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Remote Sensing
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Recent Advances in Multimodal Hyperspectral Remote Sensing
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Recent Advances in Multimodal Hyperspectral Remote Sensing

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

Deadline for manuscript submissions: 28 May 2025 | Viewed by 765

Special Issue Editors

Prof. Dr. Yifan Zhang

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi’an, China
Interests: remote sensing image analysis and processing; image fusion restoration and enhancement
Special Issues, Collections and Topics in MDPI journals
Dr. Mercedes E. Paoletti

E-Mail Website
Guest Editor
Department of Computer Architecture and Automation, Faculty of Computer Science, Complutense University of Madrid, 28040 Madrid, Spain
Interests: hyperspectral image fusion; hyperspectral classification; high-order tensor analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Yi Wang

E-Mail Website
Guest Editor Assistant
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong
Interests: machine learning; pattern recognition; object recognition; image processing

Special Issue Information

Dear Colleagues,

Hyperspectral remote sensing imaging is one of the most important means of remote sensing detection and data acquisition. With the rapid development in photoelectric sensing technology and platforms, hyperspectral remote sensing imaging has shown a new trend of multimodalization. Hyperspectral remote sensing imaging is evolving from traditional unimodal spectral imaging to a new trend encompassing multimodal spectral imaging, acquiring integrated information while maintaining a high spectral resolution. Multimodal hyperspectral remote sensing extends spatial–spectral information in time and 3D spatial dimensions, resulting in new types of spectral imaging, including multitemporal hyperspectral images, hyperspectral video, stereo hyperspectral point cloud, and so on. Meanwhile, multimodal hyperspectral remote sensing brings new challenges to the related data acquisition, processing, analysis, and application.

This Special Issue aims to report on and cover the latest advances and trends regarding multimodal hyperspectral remote sensing. We welcome papers focusing on both theoretical methods and applicative techniques, as well as contributions regarding new advanced methodologies to relevant scenarios of multimodal hyperspectral remote sensing. We are looking forward to receiving your contributions.

Prof. Dr. Yifan Zhang
Dr. Mercedes E. Paoletti
Guest Editors

Dr. Yi Wang
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

  • multimodal hyperspectral remote sensing imaging
  • multitemporal hyperspectral imaging
  • hyperspectral video imaging
  • hyperspectral stereo imaging
  • hyperspectral image processing
  • hyperspectral image analysis

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Published Papers (1 paper)

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Research

29 pages, 16582 KiB  
Article
Local Sub-Block Contrast and Spatial–Spectral Gradient Feature Fusion for Hyperspectral Anomaly Detection
by Dong Zhao, Xingchen Xu, Mingtao You, Pattathal V. Arun, Zhe Zhao, Jiahong Ren, Li Wu and Huixin Zhou
Remote Sens. 2025, 17(4), 695; https://doi.org/10.3390/rs17040695 - 18 Feb 2025
Viewed by 422
Abstract
Most existing hyperspectral anomaly detection algorithms primarily rely on spatial information to identify anomalous targets. However, they often overlook the spatial–spectral gradient information inherent in hyperspectral images, which can lead to decreased detection accuracy. To address this limitation, we propose a novel hyperspectral [...] Read more.
Most existing hyperspectral anomaly detection algorithms primarily rely on spatial information to identify anomalous targets. However, they often overlook the spatial–spectral gradient information inherent in hyperspectral images, which can lead to decreased detection accuracy. To address this limitation, we propose a novel hyperspectral anomaly detection algorithm that incorporates both local sub-block contrast and spatial–spectral gradient features. In this approach, a grid block window is utilized to capture local spatial information. To effectively detect low-contrast targets, we introduce a novel local sub-block ratio-multiply contrast method that enhances anomalous regions while suppressing the background. Additionally, to mitigate the challenges posed by complex backgrounds, a feature extraction technique based on spatial–spectral gradients is proposed. To account for the spectral reflectance differences between anomalous targets and the background, we further introduce a local sub-block ratio-difference contrast method to compute preliminary detection scores. The final anomaly detection results are obtained by merging these two detection scores. The key advantage of the proposed method lies in its ability to exploit local gradient characteristics within hyperspectral images, thereby resolving the issue of edge features being misidentified as anomalies. This method also effectively reduces the impact of noise on detection accuracy. Experimental validation based on four real-world datasets demonstrates that the proposed method outperforms seven state-of-the-art techniques, showing superior performance in both qualitative and quantitative evaluations. Full article
(This article belongs to the Special Issue Recent Advances in Multimodal Hyperspectral Remote Sensing)
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