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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 741

Special Issue Editors


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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

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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Published Papers (1 paper)

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Research

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
Viewed by 535
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
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