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Remote Sensing
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Hyperspectral Classification in Remote Sensing: State-of-the-Art Methods and Emerging Trends
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Hyperspectral Classification in Remote Sensing: State-of-the-Art Methods and Emerging Trends

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

Deadline for manuscript submissions: 25 August 2026 | Viewed by 1712

Special Issue Editors

Dr. Hao Sun

E-Mail Website
Guest Editor
School of Computer Science, Central China Normal University, Wuhan 430079, China
Interests: hyperspectral remote sensing; deep learning; computer vision; image classification
Prof. Dr. Zhigang Tu

E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
Interests: machine learning; computer vision; video understanding; reconstruction and generation
Dr. Le Dong

E-Mail Website
Guest Editor
School of Artificial Intelligence, Xidian University, Xi’an 710071, China
Interests: low-quality image reconstruction and target recognition; hyperspectral remote sensing image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Zhitong Xiong

E-Mail Website
Guest Editor
Data Science in Earth Observation, Technical University of Munich (TUM), 80333 Munich, Germany
Interests: deep learning; remote sensing; multimodal foundation models
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hyperspectral remote sensing is a technique that identifies and analyzes surface features by acquiring detailed spectral information of objects in multiple continuous spectral bands. Rich and fine-grained spectral data make hyperspectral image classification play an important role in fields such as environmental monitoring, precision agriculture, and urban planning. In recent years, hyperspectral image classification research has made rapid progress, thanks to the accumulation of a large number of hyperspectral datasets and the development of excellent algorithms, especially algorithms using deep learning techniques. These innovations have significantly improved the accuracy of hyperspectral classification methods. However, in complex and open environments, hyperspectral image classification still faces many challenges, including class imbalance, noise interference, and domain generalization. Solving these challenges is crucial to fully realize the potential of hyperspectral data and further improve the effectiveness of hyperspectral remote sensing in practical applications.

The aim of this Special Issue is to present the latest research and advancements in hyperspectral image classification in remote sensing. It will focus on state-of-the-art methods and emerging trends, as well as the challenges and opportunities in this field.

Original research articles and reviews are welcome. Articles may cover, but are not limited to, the following topics:

  • Advances in deep learning techniques for hyperspectral image classification.
  • Unsupervised and semi-supervised learning for hyperspectral image classification.
  • Interpretability of hyperspectral image classification models.
  • Emerging trends in hyperspectral image classification.
  • Application of hyperspectral image classification in real scenes.

Dr. Hao Sun
Prof. Dr. Zhigang Tu
Prof. Dr. Le Dong
Dr. Zhitong Xiong
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 classification
  • remote sensing
  • spectral-spatial features
  • feature extraction
  • deep learning
  • machine learning

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Published Papers (1 paper)

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Research

23 pages, 10516 KB  
Article
SSGTN: Spectral–Spatial Graph Transformer Network for Hyperspectral Image Classification
by Haotian Shi, Zihang Luo, Yiyang Ma, Guanquan Zhu and Xin Dai
Remote Sens. 2026, 18(2), 199; https://doi.org/10.3390/rs18020199 - 7 Jan 2026
Viewed by 725
Abstract
Hyperspectral image (HSI) classification is fundamental to a wide range of remote sensing applications, such as precision agriculture, environmental monitoring, and urban planning, because HSIs provide rich spectral signatures that enable the discrimination of subtle material differences. Deep learning approaches, including Convolutional Neural [...] Read more.
Hyperspectral image (HSI) classification is fundamental to a wide range of remote sensing applications, such as precision agriculture, environmental monitoring, and urban planning, because HSIs provide rich spectral signatures that enable the discrimination of subtle material differences. Deep learning approaches, including Convolutional Neural Networks (CNNs), Graph Convolutional Networks (GCNs), and Transformers, have achieved strong performance in learning spatial–spectral representations. However, these models often face difficulties in jointly modeling long-range dependencies, fine-grained local structures, and non-Euclidean spatial relationships, particularly when labeled training data are scarce. This paper proposes a Spectral–Spatial Graph Transformer Network (SSGTN), a dual-branch architecture that integrates superpixel-based graph modeling with Transformer-based global reasoning. SSGTN consists of four key components, namely (1) an LDA-SLIC superpixel graph construction module that preserves discriminative spectral–spatial structures while reducing computational complexity, (2) a lightweight spectral denoising module based on 1×1 convolutions and batch normalization to suppress redundant and noisy bands, (3) a Spectral–Spatial Shift Module (SSSM) that enables efficient multi-scale feature fusion through channel-wise and spatial-wise shift operations, and (4) a dual-branch GCN-Transformer block that jointly models local graph topology and global spectral–spatial dependencies. Extensive experiments on three public HSI datasets (Indian Pines, WHU-Hi-LongKou, and Houston2018) under limited supervision (1% training samples) demonstrate that SSGTN consistently outperforms state-of-the-art CNN-, Transformer-, Mamba-, and GCN-based methods in overall accuracy, Average Accuracy, and the κ coefficient. The proposed framework provides an effective baseline for HSI classification under limited supervision and highlights the benefits of integrating graph-based structural priors with global contextual modeling. Full article
(This article belongs to the Special Issue Hyperspectral Classification in Remote Sensing: State-of-the-Art Methods and Emerging Trends)
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