Artificial Intelligence in Hyperspectral Remote Sensing Data Analysis
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 279
Special Issue Editors
Interests: hyperspectral remote sensing; intelligent remote sensing interpretation
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral remote sensing; environmental remote sensing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral remote sensing; artificial intelligence; aerospace
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hyperspectral remote sensing, which captures hundreds of contiguous narrow spectral bands across the electromagnetic spectrum, has emerged as a cornerstone technology for analyzing the surface with unparalleled spectral fidelity. By integrating artificial intelligence (AI), this field has undergone a paradigm shift, enabling the extraction of actionable insights from high-dimensional datasets that were previously intractable using conventional methods. AI techniques, particularly deep learning and machine learning, address the intrinsic challenges of hyperspectral data, such as the curse of dimensionality, spectral mixing, and noise, while unlocking novel capabilities for feature extraction, classification, and predictive modeling. AI-driven frameworks, including convolutional neural networks, transformers, and generative models, have revolutionized hyperspectral applications across diverse domains, including resource exploration, environmental monitor, urban application, precision agriculture, etc.
As hyperspectral sensors proliferate across satellites, drones, and ground platforms, AI bridges the gap between data complexity and actionable insights, driving innovation in urban, energy, ecology, and beyond, while democratizing access to advanced spectral analysis tools. This Special Issue will include studies covering artificial intelligence in hyperspectral remote sensing data analysis, including both the optimization and enhancement of hyperspectral interpretation algorithms and application case studies based on deep learning for hyperspectral data analysis. Articles may address, but are not limited, to the following topics:
- Hyperspectral Pre-trained Models and Foundation Models;
- Multimodal Fusion and Spectral–Spatial Feature Learning;
- Explainable AI for Hyperspectral Remote Sensing;
- Self-Supervised Spectral–Spatial Representation Learning;
- Hyperspectral Classification and Change Detection;
- Hyperspectral Unmixing and Mixed Pixel Analysis;
- Hyperspectral Precision Agriculture;
- Intelligent Hyperspectral Environmental Monitoring.
Dr. Xue Wang
Prof. Dr. Kun Tan
Dr. Haoyang Yu
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
- spectral-spatial representation learning
- multimodal fusion & explainable AI
- hyperspectral unmixing & mixed pixel analysis
- intelligent remote sensing applications
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