From Pixels to Spectra: Towards Generalizable Large Models for Hyperspectral 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: 28 February 2026 | Viewed by 19
Special Issue Editors
Interests: multimodal remote sensing interpretation
Special Issues, Collections and Topics in MDPI journals
Interests: artificial Intelligence; remote sensing; computer vision; geospatial applications
Interests: remote sensing image anomaly detection; change detection and target detection and recognition; image generation and segmentation
Interests: deep learning; remote sensing; multimodal foundation models
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, high-resolution data from space satellites and unmanned aerial vehicles have increased sharply, and hyperspectral remote sensing has gradually become a key means for achieving high-precision Earth observation. However, due to the high dimension, spectral redundancy, and limited labeled samples of hyperspectral data, hyperspectral interpretation tasks still face challenges such as poor robustness and weak scalability. Although deep learning has made significant progress in feature extraction and spectral space modeling, most existing methods are targeted at specific tasks and supervised data. Therefore, the emergence of large-scale visual language models and inference models has provided new opportunities for constructing generalizable and reusable representations in various hyperspectral tasks.
This Special Issue will promote the development of generalized and transferable multimodal large models for hyperspectral remote sensing data. We welcome original research on hyperspectral interpretation based on large-scale pre-training, multi-task learning, spectral space transformation, and general models adapted to hyperspectral data. The topics include but are not limited to hyperspectral feature learning, spectral decomposition, general models, semantic segmentation, and change detection. We also encourage the submission of practical applications in agriculture, urban monitoring, mineral mapping, and environmental assessment.
Topics of interest include, but are not limited to, the following:
- Spectral–spatial feature extraction for hyperspectral data;
- Large-scale and transformer-based architectures for HSI understanding;
- Self-supervised, unsupervised, and few-shot learning for label-efficient representation;
- Spectral–spatial pretraining and cross-task model generalization;
- Dimensionality reduction, feature selection, and spectral manifold learning;
- Lightweight or efficient models for edge deployment or real-time inference;
- Applications in spectral unmixing, target detection, segmentation, and change detection;
- Open benchmarks, scalable datasets, and evaluation protocols for large model validation.
We also encourage research on hyperspectral interpretation training datasets and inference evaluation datasets to enhance the adaptability and robustness of hyperspectral image analysis in real-world scenarios.
Prof. Dr. Yaxiong Chen
Dr. Qingyu Li
Dr. Wuxia Zhang
Dr. Zhitong Xiong
Guest Editors
Manuscript Submission Information
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Keywords
- hyperspectral image processing
- spectral–spatial feature extraction
- dimensionality reduction
- deep learning for HSI
- self-supervised learning
- spectral unmixing
- semantic segmentation
- change detection
- lightweight models
- high-resolution remote sensing
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