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Remote Sensing
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Remote Sensing Target Recognition and Detection: Theory and Applications (Second...
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Remote Sensing Target Recognition and Detection: Theory and Applications (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: 31 March 2026 | Viewed by 750

Special Issue Editors

Dr. Hao Li

E-Mail Website1 Website2
Guest Editor
Key Laboratory of Collaborative Intelligence Systems of Ministry of Education, School of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: computational intelligence; evolutionary computation; neural networks; multi-objective optimization; remote sensing image interpretation
Special Issues, Collections and Topics in MDPI journals
Dr. Mingyang Zhang

E-Mail Website
Guest Editor
Key Laboratory of Collaborative Intelligence Systems of Ministry of Education, School of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: artificial intelligence (in particular, machine learning, multiagent systems and their applications); formal methods (in particular, machine-learning-based model checking)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are launching our second Special Issue of Remote Sensing to be released under the title "Remote Sensing Target Recognition and Detection: Theory and Applications".

Target recognition and detection is a multidisciplinary field that involves a variety of sensors, such as synthetic aperture radar (SAR), inverse synthetic aperture radar (ISAR), side-scan sonars, multispectral/hyperspectral sensors, and others. Target recognition and detection is used to search regions of interest of specific targets in an image and determine the category and location of targets. It usually marks the image, selects the target area of interest in the image with a rectangular box, and finally creates a category label for the image target. As important steps of image processing and further analysis, improvement in target recognition and detection techniques is urgently needed to achieve higher performance in various tasks. Although deep learning has achieved unprecedented success in the field, there are still open application issues that must be comprehensively addressed.

This Special Issue aims to gather papers presenting recent advances in target recognition and detection with novel and impactful applications. Topics of interest include, but are not limited to, the following:

  • Machine learning for target recognition and detection;
  • Theory of multi-objective/multi-task optimization and learning;
  • Change detection and classification in remote sensing;
  • Remote sensing/teaching image object detection, segmentation and categorization;
  • Underwater target recognition and detection;
  • Ocean acoustic remote sensing;
  • Radar high-speed target detection, tracking, imaging and recognition;
  • Computational electromagnetics and scattering measurement theory;
  • Sensor signal detection, identification and categorization.

Research articles, review articles and short communications are welcome for submission.

Dr. Hao Li
Prof. Dr. Mingyang 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

  • target recognition
  • target detection
  • deep learning
  • neural networks
  • image classification
  • image processing

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Published Papers (1 paper)

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Research

46 pages, 5911 KiB  
Article
Leveraging Prior Knowledge in Semi-Supervised Learning for Precise Target Recognition
by Guohao Xie, Zhe Chen, Yaan Li, Mingsong Chen, Feng Chen, Yuxin Zhang, Hongyan Jiang and Hongbing Qiu
Remote Sens. 2025, 17(14), 2338; https://doi.org/10.3390/rs17142338 - 8 Jul 2025
Viewed by 296
Abstract
Underwater acoustic target recognition (UATR) is challenged by complex marine noise, scarce labeled data, and inadequate multi-scale feature extraction in conventional methods. This study proposes DART-MT, a semi-supervised framework that integrates a Dual Attention Parallel Residual Network Transformer with a mean teacher paradigm, [...] Read more.
Underwater acoustic target recognition (UATR) is challenged by complex marine noise, scarce labeled data, and inadequate multi-scale feature extraction in conventional methods. This study proposes DART-MT, a semi-supervised framework that integrates a Dual Attention Parallel Residual Network Transformer with a mean teacher paradigm, enhanced by domain-specific prior knowledge. The architecture employs a Convolutional Block Attention Module (CBAM) for localized feature refinement, a lightweight New Transformer Encoder for global context modeling, and a novel TriFusion Block to synergize spectral–temporal–spatial features through parallel multi-branch fusion, addressing the limitations of single-modality extraction. Leveraging the mean teacher framework, DART-MT optimizes consistency regularization to exploit unlabeled data, effectively mitigating class imbalance and annotation scarcity. Evaluations on the DeepShip and ShipsEar datasets demonstrate state-of-the-art accuracy: with 10% labeled data, DART-MT achieves 96.20% (DeepShip) and 94.86% (ShipsEar), surpassing baseline models by 7.2–9.8% in low-data regimes, while reaching 98.80% (DeepShip) and 98.85% (ShipsEar) with 90% labeled data. Under varying noise conditions (−20 dB to 20 dB), the model maintained a robust performance (F1-score: 92.4–97.1%) with 40% lower variance than its competitors, and ablation studies validated each module’s contribution (TriFusion Block alone improved accuracy by 6.9%). This research advances UATR by (1) resolving multi-scale feature fusion bottlenecks, (2) demonstrating the efficacy of semi-supervised learning in marine acoustics, and (3) providing an open-source implementation for reproducibility. In future work, we will extend cross-domain adaptation to diverse oceanic environments. Full article
(This article belongs to the Special Issue Remote Sensing Target Recognition and Detection: Theory and Applications (Second Edition))
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