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Advances in SAR, Optical, Hyperspectral and Infrared Remote Sensing

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 May 2026 | Viewed by 628

Special Issue Editors


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Guest Editor
Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China
Interests: deep learning; computer vision; remote sensing; SAR; object detection; image processing; intelligent transportation; marine transportation; V2X
Special Issues, Collections and Topics in MDPI journals
School of Computer Science and Informatics, The University of Liverpool, Liverpool L69 7ZN, UK
Interests: synthetic aperture radar (SAR); remote sensing; computer vision

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Guest Editor
School of Computer Science, China University of Geosciences, Wuhan, China
Interests: hyperspectral image classification; hyperspectral image datasets; image processing; machine learning; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Synthetic Aperture Radar (SAR), optical, hyperspectral, and infrared remote sensing serve as the four core technologies of modern Earth observation, providing abundant data support for key fields such as environmental monitoring, precision agriculture, disaster response, and urban governance. Each of the four technologies boasts unique data advantages: SAR enables all-weather imaging, optical data offers high spatial detail expression, hyperspectral data allows fine-grained material identification, and infrared data can capture thermal anomaly information.

In the process of transforming remote sensing data into practical applications, image interpretation is one of the key challenging links. Its core difficulties focus on two aspects: first, the demand for improving the accuracy of perception tasks such as object classification, detection, and segmentation in complex scenarios; second, how to break through the limitations of pixel-level information to achieve semantic understanding and reasoning for real-world scenarios, enabling remote sensing data to better match the decision-making needs in practical applications. The continuous development of advanced artificial intelligence technologies is providing more efficient technical paths to address these interpretation challenges, promoting remote sensing interpretation from coarse-grained sensing to fine-grained sensing, and from sensing only to the integration of sensing + reasoning, moving towards a more accurate and practical direction.

This Special Issue focuses on the innovation and practice of image interpretation technologies for SAR, optical, hyperspectral, and infrared remote sensing data, which is highly aligned with the core positioning of the Remote Sensing journal in the field of remote sensing technology and applications. With the core features of “improved accuracy of perception tasks + in-depth scene semantic reasoning”, this Special Issue specifically builds an interdisciplinary exchange platform focusing on interpretation technologies, promoting targeted integration between remote sensing and artificial intelligence fields, and accelerating the transformation of interpretation technologies from theoretical innovation to practical applications.

Submitted articles should center on SAR/optical/hyperspectral/infrared images, including, but not limited to the following themes:

  • High-precision perception technologies such as object classification, detection, and segmentation in image interpretation;
  • Research on scene semantic understanding and reasoning methods in image interpretation;
  • Robustness improvement of image interpretation models in complex scenarios;
  • Image interpretation technologies driven by multi-modal/large language model technologies;
  • Efficiency optimization of image interpretation models.

Prof. Dr. Tianwen Zhang
Dr. Xiao Ke
Prof. Dr. Shaoguang Huang
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-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

  • SAR/optical/hyperspectral/infrared remote sensing
  • high-precision imaging
  • AI-driven remote sensing interpretation
  • multi-source data fusion
  • parameter inversion
  • earth observation applications

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Published Papers (1 paper)

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Research

34 pages, 20157 KB  
Article
Dual-Level Attention Relearning for Cross-Modality Rotated Object Detection in UAV RGB–Thermal Imagery
by Zhuqiang Li, Zhijun Zhen, Shengbo Chen, Liqiang Zhang and Lisai Cao
Remote Sens. 2026, 18(1), 107; https://doi.org/10.3390/rs18010107 - 28 Dec 2025
Viewed by 401
Abstract
Effectively leveraging multi-source unmanned aerial vehicle (UAV) observations for reliable object recognition is often compromised by environmental extremes (e.g., occlusion and low illumination) and the inherent physical discrepancies between modalities. To overcome these limitations, we propose DLANet, a lightweight, rotation-aware multimodal object detection [...] Read more.
Effectively leveraging multi-source unmanned aerial vehicle (UAV) observations for reliable object recognition is often compromised by environmental extremes (e.g., occlusion and low illumination) and the inherent physical discrepancies between modalities. To overcome these limitations, we propose DLANet, a lightweight, rotation-aware multimodal object detection framework that introduces a dual-level attention relearning strategy to maximize complementary information from visible (RGB) and thermal infrared (TIR) imagery. DLANet integrates two novel components: the Implicit Fine-Grained Fusion Module (IF2M), which facilitates deep cross-modal interaction by jointly modeling channel and spatial dependencies at intermediate stages, and the Adaptive Branch Feature Weighting (ABFW) module, which dynamically recalibrates modality contributions at higher levels to suppress noise and pseudo-targets. This synergistic approach allows the network to relearn feature importance based on real-time scene conditions. To support industrial applications, we construct the OilLeak dataset, a dedicated benchmark for onshore oil-spill detection. The experimental results demonstrate that DLANet achieves state-of-the-art performance, recording an mAP0.5 of 0.858 on the public DroneVehicle dataset while maintaining high efficiency, with 39.04 M parameters and 72.69 GFLOPs, making it suitable for real-time edge deployment. Full article
(This article belongs to the Special Issue Advances in SAR, Optical, Hyperspectral and Infrared Remote Sensing)
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