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Remote Sensing
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Recent Advances in Hyperspectral Remote Sensing: Theories, Technologies and Applications
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Recent Advances in Hyperspectral Remote Sensing: Theories, Technologies and Applications

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 January 2026 | Viewed by 1951

Special Issue Editors

Dr. Jiaxin Li

E-Mail Website
Guest Editor
School of Computer Science and Technology (National Exemplary Software School), Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Interests: hyperspectral remote sensing; remote sensing; multi-modal remote sensing; artificial intelligence; deep learning
Prof. Dr. Lianru Gao

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: geophysical image processing; image classification; hyperspectral imaging; remote sensing; feature extraction; image resolution; learning (artificial intelligence); geophysical techniques; object detection; feedforward neural nets; optical radar; convolutional neural nets; image fusion; image reconstruction; image representation; remote sensing by laser beam; Bayes methods; Markov processes; aerosols; agriculture; air pollution; artificial satellites; atmospheric optics; convex programming; convolution
Special Issues, Collections and Topics in MDPI journals
Dr. Ke Zheng

E-Mail Website
Guest Editor Assistant
School of Geography and Environment, Liaocheng University, Liaocheng 252000, China
Interests: image processing, machine learning, deep learning and their application in Earth Vision; remote sensing

Special Issue Information

Dear Colleagues,

Hyperspectral remote sensing enables the detection of targets with a high spectral resolution across narrow wavelength bands, combining spatial imagery with continuous spectral data. This technology allows for precise identification and offers a transformative perspective regarding the observation of Earth. Moreover, recent advances in artificial intelligence have further propelled progress in hyperspectral remote sensing, enhancing its theoretical foundations, technological capabilities, and practical applications across diverse fields.

This Special Issue seeks to showcase the cutting-edge developments in hyperspectral remote sensing, including theoretical innovations, technological breakthroughs, and novel applications. By compiling a collection of the latest studies, we aim to provide valuable insights for the remote sensing research community and foster future advancements in this dynamic field.

In this Special Issue, both original research articles and reviews are welcome. The research areas may include (but are not limited to) the following:

  1. Hyperspectral low-level vision tasks (e.g., denoising, restoration, super-resolution, fusion);
  2. Hyperspectral high-level tasks (e.g., classification, segmentation, anomaly detection);
  3. The application of hyperspectral remote sensing in specific fields (e.g., precision agriculture, water resource management, mineral exploration).

Dr. Jiaxin Li
Prof. Dr. Lianru Gao
Guest Editors

Dr. Ke Zheng
Guest Editor Assistant

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

  • remote sensing
  • hyperspectral remote sensing
  • image processing
  • hyperspectral applications
  • artificial intelligence

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

32 pages, 8264 KB  
Article
SATRNet: Self-Attention-Aided Deep Unfolding Tensor Representation Network for Robust Hyperspectral Anomaly Detection
by Jing Yang, Jianbin Zhao, Lu Chen, Haorui Ning and Ying Li
Remote Sens. 2025, 17(18), 3137; https://doi.org/10.3390/rs17183137 - 10 Sep 2025
Viewed by 353
Abstract
Hyperspectral anomaly detection (HAD) aims to separate subtle anomalies of a given hyperspectral image (HSI) from its background, which is a hot topic as well as a challenging inverse problem. Despite the significant success of the deep learning-based HAD methods, they are hard [...] Read more.
Hyperspectral anomaly detection (HAD) aims to separate subtle anomalies of a given hyperspectral image (HSI) from its background, which is a hot topic as well as a challenging inverse problem. Despite the significant success of the deep learning-based HAD methods, they are hard to interpret due to their black-box nature. Meanwhile, deep learning methods suffer from the identity mapping (IM) problem, referring to the network excessively focusing on the precise reconstruction of the background while neglecting the appropriate representation of anomalies. To this end, this paper proposes a self-attention-aided deep unfolding tensor representation network (SATRNet) for interpretable HAD by solving the tensor representation (TR)-based optimization model within the framework of deep networks. In particular, a Self-Attention Learning Module (SALM) was first designed to extract discriminative features of the input HSI. The HAD problem was then formulated as a tensor representation problem by exploring both the low-rankness of the background and the sparsity of the anomaly. A Weight Learning Module (WLM) exploring local details was also generated for precise background reconstruction. Finally, a deep network was built to solve the TR-based problem through unfolding and parameterizing the iterative optimization algorithm. The proposed SATRNet prevents the network from learning meaningless mappings, making the network interpretable to some extent while essentially solving the IM problem. The effectiveness of the proposed SATRNet is validated on 11 benchmark HSI datasets. Notably, the performance of SATRNet against adversarial attacks is also investigated in the experimentation, which is the first work exploring adversarial robustness in HAD to the best of our knowledge. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Remote Sensing: Theories, Technologies and Applications)
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23 pages, 11704 KB  
Article
Reliable Task-Constrained Band Selection Method for Hyperspectral Anomaly Detection
by Genrui Zhang, Wenzheng Wang, Yuqi Han, Chenwei Deng and Xingshi Luo
Remote Sens. 2025, 17(17), 3081; https://doi.org/10.3390/rs17173081 - 4 Sep 2025
Viewed by 869
Abstract
Hyperspectral band selection utilizes a crucial band subset to represent original data. In hyperspectral anomaly detection tailored for specific tasks, detection performance can be enhanced by pre-selecting a subset of bands that are more representative. However, existing methods remain constrained in modeling spatial–spectral [...] Read more.
Hyperspectral band selection utilizes a crucial band subset to represent original data. In hyperspectral anomaly detection tailored for specific tasks, detection performance can be enhanced by pre-selecting a subset of bands that are more representative. However, existing methods remain constrained in modeling spatial–spectral dependencies and simultaneously extracting distinct bands’ contribution from the established model, thus struggling to balance effectiveness and stability. To address these issues, we propose a reliable band selection method for anomaly detection. Concretely, we conduct a convolution–transformer hybrid autoencoder architecture to fully exploit the local and global spatial–spectral interdependencies. Next, we design an anomaly–background separability constraint to seamlessly integrate the task priors of anomaly detection into network optimization. Furthermore, we design a spectral attention module to quantify the contribution of different bands during network optimization. Simultaneously, an adaptive band allocation method is designed to optimize the internal structure of the selected band subset. Extensive experiments demonstrate that the proposed method achieves more robust band selection results compared to existing related methods. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Remote Sensing: Theories, Technologies and Applications)
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