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Remote Sensing
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Multisource Data Fusion and Reasoning in Remote Sensing: From Perception...
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Multisource Data Fusion and Reasoning in Remote Sensing: From Perception to Decision Making

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2186

Editors

Prof. Dr. Xiangtao Zheng

E-Mail Website
Guest Editor
College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
Interests: multi-source image interpretation; cross-domain scene classification
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yaxiong Chen

E-Mail Website
Guest Editor
School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China
Interests: pattern recognition; machine learning; hyperspectral image analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Hailong Ning

E-Mail Website
Guest Editor
School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
Interests: multi-modal learning; remote sensing interpretation

Special Issue Information

Dear Colleagues,

With the rapid advancement of remote sensing technology, multi-source data acquisition has become increasingly prevalent, including imagery from diverse sensors such as optical, SAR, infrared, hyperspectral, and LiDAR. These multi-modal data provide complementary insights yet exhibit significant variations in geometric, radiometric, and physical properties. Effectively integrating such diverse information is essential to achieving a comprehensive, accurate, and robust interpretation of complex scenes and dynamic processes.

Multi-source data fusion and reasoning hold great potential to bridge low-level perceptual tasks with high-level semantic understanding and structured decision support. Such capabilities are critical for a wide range of applications, including urban planning and smart city management, disaster emergency response and recovery, environmental and climate change monitoring, agricultural resource allocation, and ecosystem conservation. However, substantial challenges remain in achieving seamless cross-modal integration, such as reconciling cross-modal semantic gaps, modeling spatial and structural relationships, and establishing reliable reasoning mechanisms under high heterogeneity.

This Special Issue aims to present original research and review articles that address these challenges and present novel methodologies, scalable frameworks, and practical applications in multi-source remote sensing data fusion and reasoning. We welcome contributions that explore advanced topics including, but not limited to, the following:

  • Cross-platform (satellite, drone, ground) semantic alignment and knowledge transfer;
  • Joint modeling and reasoning of heterogeneous modes such as optics, SAR, and infrared;
  • Reasoning mechanisms for multi-temporal, multi-scale, and multi-angle remote sensing scenes;
  • Scene semantic relationship reasoning and complex event understanding;
  • Causal inference and context awareness for remote sensing tasks;
  • Migration and adaptation of LLM/MLLM in remote sensing;
  • Large-scale cross-modal datasets and annotation systems for remote sensing reasoning;
  • Integrated perception and intelligent reasoning of air, space, and ground.

Dr. Xiangtao Zheng
Prof. Dr. Yaxiong Chen
Dr. Hailong Ning
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

  • remote sensing multi-modal learning
  • vision–language models
  • multi-modal reasoning
  • cross-modal alignment
  • multi-source fusion
  • reinforcement learning
  • remote sensing agent systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

33 pages, 32613 KB  
Article
A Hybrid Prior-Based Framework for Infrared Image Enhancement Towards Reliable Scene Interpretation
by Jie Li, Cheng Wang, Xiangyu Li, Xiuqin Su, Meilin Xie, Min Guo and Xubin Feng
Remote Sens. 2026, 18(15), 2507; https://doi.org/10.3390/rs18152507 - 1 Aug 2026
Viewed by 191
Abstract
Infrared imaging has unique advantages in remote sensing observation and non-contact measurement, but its inherent low contrast and blurred structural details limit the reliability of scene interpretation by human observers. Unlike deep learning-based approaches that rely on data-driven training and substantial computational resources, [...] Read more.
Infrared imaging has unique advantages in remote sensing observation and non-contact measurement, but its inherent low contrast and blurred structural details limit the reliability of scene interpretation by human observers. Unlike deep learning-based approaches that rely on data-driven training and substantial computational resources, we propose a Hybrid Prior Enhanced Decomposition (HPED) model, a training-free, model-driven framework that incorporates structural and luminance priors into a multi-stage enhancement pipeline. An l1–l0-regularized decomposition separates the input into a base layer that preserves global structures and salient edges and a detail layer in which low-amplitude fluctuations and noise are suppressed. A prior-preserving bi-gamma correction method enhances base-layer contrast through prior-guided histogram segmentation and adaptive gray-level redistribution. An improved grayscale mapping strategy further enhances global contrast while maintaining interframe consistency. Experiments on real SWIR, MWIR, and LWIR images show that HPED ranks first among evaluated traditional and deep learning-based methods on key perceptual quality metrics (SSIM, VIF, LIF), while achieving over 25 fps on a CPU-only platform, sufficient for smooth real-time visual display. Task-oriented evaluation further shows that the HPED improves CNR and SCR by 174.7 ± 11.4% and 298.5 ± 52.1% on average over the raw input, outperforming all competing methods and suggesting potential applicability in downstream machine perception tasks such as detection and tracking. Full article
(This article belongs to the Special Issue Multisource Data Fusion and Reasoning in Remote Sensing: From Perception to Decision Making)
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24 pages, 4842 KB  
Article
Beyond Spatial Domain: Multi-View Geo-Localization with Frequency-Based Positive-Incentive Information Screening
by Bangyong Sun, Mian Li, Bo Sun, Ganchao Liu, Cheng Bi, Weifeng Wang, Xiangpeng Feng, Geng Zhang and Bingliang Hu
Remote Sens. 2026, 18(1), 88; https://doi.org/10.3390/rs18010088 - 26 Dec 2025
Cited by 1 | Viewed by 1171
Abstract
The substantial domain discrepancy inherent in multi-source and multi-view imagery presents formidable challenges to achieving precise drone-based multi-view geo-localization. Existing methodologies primarily focus on designing sophisticated backbone architectures to extract view-invariant representations within abstract feature spaces, yet they often overlook the rich and [...] Read more.
The substantial domain discrepancy inherent in multi-source and multi-view imagery presents formidable challenges to achieving precise drone-based multi-view geo-localization. Existing methodologies primarily focus on designing sophisticated backbone architectures to extract view-invariant representations within abstract feature spaces, yet they often overlook the rich and discriminative frequency-domain cues embedded in multi-view data. Inspired by the principles of π-Noise theory, this paper proposes a frequency-domain Positive-Incentive Information Screening (PIIS) mechanism that adaptively identifies and preserves task-relevant frequency components based on entropy-guided information metrics. This principled approach selectively enhances discriminative spectral signatures while suppressing redundant or noisy components, thereby improving multi-view feature alignment under substantial appearance and geometric variations. The proposed PIIS strategy demonstrates strong architectural generality, as it can be seamlessly integrated into various backbone networks including convolutional-based and Transformer-based architectures while maintaining consistent performance improvements across different models. Extensive evaluations on the University-1652 and SUES-200 datasets have validated the great potential of the proposed method. Specifically, the PIIS-N model achieves a Recall@1 of 94.56% and a mean Average Precision (mAP) of 95.44% on the University-1652 dataset, exhibiting competitive accuracy among contemporary approaches. These findings underscore the considerable promise of frequency-domain analysis in advancing multi-view geo-localization. Full article
(This article belongs to the Special Issue Multisource Data Fusion and Reasoning in Remote Sensing: From Perception to Decision Making)
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