Hyperspectral Remote Sensing Image Analysis via Advanced Deep Learning and Computer Vision
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 June 2026 | Viewed by 9
Special Issue Editors
Interests: hyperspectral images; deep learning; machine learning; image processing; image enhancement
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral images; remote sensing image processing; machine learning; computer vision; large language models
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; low-level image processing; deep learning; computer vision
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hyperspectral imaging (HSI), with its ability to capture detailed spectral information across numerous contiguous bands, has revolutionized remote sensing by enabling fine-grained material identification and analysis. From precision agriculture and environmental monitoring to mineral exploration and urban planning, the applications of HSI are vast and critical. However, the high dimensionality, spectral–spatial complexity, and inherent noise of hyperspectral data present significant challenges for traditional analytical methods. Effectively unlocking the rich information within these datasets requires sophisticated computational approaches. Thus, it is urgent to analyze HSI through the advanced technological approaches of deep learning and computer vision.
This Special Issue aims to showcase the latest breakthroughs at the intersection of hyperspectral image analysis (HSI), advanced deep learning, and computer vision. It explores how modern computational intelligence can overcome the traditional limitations of HSI processing, pushing the boundaries of accuracy, efficiency, and interpretability. The goal is to compile a collection of high-quality research that demonstrates novel methodologies, addresses fundamental challenges like limited labeled data and model generalization, and opens new avenues for practical HSI applications.
Articles may address, but are not limited to, the following topics:
- Advanced Deep Learning Models for HSI: Exploration of Transformers, Graph Neural Networks (GNNs), Diffusion Models, and Large Language Models (LLMs) for HSI classification, segmentation, and target detection.
- Self-Supervised, Semi-Supervised, and Unsupervised Learning: Innovative techniques to mitigate the challenge of limited ground-truth labels in HSI analysis.
- Spectral–Spatial Feature Fusion: Novel architectures and methods (e.g., Pansharpening) for joint and effective exploitation of spectral and spatial information.
- HSI Super-Resolution and Restoration: Enhancing spatial and spectral resolution using deep learning, and developing related techniques for HSI denoising and destriping.
- Explainable AI (XAI) for HSI Interpretation: Developing transparent and interpretable deep learning models (such as deep unfolding networks, white-box Transformer, etc.) to build trust and provide insights into model decisions.
- Domain Adaptation and Transfer Learning: Methods to improve deep learning model robustness and generalizability across different sensors, seasons, and geographical areas.
- Lightweight and Efficient Deep Learning Models: Solutions for real-time HSI processing onboard satellites, drones, and other mobile platforms.
- Multimodal Data Fusion: Integrating HSI with LiDAR, SAR, or other data sources using deep learning for comprehensive analysis.
- Generative Models for HSI: Using GANs or VAEs for data augmentation, synthesis, anomaly detection, etc.
Dr. Peixian Zhuang
Dr. Xiangyong Cao
Prof. Dr. Xueyang Fu
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
- hyperspectral image analysis
- spectral–spatial feature fusion
- self-supervised learning
- Explainable AI (XAI)
- domain adaptation
- multimodal data fusion
- generative models
- lightweight deep learning
- transformer networks
- large language models
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