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Remote Sensing
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Advances in Hyperspectral Remote Sensing Image Processing: 2nd Edition
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Advances in Hyperspectral Remote Sensing Image Processing: 2nd Edition

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1782

Special Issue Editors

Prof. Dr. Xudong Kang

E-Mail Website
Guest Editor
School of Robotics, Hunan University, Changsha 410082, China
Interests: remote sensing image processing and application
Special Issues, Collections and Topics in MDPI journals
Dr. Mingmin Chi

E-Mail Website
Guest Editor
School of Computer Science, Fudan University, 2005 SongHu Road, YangPu District, Shanghai 200438, China
Interests: big data; data science; industrial intelligence; machine learning; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hyperspectral remote sensing images have become a well-established data source for precision agriculture, resource investigation, environmental monitoring, national defense, and other important fields for their fine spectral information. Meanwhile, the renewal of the space industry in various countries has also established a relatively complete remote sensing data acquisition system, which provides data support and new power for hyperspectral remote sensing earth observation. Under the rapid development of artificial intelligence, big data, 5G, and other advanced technologies, massive hyperspectral data processing and reasonable application have become a hot research topic in remote sensing.

This Special Issue aims to study hyperspectral remote sensing image processing methods and applications. Topics may range from comparative study, overview, and different types of hyperspectral remote sensing image processing methods to more comprehensive practical applications. Therefore, the method design, performance evaluation, application implementation, and other issues based on hyperspectral remote sensing image processing are welcome. Articles may address, but are not limited to, the following topics:

  • Comparative study or review of hyperspectral image processing methods;
  • Primary processing methods of hyperspectral remote sensing images (e.g., rectification, denoising, restoration, enhancement);
  • Intermediate processing methods of hyperspectral remote sensing images (e.g., feature selection and extraction, super-resolution, clustering, image fusion, unmixing);
  • Advanced processing methods of hyperspectral remote sensing images (e.g., classification, target detection, anomaly detection, segmentation, scene recognition, image interpretation);
  • Application of hyperspectral remote sensing image processing results in real scenarios (e.g., disaster monitoring, precision agriculture, resource investigation);
  • Lightweight networks and efficient targeted systems designed for hyperspectral remote sensing image processing;
  • Opportunities and challenges in hyperspectral remote sensing image processing.

Prof. Dr. Xudong Kang
Dr. Mingmin Chi
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 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 image
  • feature extraction
  • image processing
  • image analysis
  • artificial intelligence
  • image classification
  • information fusion

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Related Special Issue

Published Papers (2 papers)

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Research

22 pages, 16205 KiB  
Article
Hyper Spectral Camera ANalyzer (HyperSCAN)
by Wen-Qian Chang, Hsun-Ya Hou, Pei-Yuan Li, Michael W. Shen, Cheng-Ling Kuo, Tang-Huang Lin, Loren C. Chang, Chi-Kuang Chao and Jann-Yenq Liu
Remote Sens. 2025, 17(5), 842; https://doi.org/10.3390/rs17050842 - 27 Feb 2025
Viewed by 324
Abstract
HyperSCAN (Hyper Spectral Camera ANalyzer) is a hyperspectral imager which monitors the Earth’s environment and also an educational platform to integrate college students’ ideas and skills in optical design and data processing. The advantages of HyperSCAN are that it is designed for modular [...] Read more.
HyperSCAN (Hyper Spectral Camera ANalyzer) is a hyperspectral imager which monitors the Earth’s environment and also an educational platform to integrate college students’ ideas and skills in optical design and data processing. The advantages of HyperSCAN are that it is designed for modular design, is compact and lightweight, and low-cost using commercial off-the-shelf (COTS) optical components. The modular design allows for flexible and rapid development, as well as validation within college lab environments. To optimize space utilization and reduce the optical path, HyperSCAN’s optical system incorporates a folding mirror, making it ideal for the constrained environment of a CubeSat. The use of COTS components significantly lowers pre-development costs and minimizes associated risks. The compact size and cost-effectiveness of CubeSats, combined with the advanced capabilities of hyperspectral imagers, make them a powerful tool for a broad range of applications, such as environmental monitoring of Earth, disaster management, mineral and resource exploration, atmospheric and climate studies, and coastal and marine research. We conducted a spatial-resolution-boost experiment using HyperSCAN data and various hyperspectral datasets including Urban, Pavia University, Pavia Centre, Botswana, and Indian Pines. After testing various data-fusion deep learning models, the best image quality of these methods is a two-branches convolutional neural network (TBCNN), where TBCNN retrieves spatial and spectral features in parallel and reconstructs the higher-spatial-resolution data. With the aid of higher-spatial-resolution multispectral data, we can boost the spatial resolution of HyperSCAN data. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing: 2nd Edition)
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22 pages, 4188 KiB  
Article
Hyperspectral Object Detection Based on Spatial–Spectral Fusion and Visual Mamba
by Wenjun Li, Fuqiang Yuan, Hongkun Zhang, Zhiwen Lv and Beiqi Wu
Remote Sens. 2024, 16(23), 4482; https://doi.org/10.3390/rs16234482 - 29 Nov 2024
Cited by 1 | Viewed by 1134
Abstract
Hyperspectral object-detection algorithms based on deep learning have been receiving increasing attention due to their ability to operate without relying on prior spectral information about the target and their strong real-time inference performance. However, current methods are unable to efficiently extract both spatial [...] Read more.
Hyperspectral object-detection algorithms based on deep learning have been receiving increasing attention due to their ability to operate without relying on prior spectral information about the target and their strong real-time inference performance. However, current methods are unable to efficiently extract both spatial and spectral information from hyperspectral image data simultaneously. In this study, an innovative hyperspectral object-detection algorithm is proposed that improves the detection accuracy compared to benchmark algorithms and state-of-the-art hyperspectral object-detection algorithms. Specifically, to achieve the integration of spectral and spatial information, we propose an innovative edge-preserving dimensionality reduction (EPDR) module. This module applies edge-preserving dimensionality reduction, based on spatial texture-weighted fusion, to the raw hyperspectral data, producing hyperspectral data that integrate both spectral and spatial information. Subsequently, to enhance the network’s perception of aggregated spatial and spectral data, we integrate a CNN with Visual Mamba to construct a spatial feature enhancement module (SFEM) with linear complexity. The experimental results demonstrate the effectiveness of our method. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing: 2nd Edition)
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