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Trends and Prospects in Hyperspectral Remote Sensing Images Processing and...
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Trends and Prospects in Hyperspectral Remote Sensing Images Processing and Analysis

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

Deadline for manuscript submissions: 15 September 2025 | Viewed by 2554

Special Issue Editors

Dr. Qiang Zhang

E-Mail Website
Guest Editor
Information Science and Technology College, Dalian Maritime University, Dalian 116028, China
Interests: hyperspectral image processing; deep learning; computer vision
Dr. Kui Jiang

E-Mail Website
Guest Editor
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Interests: computer vision; image processing; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hyperspectral remote sensing technology is able to analyze the structure and material characteristics of surface features by simultaneously obtaining spatial and spectral information. It also has the ability to distinguish more types of features and invert an increased number of features. Currently, hyperspectral remote sensing is widely used in many fields, such as precision agriculture, geological surveys, land and resource surveys, atmospheric environment analysis, and military target detection. Hyperspectral image processing and analysis play the crucial role for the above applications, such as anomaly detection, denoising, super-resolution, object detection, fusion, classification, unmixing, etc. Therefore, the main purpose of this Special Issue is to showcase recent techniques and trends used for processing and analyzing hyperspectral remote sensing images and to promote the development of spectral imaging technologies.

This Special Issue aims to collect studies concerning hyperspectral remote sensing image processing and analysis, especially for the new methods, frameworks, trends, and prospects.

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

  • Hyperspectral image anomaly detection;
  • Hyperspectral image denoising;
  • Hyperspectral image super-resolution;
  • Hyperspectral image object detection;
  • Hyperspectral image fusion;
  • Hyperspectral image classification;
  • Hyperspectral image unmixing;
  • Hyperspectral video tracking.

Dr. Qiang Zhang
Dr. Kui Jiang
Guest Editors

Mr. Yi Xiao
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

  • hyperspectral image
  • object detection
  • deep learning
  • image processing
  • classification
  • anomaly detection
  • super-resolution
  • denoising
  • fusion
  • unmixing

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Published Papers (3 papers)

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Research

18 pages, 10219 KiB  
Article
Automatic Registration of Remote Sensing High-Resolution Hyperspectral Images Based on Global and Local Features
by Xiaorong Zhang, Siyuan Li, Zhongyang Xing, Binliang Hu and Xi Zheng
Remote Sens. 2025, 17(6), 1011; https://doi.org/10.3390/rs17061011 - 13 Mar 2025
Viewed by 411
Abstract
Automatic registration of remote sensing images is an important task, which requires the establishment of appropriate correspondence between the sensed image and the reference image. Nowadays, the trend of satellite remote sensing technology is shifting towards high-resolution hyperspectral imaging technology. Ever higher revisit [...] Read more.
Automatic registration of remote sensing images is an important task, which requires the establishment of appropriate correspondence between the sensed image and the reference image. Nowadays, the trend of satellite remote sensing technology is shifting towards high-resolution hyperspectral imaging technology. Ever higher revisit cycles and image resolutions require higher accuracy and real-time performance for automatic registration. The push-broom payload is affected by the push-broom stability of the satellite platform and the elevation change of ground objects, and the obtained hyperspectral image may have distortions such as stretching or shrinking at different parts of the image. In order to solve this problem, a new automatic registration strategy for remote sensing hyperspectral images based on the combination of whole and local features of the image was established, and two granularity registrations were carried out, namely coarse-grained matching and fine-grained matching. The high-resolution spatial features are first employed for detecting scale-invariant features, while the spectral information is used for matching, and then the idea of image stitching is employed to fuse the image after fine registration to obtain high-precision registration results. In order to verify the proposed algorithm, a simulated on-orbit push-broom imaging experiment was carried out to obtain hyperspectral images with local complex distortions under different lighting conditions. The simulation results show that the proposed remote sensing hyperspectral image registration algorithm is superior to the existing automatic registration algorithms. The advantages of the proposed algorithm in terms of registration accuracy and real-time performance make it have a broad prospect for application in satellite ground application systems. Full article
(This article belongs to the Special Issue Trends and Prospects in Hyperspectral Remote Sensing Images Processing and Analysis)
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25 pages, 12377 KiB  
Article
Exploiting Weighted Multidirectional Sparsity for Prior Enhanced Anomaly Detection in Hyperspectral Images
by Jingjing Liu, Jiashun Jin, Xianchao Xiu, Wanquan Liu and Jianhua Zhang
Remote Sens. 2025, 17(4), 602; https://doi.org/10.3390/rs17040602 - 10 Feb 2025
Cited by 1 | Viewed by 533
Abstract
Anomaly detection (AD) is an important topic in remote sensing, aiming to identify unusual or abnormal features within the data. However, most existing low-rank representation methods usually use the nuclear norm for background estimation, and do not consider the different contributions of different [...] Read more.
Anomaly detection (AD) is an important topic in remote sensing, aiming to identify unusual or abnormal features within the data. However, most existing low-rank representation methods usually use the nuclear norm for background estimation, and do not consider the different contributions of different singular values. Besides, they overlook the spatial relationships of abnormal regions, particularly failing to fully leverage the 3D structured information of the data. Moreover, noise in practical scenarios can disrupt the low-rank structure of the background, making it challenging to separate anomaly from the background and ultimately reducing detection accuracy. To address these challenges, this paper proposes a weighted multidirectional sparsity regularized low-rank tensor representation method (WMS-LRTR) for AD. WMS-LRTR uses the weighted tensor nuclear norm for background estimation to characterize the low-rank property of the background. Considering the correlation between abnormal pixels across different dimensions, the proposed method introduces a novel weighted multidirectional sparsity (WMS) by unfolding anomaly into multimodal to better exploit the sparsity of the anomaly. In order to improve the robustness of AD, we further embed a user-friendly plug-and-play (PnP) denoising prior to optimize the background modeling under low-rank structure and facilitate the separation of sparse anomalous regions. Furthermore, an effective iterative algorithm using alternate direction method of multipliers (ADMM) is introduced, whose subproblems can be solved quickly by fast solvers or have closed-form solutions. Numerical experiments on various datasets show that WMS-LRTR outperforms state-of-the-art AD methods, demonstrating its better detection ability. Full article
(This article belongs to the Special Issue Trends and Prospects in Hyperspectral Remote Sensing Images Processing and Analysis)
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21 pages, 9794 KiB  
Article
Weamba: Weather-Degraded Remote Sensing Image Restoration with Multi-Router State Space Model
by Shuang Wu, Xin He and Xiang Chen
Remote Sens. 2025, 17(3), 458; https://doi.org/10.3390/rs17030458 - 29 Jan 2025
Viewed by 814
Abstract
Adverse weather conditions, such as haze and raindrop, consistently degrade the quality of remote sensing images and affect subsequent vision-based applications. Recent years have witnessed advancements in convolutional neural networks (CNNs) and Transformers in the field of remote sensing image restoration. However, these [...] Read more.
Adverse weather conditions, such as haze and raindrop, consistently degrade the quality of remote sensing images and affect subsequent vision-based applications. Recent years have witnessed advancements in convolutional neural networks (CNNs) and Transformers in the field of remote sensing image restoration. However, these methods either suffer from limited receptive fields or incur quadratic computational overhead, leading to an imbalance between performance and model efficiency. In this paper, we propose an effective vision state space model (called Weamba) for remote sensing image restoration by modeling long-range pixel dependencies with linear complexity. Specifically, we develop a local-enhanced state space module to better aggregate rich local and global information, both of which are complementary and beneficial for high-quality image reconstruction. Furthermore, we design a multi-router scanning strategy for spatially varying feature extraction, alleviating the issue of redundant information caused by repeated scanning directions in existing methods. Extensive experiments on multiple benchmarks show that the proposed Weamba performs favorably against state-of-the-art approaches. Full article
(This article belongs to the Special Issue Trends and Prospects in Hyperspectral Remote Sensing Images Processing and Analysis)
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