issue Object Detection and Information Extraction Based on Remote Sensing Imagery (Second Edition))
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22 pages, 21162 KiB  
Article
SEMA-YOLO: Lightweight Small Object Detection in Remote Sensing Image via Shallow-Layer Enhancement and Multi-Scale Adaptation
by Zhenchuan Wu, Hang Zhen, Xiaoxinxi Zhang, Xuechen Bai and Xinghua Li
Remote Sens. 2025, 17(11), 1917; https://doi.org/10.3390/rs17111917 - 31 May 2025
Viewed by 934
Abstract
Small object detection remains a challenge in the remote sensing field due to feature loss during downsampling and interference from complex backgrounds. A novel network, termed SEMA-YOLO, is proposed in this paper as an enhanced YOLOv11-based framework incorporating three technical advancements. By fundamentally [...] Read more.
Small object detection remains a challenge in the remote sensing field due to feature loss during downsampling and interference from complex backgrounds. A novel network, termed SEMA-YOLO, is proposed in this paper as an enhanced YOLOv11-based framework incorporating three technical advancements. By fundamentally reducing information loss and incorporating a cross-scale feature fusion mechanism, the proposed framework significantly enhances small object detection performance. First, the Shallow Layer Enhancement (SLE) strategy reduces backbone depth and introduces small-object detection heads, thereby increasing feature map size and improving small object detection performance. Then, the Global Context Pooling-enhanced Adaptively Spatial Feature Fusion (GCP-ASFF) architecture is designed to optimize cross-scale feature interaction across four detection heads. Finally, the RFA-C3k2 module, which integrates Receptive Field Adaptation (RFA) with the C3k2 structure, is introduced to achieve more refined feature extraction. SEMA-YOLO demonstrates significant advantages in complex urban environments and dense target areas, while its generalization capability meets the detection requirements across diverse scenarios. The experimental results show that SEMA-YOLO achieves mAP50 scores of 72.5% on the RS-STOD dataset and 61.5% on the AI-TOD dataset, surpassing state-of-the-art models. Full article
(This article belongs to the Special Issue Object Detection and Information Extraction Based on Remote Sensing Imagery (Second Edition))
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19 pages, 21587 KiB  
Article
LocaLock: Enhancing Multi-Object Tracking in Satellite Videos via Local Feature Matching
by Lingyu Kong, Zhiyuan Yan, Hanru Shi, Ting Zhang and Lei Wang
Remote Sens. 2025, 17(3), 371; https://doi.org/10.3390/rs17030371 - 22 Jan 2025
Cited by 2 | Viewed by 1138
Abstract
Multi-object tracking (MOT) in satellite videos is a challenging task due to the small size and blurry features of objects, which often lead to intermittent detection and tracking instability. Many existing object detection and tracking models often struggle with these issues, as they [...] Read more.
Multi-object tracking (MOT) in satellite videos is a challenging task due to the small size and blurry features of objects, which often lead to intermittent detection and tracking instability. Many existing object detection and tracking models often struggle with these issues, as they are not designed to effectively handle the unique characteristics of satellite videos. To address these challenges, we propose LocaLock, a joint detection and tracking framework for MOT that incorporates feature matching concepts from single object tracking (SOT) to enhance tracking stability and reduce intermittent tracking results. Specifically, LocaLock utilizes an anchor-free detection backbone for efficiency and employs a local cost volume (LCV) module to perform precise feature matching in the local area. This provides valuable object priors to the detection head, enabling the model to “lock” onto objects with greater accuracy and mitigate the instability associated with small object detection. Additionally, the local computation within the LCV module ensures low computational complexity and memory usage. Furthermore, LocaLock incorporates a novel motion flow (MoF) module to accumulate and exploit temporal information, further enhancing feature robustness and consistency across frames. Rigorous evaluations on the VISO dataset demonstrate the superior performance of LocaLock, surpassing existing methods in tracking accuracy and precision within the demanding satellite video analysis domain. Notably, LocaLock achieved state-of-the-art performance on the VISO benchmark, achieving a multi-object tracking accuracy (MOTA) of 62.6 while ensuring fast running speed. Full article
(This article belongs to the Special Issue Object Detection and Information Extraction Based on Remote Sensing Imagery (Second Edition))
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27 pages, 39262 KiB  
Article
Advanced Object Detection in Low-Light Conditions: Enhancements to YOLOv7 Framework
by Dewei Zhao, Faming Shao, Sheng Zhang, Li Yang, Heng Zhang, Shaodong Liu and Qiang Liu
Remote Sens. 2024, 16(23), 4493; https://doi.org/10.3390/rs16234493 - 29 Nov 2024
Cited by 5 | Viewed by 3053
Abstract
Object detection in low-light conditions is increasingly relevant across various applications, presenting a challenge for improving accuracy. This study employs the popular YOLOv7 framework and examines low-light image characteristics, implementing performance enhancement strategies tailored to these conditions. We integrate an agile hybrid convolutional [...] Read more.
Object detection in low-light conditions is increasingly relevant across various applications, presenting a challenge for improving accuracy. This study employs the popular YOLOv7 framework and examines low-light image characteristics, implementing performance enhancement strategies tailored to these conditions. We integrate an agile hybrid convolutional module to enhance edge information extraction, improving detailed discernment in low-light scenes. Convolutional attention and deformable convolutional modules are added to extract rich semantic information. Cross-layer connection structures are established to reinforce critical information, enhancing feature representation. We use brightness-adjusted data augmentation and a novel bounding box loss function to improve detection performance. Evaluations on the ExDark dataset show that our method achieved an mAP50 of 80.1% and an mAP50:95 of 52.3%, improving by 8.6% and 11.5% over the baseline model, respectively. These results validate the effectiveness of our approach for low-light object detection. Full article
(This article belongs to the Special Issue Object Detection and Information Extraction Based on Remote Sensing Imagery (Second Edition))
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