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Target Detection, Recognition, Tracking, and Positioning Using Remote Sensing and AI Techniques (Second Edition)

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

Deadline for manuscript submissions: 28 September 2026 | Viewed by 1960

Editors

Dr. Wensheng Wang

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
Interests: target detection; target tracking; AI integration; multi-agent system; task-driven optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Martin Gade

E-Mail Website
Guest Editor
Institut für Meereskunde, Universität Hamburg, Bundesstraße 53, 20146 Hamburg, Germany
Interests: coastal remote sensing; SAR; marine surface films; ocean radar backscattering; air-sea interactions; air-sea fluxes
Special Issues, Collections and Topics in MDPI journals
Dr. Yi Wan

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: cross view geo-localization; satellite photogrammetry; 3D reconstruction
Special Issues, Collections and Topics in MDPI journals
Dr. Zhirui Wang

E-Mail Website
Guest Editor
1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
2. Key Laboratory of Network Information System Technology (NIST), University of Chinese Academy of Sciences, Beijing, China
Interests: multi-source information fusion; AI remote sensing; multi-agent cooperative perception
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of AI-driven target detection in remote sensing has enabled transformative applications, including disaster response, traffic monitoring, and autonomous driving; therefore, following the highly successful first edition of this Special Issue, which attracted strong interest and high-quality contributions, this second edition continues to explore emerging advances in this dynamic field. Traditional approaches based on hand-crafted features often struggle to cope with challenges such as viewpoint variation, multi-modal remote sensing data, and non-cooperative targets; however, recent breakthroughs in deep learning and multi-sensor fusion have led to substantial improvements in detection accuracy and computational efficiency, particularly for small, non-cooperative targets in cluttered scenes. Nevertheless, several key challenges remain: (1) balancing detection and tracking performance with computational efficiency for edge-device deployment; (2) enabling reliable automatic detection and tracking of dynamic targets across diverse environments; (3) integrating satellite–aerial–ground collaborative perception for large-scale, real-time monitoring; and (4) recognizing ambiguous relationships within complex and dynamic target groups.

This Special Issue aligns closely with the scope of Remote Sensing, especially in AI-driven remote sensing applications and multi-source data fusion. It aims to: (1) narrow the gap between advanced AI techniques and practical remote sensing needs; (2) provide a forum for cross-disciplinary research at the intersection of computer vision, robotics, and environmental science; (3) encourage the development of robust and deployable systems for dynamic and heterogeneous environments; and (4) strengthen latent association recognition for complex non-cooperative target groups.

The Special Issue seeks to attract research covering the following themes:

  • Multi-modal/multi-scale remote sensing data fusion;
  • Multi-view collaborative perception;
  • AI-driven environment recognition;
  • Target detection, recognition, tracking, and positioning;
  • Cross-view geolocalization and matching;
  • Target association analysis based on knowledge graphs;
  • Understanding 3D scenes from heterogeneous views.

Dr. Wensheng Wang
Dr. Martin Gade
Dr. Yi Wan
Dr. Zhirui Wang
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-anonymized 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

  • satellite–aerial–ground collaborative perception
  • multi-agent system
  • target detection, recognition, tracking and positioning
  • geographic retrieval, cross-view geolocalization
  • knowledge graph
  • deep learning
  • task-driven optimization

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Related Special Issue

Published Papers (3 papers)

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Research

31 pages, 7456 KB  
Article
MSIA-YOLO: A Multi-Scale Semantic Interaction and Alignment Network for Small Object Detection in Low-Altitude UAV Remote Sensing Images
by Wen Zhang, Xiaorong Xue, Bingyan Lu, Yishuo Tian, Jingtong Yang, Xin Zhao and Wancheng Wang
Remote Sens. 2026, 18(13), 2210; https://doi.org/10.3390/rs18132210 - 5 Jul 2026
Cited by 1 | Viewed by 456
Abstract
Small object detection is fundamentally constrained by the lack of discriminative fine-grained features. Although introducing higher resolution detection scales can improve performance, it also amplifies background noise. In addition, the independently decoupled design of conventional detection heads is insufficient to address the persistent [...] Read more.
Small object detection is fundamentally constrained by the lack of discriminative fine-grained features. Although introducing higher resolution detection scales can improve performance, it also amplifies background noise. In addition, the independently decoupled design of conventional detection heads is insufficient to address the persistent challenges of missed detections and false positives for small objects. To this end, we propose MSIA-YOLO, a YOLOv11-based detector with multi-scale semantic interaction and alignment, optimized from three complementary perspectives: feature modeling, high resolution semantic compensation, and task coordinated alignment. First, Receptive Field Attention Convolution (RFAConv) is integrated into the backbone to enhance critical local details, such as edge and texture cues, via receptive field aware attention. Second, to alleviate fine detail attenuation caused by repeated downsampling, we construct a CHSP-P2 small object detection framework with an additional P2 branch. A scale sequence fusion mechanism is further introduced to perform high resolution semantic compensation through cross scale hybrid inputs. Finally, we design a DTIA-Head (Dynamic Task Interaction and Alignment Head), which promotes joint optimization of classification and localization through dynamic task interaction and spatial alignment. Extensive experiments on the public datasets VisDrone, TinyPerson, and RSOD show that, compared with the YOLOv11n baseline, MSIA-YOLO improves mAP50 by 7.7%, 10.3%, and 1.0%, respectively, while also outperforming several advanced detectors. These results demonstrate the effectiveness and generalization capability of the proposed method in small object, dense object, and complex scene object detection scenarios. Full article
(This article belongs to the Special Issue Target Detection, Recognition, Tracking, and Positioning Using Remote Sensing and AI Techniques (Second Edition))
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22 pages, 25688 KB  
Article
Maritime Distress Target Detection Based on Improved RT-DETR: For Robust Small Target Localization
by Kun Liu, Xinbo Chang, Zhen Liu, Jian Xu, Yuhan Zhang and Yang Liu
Remote Sens. 2026, 18(12), 1908; https://doi.org/10.3390/rs18121908 - 9 Jun 2026
Viewed by 394
Abstract
With the rapid development of maritime transportation and resource development activities, maritime distress events are increasingly frequent, and efficient and accurate target recognition and rescue response methods are urgently needed. The traditional monitoring methods are limited by efficiency and real time, which is [...] Read more.
With the rapid development of maritime transportation and resource development activities, maritime distress events are increasingly frequent, and efficient and accurate target recognition and rescue response methods are urgently needed. The traditional monitoring methods are limited by efficiency and real time, which is difficult to adapt to the complex and changeable marine environment. Therefore, based on the RT-DETR model of transformer architecture, an improved scheme for maritime distress target detection is proposed to improve the small target recognition ability and detection efficiency. Specific improvements include: a small target-focused convolution module (SFConv) is designed to enhance the efficiency of feature extraction and reasoning of small-scale targets; The cross-scale feature interaction optimization module (SPE) is further proposed to improve the ability of multi-scale perception and background suppression; The Focaler-DIoU loss function is introduced to enhance the discrimination performance of the model for difficult samples. On the basis of maintaining the end-to-end detection advantage of RT-DETR, the improvement is of 0.83474, which is 5.7% higher than the original model (0.78964). The accuracy and robustness of the model in complex marine environment is significantly improved, and technical support is provided for the construction of an efficient and intelligent marine monitoring and emergency response system. Full article
(This article belongs to the Special Issue Target Detection, Recognition, Tracking, and Positioning Using Remote Sensing and AI Techniques (Second Edition))
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23 pages, 10725 KB  
Article
Search Region-Guided Adaptive Template Update for Robust Multi-Modal UAV Tracking
by Lei Liu, Qi Li, Jiaxin Lv and Jiaxiang Wang
Remote Sens. 2026, 18(11), 1817; https://doi.org/10.3390/rs18111817 - 2 Jun 2026
Viewed by 504
Abstract
Existing multi-modal UAV tracking methods typically rely on fixed-interval dynamic template update strategies to capture diverse target appearances, together with predefined thresholds to select high-quality search regions for template update. However, due to the irregular motion of targets and the complexity of real-world [...] Read more.
Existing multi-modal UAV tracking methods typically rely on fixed-interval dynamic template update strategies to capture diverse target appearances, together with predefined thresholds to select high-quality search regions for template update. However, due to the irregular motion of targets and the complexity of real-world scenarios, such passive update mechanisms suffer from notable limitations. Fixed sampling intervals often fail to adequately capture appearance variations, while fixed threshold-based selection is insufficient to accommodate diverse imaging conditions, leading to ineffective updates or the introduction of noisy templates, thereby degrading tracking robustness and accuracy. To address these issues, we propose a search region-guided adaptive dynamic template update framework for robust multi-modal UAV tracking, aiming to improve both scene adaptability and target matching capability. Specifically, we design a Guided Template Selection Transformer, which dynamically matches templates conditioned on the current search region, enabling the tracker to autonomously select the most suitable template for the target’s current state. Furthermore, we introduce a Dynamic Threshold Module that adaptively adjusts template selection criteria according to different tracking scenarios, ensuring the reliability and contextual relevance of candidate templates. In addition, we develop a Dynamic Template Memory Module to maintain an ordered repository of target templates under different target states, providing a structured and high-quality template pool for the proposed selection mechanism. Extensive experiments on a standard multi-modal UAV tracking benchmark demonstrate that the proposed method significantly outperforms existing approaches, effectively overcoming the limitations of conventional fixed update strategies. Moreover, the proposed approach exhibits strong generalization capability across three additional multi-modal tracking datasets from typical surveillance scenarios. Full article
(This article belongs to the Special Issue Target Detection, Recognition, Tracking, and Positioning Using Remote Sensing and AI Techniques (Second Edition))
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