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Advanced Image Processing Algorithms for Object Detection and Tracking in Aerial and Satellite Imagery

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

Deadline for manuscript submissions: 12 February 2026 | Viewed by 2091

Special Issue Editors

Prof. Dr. Jun Zhou

E-Mail Website
Guest Editor
School of Information and Communication Technology, Griffith University, Nathan, QLD 4111, Australia
Interests: hyperspectral imaging; computer vision; pattern recognition and their applications to remote sensing; agriculture; environment; medicine
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Libao Zhang

E-Mail Website
Guest Editor
School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
Interests: remote sensing image processing; deep learning for remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Junpeng Zhang

E-Mail Website
Guest Editor
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an, China
Interests: satellite video object detection; remote sensing object detection
Dr. Jue Zhang

E-Mail Website
Guest Editor
Institute of Integrated and Intelligent Systems, Griffith University, Nathan, QLD, Australia
Interests: remote sensing image processing; weakly supervised learning; deep learning

Special Issue Information

Dear Colleagues,

The availability of aerial and satellite imagery has revolutionized numerous fields, including environmental monitoring, urban planning, and disaster management. Central to harnessing the full potential of this imagery are advanced image processing algorithms that facilitate accurate object detection and tracking. These algorithms enable the precise identification and analysis of features of geospatial objects within diverse and complex landscapes.

This Special Issue aims to collate cutting-edge research and developments in image processing techniques focusing on object detection and tracking for aerial and satellite imagery. We invite contributions that address the unique challenges posed by these data sources, including varying resolutions, perspectives, and environmental conditions. Our goal is to showcase innovative methodologies that enhance the accuracy, efficiency, and applicability of object detection and tracking in remote sensing.

  1. Deep Learning-based Object Detection in Aerial and Satellite Imagery;
  2. Advanced Algorithms and Techniques for Object Tracking in Hyperspectral Videos;
  3. Cross-modal Remote Sensing Object Detection and Tracking;
  4. The Applications of Remote Sensing Object Detection in Urban Planning, Disaster Management, Environmental Monitoring, and Smart Farming.

Prof. Dr. Jun Zhou
Prof. Dr. Libao Zhang
Dr. Junpeng Zhang
Dr. Jue Zhang
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

  • remote sensing
  • deep learning
  • object detection
  • object tracking
  • cross-modal representation learning
  • hyperspectral videos

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Published Papers (1 paper)

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Research

39 pages, 13725 KB  
Article
SRTSOD-YOLO: Stronger Real-Time Small Object Detection Algorithm Based on Improved YOLO11 for UAV Imageries
by Zechao Xu, Huaici Zhao, Pengfei Liu, Liyong Wang, Guilong Zhang and Yuan Chai
Remote Sens. 2025, 17(20), 3414; https://doi.org/10.3390/rs17203414 - 12 Oct 2025
Viewed by 1280
Abstract
To address the challenges of small target detection in UAV aerial images—such as difficulty in feature extraction, complex background interference, high miss rates, and stringent real-time requirements—this paper proposes an innovative model series named SRTSOD-YOLO, based on YOLO11. The backbone network incorporates a [...] Read more.
To address the challenges of small target detection in UAV aerial images—such as difficulty in feature extraction, complex background interference, high miss rates, and stringent real-time requirements—this paper proposes an innovative model series named SRTSOD-YOLO, based on YOLO11. The backbone network incorporates a Multi-scale Feature Complementary Aggregation Module (MFCAM), designed to mitigate the loss of small target information as network depth increases. By integrating channel and spatial attention mechanisms with multi-scale convolutional feature extraction, MFCAM effectively locates small objects in the image. Furthermore, we introduce a novel neck architecture termed Gated Activation Convolutional Fusion Pyramid Network (GAC-FPN). This module enhances multi-scale feature fusion by emphasizing salient features while suppressing irrelevant background information. GAC-FPN employs three key strategies: adding a detection head with a small receptive field while removing the original largest one, leveraging large-scale features more effectively, and incorporating gated activation convolutional modules. To tackle the issue of positive-negative sample imbalance, we replace the conventional binary cross-entropy loss with an adaptive threshold focal loss in the detection head, accelerating network convergence. Additionally, to accommodate diverse application scenarios, we develop multiple versions of SRTSOD-YOLO by adjusting the width and depth of the network modules: a nano version (SRTSOD-YOLO-n), small (SRTSOD-YOLO-s), medium (SRTSOD-YOLO-m), and large (SRTSOD-YOLO-l). Experimental results on the VisDrone2019 and UAVDT datasets demonstrate that SRTSOD-YOLO-n improves the mAP@0.5 by 3.1% and 1.2% compared to YOLO11n, while SRTSOD-YOLO-l achieves gains of 7.9% and 3.3% over YOLO11l, respectively. Compared to other state-of-the-art methods, SRTSOD-YOLO-l attains the highest detection accuracy while maintaining real-time performance, underscoring the superiority of the proposed approach. Full article
(This article belongs to the Special Issue Advanced Image Processing Algorithms for Object Detection and Tracking in Aerial and Satellite Imagery)
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