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Remote Sensing
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Deep Learning for Spectral-Spatial Hyperspectral Image Classification (2nd Edition)
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Deep Learning for Spectral-Spatial Hyperspectral Image Classification (2nd Edition)

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

Deadline for manuscript submissions: 12 February 2026 | Viewed by 1859

Special Issue Editors

Dr. Jiayi Li

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: urban remote sensing; urban ecology and environmental analysis; high-resolution remote sensing processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maria Grazia D’Urso

E-Mail Website
Guest Editor
Department of Engineering and Applied Sciences, University of Bergamo, Via Salvecchio 19, 24129 Bergamo, Italy
Interests: platform UAVs and sensors in precision farming; growth and health indices of crops; machine learning and land use simulation models
Special Issues, Collections and Topics in MDPI journals
Dr. Xian Guo

E-Mail Website
Guest Editor
School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing, China
Interests: high-dimensional spatiotemporal data mining; hyperspectral image classification
Special Issues, Collections and Topics in MDPI journals
Dr. Jie Yang

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: urban remote sensing; operational land cover mapping; spatiotemporal analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xin Huang

grade E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: high spatial and hyperspectral remote sensing image processing methods and applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hyperspectral imaging has expanded our ability to gather detailed data about the Earth's surface. However, effectively utilizing this rich spectral information remains a challenge. Deep learning has emerged as a promising solution, revolutionizing hyperspectral image classification by automatically learning intricate spectral–spatial patterns. We therefore welcome contributions that present advancements in this exciting field and provide insights into more accurate and impactful applications.

This Special Issue aims to explore innovative developments in the application of deep learning techniques for spectral–spatial hyperspectral image classification. Researchers are encouraged to submit original research papers, reviews, or surveys. Submissions should adhere to high scientific standards, demonstrate the significance of their contributions, and offer clear experimental validation. We welcome submissions that address both theoretical advancements and real-world applications.

This Special Issue aims to cover a wide range of topics related to deep learning for spectral–spatial hyperspectral image classification, including, but not limited to, the following:

  1. The development and optimization of deep neural network architectures for hyperspectral data.
  2. Spectral and spatial information fusion in deep learning models.
  3. Dimensionality reduction methods for hyperspectral data pre-processing.
  4. Transfer learning and domain adaptation in hyperspectral image classification.
  5. Data augmentation and label noise learning.
  6. Benchmark datasets for hyperspectral classification.
  7. Explainable deep learning.
  8. Applications in environmental monitoring, agriculture, mineral exploration, and more.
  9. The integration of multi-modal data sources with hyperspectral imagery.
  10. Multi-task learning strategies for improved hyperspectral image classification and analysis.
  11. The optimization of deep learning models for computational efficiency in hyperspectral image classification.

This Special Issue is the second edition of “Deep Learning for Spectral-Spatial Hyperspectral Image Classification”: https://www.mdpi.com/journal/remotesensing/special_issues/37S843D4S5.

Dr. Jiayi Li
Prof. Dr. Maria Grazia D’Urso
Dr. Xian Guo
Dr. Jie Yang
Prof. Dr. Xin Huang
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

  • deep learning
  • hyperspectral imaging
  • spectral–spatial classification
  • domain adaption
  • attention mechanisms

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Related Special Issue

Published Papers (3 papers)

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Research

27 pages, 19042 KB  
Article
A Global Distribution-Aware Network for Open-Set Hyperspectral Image Classification
by Fengcheng Ji, Wenzhi Zhao, Qiao Wang and Rui Peng
Remote Sens. 2025, 17(24), 3938; https://doi.org/10.3390/rs17243938 - 5 Dec 2025
Viewed by 316
Abstract
Recently, developments in hyperspectral image (HSI) classification have brought increasing attention to the challenges of the open-set problem. However, current open-set methods generally overlook the intra-class multimodal structure, making it difficult to comprehensively capture the global data distribution, which in turn reduces their [...] Read more.
Recently, developments in hyperspectral image (HSI) classification have brought increasing attention to the challenges of the open-set problem. However, current open-set methods generally overlook the intra-class multimodal structure, making it difficult to comprehensively capture the global data distribution, which in turn reduces their ability to distinguish known from unknown classes. To address this, we propose a novel global distribution-aware network (GDAN) that jointly performs pixel-wise HSI classification and trustworthy uncertainty-aware identification of unknown class. First, a generative adversarial network (GAN) is employed as the backbone, enhanced with a self-attention (SA) module to capture long-range dependencies across the extensive spectral bands of hyperspectral data. Second, an interpretable open-set HSI classification framework is designed, combining GAN with Markov Chain Monte Carlo (MCMC) to model global distribution by exploring intra-class multimodal structures and estimate predictive uncertainty. In this framework, the traditionally fixed discriminator weights are reformulated as probability distributions, and posterior inference is conducted using MCMC within a Bayesian framework. Finally, accurate categories and predictive uncertainty of ground objects can be obtained through posterior sampling, while samples with high uncertainty are assigned to the unknown class, thus enabling accurate open-set HSI classification. Extensive experiments on three benchmark HSI datasets demonstrate the superiority of the proposed GDAN for open-set HSI classification, yielding overall classification accuracies of 94.6%, 92.6%, and 94.8% in the 200-sample scenario. Full article
(This article belongs to the Special Issue Deep Learning for Spectral-Spatial Hyperspectral Image Classification (2nd Edition))
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33 pages, 10355 KB  
Article
S2GL-MambaResNet: A Spatial–Spectral Global–Local Mamba Residual Network for Hyperspectral Image Classification
by Tao Chen, Hongming Ye, Guojie Li, Yaohan Peng, Jianming Ding, Huayue Chen, Xiangbing Zhou and Wu Deng
Remote Sens. 2025, 17(23), 3917; https://doi.org/10.3390/rs17233917 - 3 Dec 2025
Viewed by 483
Abstract
In hyperspectral image classification (HSIC), each pixel contains information across hundreds of contiguous spectral bands; therefore, the ability to perform long-distance modeling that stably captures and propagates these long-distance dependencies is critical. A selective structured state space model (SSM) named Mamba has shown [...] Read more.
In hyperspectral image classification (HSIC), each pixel contains information across hundreds of contiguous spectral bands; therefore, the ability to perform long-distance modeling that stably captures and propagates these long-distance dependencies is critical. A selective structured state space model (SSM) named Mamba has shown strong capabilities for capturing cross-band long-distance dependencies and exhibits advantages in long-distance modeling. However, the inherently high spectral dimensionality, information redundancy, and spatial heterogeneity of hyperspectral images (HSI) pose challenges for Mamba in fully extracting spatial–spectral features and in maintaining computational efficiency. To address these issues, we propose S2GL-MambaResNet, a lightweight HSI classification network that tightly couples Mamba with progressive residuals to enable richer global, local, and multi-scale spatial–spectral feature extraction, thereby mitigating the negative effects of high dimensionality, redundancy, and spatial heterogeneity on long-distance modeling. To avoid fragmentation of spatial–spectral information caused by serialization and to enhance local discriminability, we design a preprocessing method applied to the features before they are input to Mamba, termed the Spatial–Spectral Gated Attention Aggregator (SS-GAA). SS-GAA uses spatial–spectral adaptive gated fusion to preserve and strengthen the continuity of the central pixel’s neighborhood and its local spatial–spectral representation. To compensate for a single global sequence network’s tendency to overlook local structures, we introduce a novel Mamba variant called the Global_Local Spatial_Spectral Mamba Encoder (GLS2ME). GLS2ME comprises a pixel-level global branch and a non-overlapping sliding-window local branch for modeling long-distance dependencies and patch-level spatial–spectral relations, respectively, jointly improving generalization stability under limited sample regimes. To ensure that spatial details and boundary integrity are maintained while capturing spectral patterns at multiple scales, we propose a multi-scale Mamba encoding scheme, the Hierarchical Spectral Mamba Encoder (HSME). HSME first extracts spectral responses via multi-scale 1D spectral convolutions, then groups spectral bands and feeds these groups into Mamba encoders to capture spectral pattern information at different scales. Finally, we design a Progressive Residual Fusion Block (PRFB) that integrates 3D residual recalibration units with Efficient Channel Attention (ECA) to fuse multi-kernel outputs within a global context. This enables ordered fusion of local multi-scale features under a global semantic context, improving information utilization efficiency while keeping computational overhead under control. Comparative experiments on four publicly available HSI datasets demonstrate that S2GL-MambaResNet achieves superior classification accuracy compared with several state-of-the-art methods, with particularly pronounced advantages under few-shot and class-imbalanced conditions. Full article
(This article belongs to the Special Issue Deep Learning for Spectral-Spatial Hyperspectral Image Classification (2nd Edition))
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25 pages, 6484 KB  
Article
FreqMamba: A Frequency-Aware Mamba Framework with Group-Separated Attention for Hyperspectral Image Classification
by Tong Zhou, Jianghe Zhai and Zhiwen Zhang
Remote Sens. 2025, 17(22), 3749; https://doi.org/10.3390/rs17223749 - 18 Nov 2025
Viewed by 617
Abstract
Hyperspectral imagery (HSI), characterized by the integration of both spatial and spectral information, is widely employed in various fields, such as environmental monitoring, geological exploration, precision agriculture, and medical imaging. Hyperspectral image classification (HSIC), as a key research direction, aims to establish a [...] Read more.
Hyperspectral imagery (HSI), characterized by the integration of both spatial and spectral information, is widely employed in various fields, such as environmental monitoring, geological exploration, precision agriculture, and medical imaging. Hyperspectral image classification (HSIC), as a key research direction, aims to establish a mapping relationship between pixels and land-cover categories. Nevertheless, several challenges persist, including difficulties in feature extraction, the trade-off between effective integration of local and global features, and spectral redundancy. We propose FreqMamba, a novel model that efficiently combines CNN, a custom attention mechanism, and the Mamba architecture. The proposed framework comprises three key components: (1) A novel multi-scale deformable convolution feature extraction module equipped with spectral attention, which processes spectral and spatial information through a dual-branch structure to enhance feature representation for irregular terrain contours; (2) a novel group-separated attention module that integrates group convolution with group-separated self-attention, effectively balancing local feature extraction and global contextual modeling; (3) a newly introduced bidirectional scanning Mamba branch that efficiently captures long-range dependencies with linear computational complexity. The proposed method achieves optimal performance on multiple benchmark datasets, including QUH-Tangdaowan, QUH-Qingyun, and QUH-Pingan, with the highest overall accuracy reaching 97.47%, average accuracy reaching 93.52%, and a Kappa coefficient of 96.22%. It significantly outperforms existing CNN, Transformer, and SSM-based methods, demonstrating its effectiveness, robustness, and superior generalization capability. Full article
(This article belongs to the Special Issue Deep Learning for Spectral-Spatial Hyperspectral Image Classification (2nd Edition))
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