Intelligent Processing of Multimodal Remote Sensing Data
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: 30 November 2025 | Viewed by 4
Special Issue Editors
Interests: multimodal remote sensing; multi/hyper-spectral and lidar data processing; deep learning
Interests: artificial intelligence in machine vision; multiresolution statistical image models; image and video reconstruction, restoration, and analysis
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Special Issue Information
Dear Colleagues,
Earth observation via remote sensing enables accurate characterization and monitoring of objects on the surface from satellite and airborne platforms. Multimodal remote sensing data, acquired by both active (synthetic aperture radar, and LiDAR) and passive (optical, multispectral, hyperspectral, and infrared) sensors, became readily available, providing complementary information regarding the structure, elevation, and material composition of objects in the observed scenes. However, the high dimensionality, heterogeneity, diverse temporal–spatial–spectral resolutions, and intrinsic complexity of these multimodal data pose significant challenges for conventional processing methodologies. Recent breakthroughs in artificial intelligence—particularly transformer networks, graph neural networks, and domain-specific foundation models (e.g., Skysense, Fomo, RingMoE, and EarthGPT)—are revolutionizing the joint exploitation and intelligent processing of multimodal remote sensing data. This paradigm shift enables unprecedented capabilities to fuse complementary information and extract actionable insights.
This Special Issue aims to compile cutting-edge research on AI-driven methods for processing multimodal remote sensing data acquired from diverse sensors and platforms. Topics may cover novel sensor techniques, benchmark datasets, advanced algorithms (including multimodal fusion techniques), and practical applications. By fostering interdisciplinary collaboration at the nexus of AI, geoscience, and remote sensing, this Special Issue strives to accelerate progress in this field, bridging theoretical innovation and practical implementation, thereby advancing scalable, robust, and trustworthy Earth observation solutions in multimodal remote sensing.
We invite original articles, reviews, and perspectives on the following themes:
(1) Advanced deep learning architectures: Novel models for joint processing heterogeneous data.
(2) Multimodal data preprocessing: Innovative techniques for registration, calibration, and noise reduction of heterogeneous data.
(3) Foundation models for remote sensing: Development, fine-tuning, and application of large-scale pre-trained models tailored to multimodal data.
(4) High-level information extraction: AI-driven approaches for semantic segmentation, object detection, scene classification, change detection, and scene understanding using multimodal data.
(5) Spectral point cloud generation: Fusion point clouds and multispectral or hyperspectral imagery for 3D spatial–spectral information generation and analysis.
(6) Benchmark datasets and evaluation: Novel, publicly available multimodal datasets (e.g., combining satellite and UAV data) accompanied by standardized evaluation protocols and baselines.
We look forward to receiving your submissions.
Dr. Xian Li
Prof. Dr. Aleksandra Pizurica
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
- multimodal remote sensing
- intelligent processing
- remote sensing
- multisource remote sensing data processing
- multi/hyperspectral and LiDAR remote sensing
- deep learning
- artificial intelligence in machine vision
- multimodal feature fusion
- heterogeneous feature fusion
- space-intelligent information processing
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