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Remote Sensing
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Radar and Photo-Electronic Multi-Modal Intelligent Fusion
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Radar and Photo-Electronic Multi-Modal Intelligent Fusion

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

Deadline for manuscript submissions: 10 September 2026 | Viewed by 2761

Editors

Prof. Dr. Ganggang Dong

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Guest Editor
National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
Interests: radar imaging; remote sensing image interpretation; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xinhua Mao

E-Mail Website
Guest Editor
Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: radar imaging; target detection; SAR autofocus
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jianhua Shi

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Guest Editor
College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China
Interests: photo-electronics image/signal processing; infrared target detection
Dr. Xiaowei Hu

E-Mail
Guest Editor
Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China
Interests: radar target detection; radar image interpretation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past several decades, significant developments—such as rapid progress in radar and photo-electronic sensing technologies, radar signal processing, and remote sensing image interpretation—have been achieved.  The performance of object detection, for example, has skyrocketed from approximately 30 percent in mean average precision to more than 90 percent when using the PASCAL VOC benchmark. Likewise, the performance for radar target recognition with the MSTAR SAR dataset even achieved beyond 99%. These developments in signal processing and image interpretation influence significant impacts on a wide range of practical applications. The major driving force behind recent advances in radar and photo-electronic sensing technologies lies in the development of deep learning skills. These developments are powered by two crucial factors: large-scale datasets, and powerful platforms. The performance obtained in various fields sharply outperformed the engineering features. Although great successes have been achieved in previous works, there are still numerous challenges in the vertical field of remote sensing, especially in regard to practical applications. For example, it is infeasible to collect large amounts of radar data with label information; therefore, how to achieve better learning efficiency and inference results with limited sensor data remains challenging, in addition to the challenges around how to boost learning effectiveness with many more kinds of modal data. The aim of this Special Issue is to collect new solutions to these vertical fields. Topics of interests include, but are not limited to, the following:

  1. Fusion of multi-source radar data for environment monitoring and assessment, natural disaster warning, and evaluation (floods, landslides, etc.);
  2. Cross-modal data fusion for image interpretation, e.g., optical-SAR data fusion, or infrared and visible image fusion;
  3. Radar imaging driven by intelligence techniques, e.g., SAR imaging and interpretation driven by LLMs;
  4. Radar signal processing, co-driven by models and data;
  5. Artificial intelligence-powered signal processing, e.g., image dehazing, denoising, and retrieval.

The topic of this Special Issue is closely related to the vertical field of remote sensing, such as multi-modal fusion and understanding. The following themes are suggested: radar imaging; remote sensing image interpretation; infrared and visible image fusion; target detection and recognition; AI-powered signal processing.

Prof. Dr. Ganggang Dong
Prof. Dr. Xinhua Mao
Prof. Dr. Jianhua Shi
Dr. Xiaowei Hu
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

  • radar signal processing
  • photo-electronic image understanding
  • target detection and recognition
  • radar imaging

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Published Papers (4 papers)

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Research

19 pages, 15929 KB  
Article
HCA-YOLO: A Hierarchical Cross-Scale Attention Learning Framework for UAV Detection
by Wei Wang, Yan Zhang, Yaxiu Zhang, Lingjun Zhao and Xingwei Yan
Remote Sens. 2026, 18(13), 2196; https://doi.org/10.3390/rs18132196 - 5 Jul 2026
Viewed by 362
Abstract
The accurate detection of unmanned aerial vehicles (UAVs) in various sizes played an important role in the practical applications. Yet the preceding works suffered from the missing inference, the false alarms, and the poor accuracy due to the the adverse scene conditions, as [...] Read more.
The accurate detection of unmanned aerial vehicles (UAVs) in various sizes played an important role in the practical applications. Yet the preceding works suffered from the missing inference, the false alarms, and the poor accuracy due to the the adverse scene conditions, as well as the mutable scales. To solve the problems, a hierarchical attention promoted cross-scale learning framework was proposed in this paper. First, the hierarchical attention mechanism was introduced in the backbone to generate the multi-scale features of targets, so they can be discerned and located at different scales. The resulting features were further delivered to the neck, in which two branches of features were built, respectively. The former was obtained by the target-specific feature operator, while the latter was generated by the upsampling operation. The dual branches were further connected in the quasi-residual structure. So the content of targets can be protected well, and the detail information can be reconstructed. Finally, the dynamic focusing loss measurement was presented to regress the bounding box of the target, so the learning effectiveness of presented the architecture can be promoted. To verify the proposed method, multiple rounds of experiments were performed. The results demonstrated that small and weak drones can be detected accurately, especially in adverse lighting and weather conditions. The evaluation metric of mean average precision rate (mAP) can be improved by 18.5% (YOLO6) on the collected dataset. Full article
(This article belongs to the Special Issue Radar and Photo-Electronic Multi-Modal Intelligent Fusion)
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22 pages, 10948 KB  
Article
Scale-Adaptive Infrared UAV Detection Under Fast Motion and Zooming
by Xingwei Yan, Yan Zhang, Haiyong Chen, Yaxiu Zhang and Kunlin Zou
Remote Sens. 2026, 18(13), 2138; https://doi.org/10.3390/rs18132138 - 2 Jul 2026
Viewed by 383
Abstract
Infrared UAV detection plays a crucial role in both security surveillance and military applications. However, under fast UAV movement or dynamic zooming scenarios, the rapid scale variation of targets poses severe challenges to existing detection models, especially on resource-constrained edge devices. To address [...] Read more.
Infrared UAV detection plays a crucial role in both security surveillance and military applications. However, under fast UAV movement or dynamic zooming scenarios, the rapid scale variation of targets poses severe challenges to existing detection models, especially on resource-constrained edge devices. To address this, a lightweight scale-adaptive multi-scale feature fusion model, termed LMF-IR, is proposed for efficient and accurate detection under sudden target size changes. The model integrates three key components: a Multi-Dilation Residual Block (MDRB) for enhanced multi-scale feature representation, an improved Channel Attention Model–Feature Fusion Pyramid Network (CAM-FPN) to boost adaptive feature fusion, and a modified P-WIoU loss function designed for precise bounding box regression under varying target sizes. The MDRB module effectively captures fine-grained features across multiple scales and reliably identifies targets of varying sizes. The CAM-FPN incorporates a channel attention mechanism, which can dynamically adjust the weights of features, enabling the model to focus on informative feature channels. The redesigned P-WIoU loss function is designed to account for the shape characteristics of UAV target bounding boxes. It includes centroid distance, overlap ratio, and aspect ratio, thereby improving localization accuracy under rapid scale changes. The experimental results on our self-built UAV–infrared dataset show that LMF-IR reduces 1.4 G in floating-point operations compared to the baseline model, and the parameter count is reduced to 62% of the baseline. At the same time, mAP@0.5:0.95 increases by 2.4%. Moreover, on the public ANTI-UAV dataset, our method increases mAP@0.5:0.95 by 4.8%, indicating that our method has excellent performance in real-time infrared UAV detection under rapid target scale changes. Full article
(This article belongs to the Special Issue Radar and Photo-Electronic Multi-Modal Intelligent Fusion)
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32 pages, 8414 KB  
Article
TVLightFormer: A Lightweight Cross-Modal Transformer for Language-Guided Target Localization in SAR Imagery
by Yuqiao Zhong, Haoqi Quan, Chenyu Nie, Yingmei Wei and Yanming Guo
Remote Sens. 2026, 18(9), 1430; https://doi.org/10.3390/rs18091430 - 4 May 2026
Viewed by 434
Abstract
We study language-guided target localization in synthetic aperture radar (SAR) imagery for deployment on resource-constrained platforms. Existing vision-language models either rely on heavy backbones unsuitable for edge devices or are designed for natural images, overlooking SAR-specific characteristics such as speckle noise, weak scattering [...] Read more.
We study language-guided target localization in synthetic aperture radar (SAR) imagery for deployment on resource-constrained platforms. Existing vision-language models either rely on heavy backbones unsuitable for edge devices or are designed for natural images, overlooking SAR-specific characteristics such as speckle noise, weak scattering responses, and geometric distortions. The proposed model, TVLightFormer, combines a lightweight dual-modal encoder (MobileNetV3 and TinyBERT) with a grouped-query attention (GQA) mechanism for efficient cross-modal interaction and an activation-free lightweight feature pyramid network (LFPN) to handle scale variation while preserving weak scattering signals. The individual modules are not claimed as newly invented components; the main contribution lies in their SAR-aware integration for edge-oriented cross-modal localization. We evaluate the model on five remote sensing datasets—SOMA-1M, ATRNet-STAR, GAIA, MLRSNet, and SODAS—under a unified localization setting, and we explicitly discuss the limitations introduced by weak or scene-level annotations. The results show that TVLightFormer achieves a favorable trade-off between accuracy and efficiency, reaching an average mIoU of 69.8% with 27.4 M parameters and 9.7 GFLOPs. Ablation studies quantify the contribution of each component. The model is suited for edge-oriented scenarios where computational resources are limited. We also provide a critical analysis of failure cases, SAR-specific disturbance factors, loss-function choices, and dataset-protocol sensitivity. Full article
(This article belongs to the Special Issue Radar and Photo-Electronic Multi-Modal Intelligent Fusion)
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36 pages, 6057 KB  
Article
SADW-Det: A Lightweight SAR Ship Detection Algorithm with Direction-Weighted Attention and Factorized-Parallel Structure Design
by Mengshan Gui, Hairui Zhu, Weixing Sheng and Renli Zhang
Remote Sens. 2026, 18(4), 582; https://doi.org/10.3390/rs18040582 - 13 Feb 2026
Cited by 1 | Viewed by 878
Abstract
Synthetic Aperture Radar (SAR) is a powerful observation system capable of delivering high-resolution imagery under variable sea conditions to support target detection and tracking, such as for ships. However, conventional optical target detection models are typically engineered for complex optical imagery, leading to [...] Read more.
Synthetic Aperture Radar (SAR) is a powerful observation system capable of delivering high-resolution imagery under variable sea conditions to support target detection and tracking, such as for ships. However, conventional optical target detection models are typically engineered for complex optical imagery, leading to limitations in accuracy and high computational resource consumption when directly applied to SAR imagery. To address this, this paper proposes a lightweight shape-aware and direction-weighted algorithm for SAR ship detection, SADW-Det. First, a lightweight streamlined backbone network, LSFP-NET, is redesigned based on the YOLOX architecture. This achieves reduced parameter counts and computational burden by incorporating depthwise separable convolutions and factorized convolutions. Concurrently, a parallel fusion module is designed, leveraging multiple small-kernel depthwise separable convolutions to extract features in parallel. This approach maintains accuracy while achieving lightweight processing. Furthermore, addressing the differences between SAR imagery and other imaging modalities, a direction-weighted attention was devised. This enhances model performance with minimal computational overhead by incorporating positional information while preserving channel data. Experimental results demonstrate superior detection accuracy compared to existing methods on three representative SAR datasets, SSDD, HRSID and DSSDD, while achieving reduced parameter counts and computational complexity, indicating strong application potential and laying the foundation for cross-modal applications. Full article
(This article belongs to the Special Issue Radar and Photo-Electronic Multi-Modal Intelligent Fusion)
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