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Sensors for Hyperspectral Imaging: Technologies, Methods and Data Processing—Second Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 525

Special Issue Editor


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Guest Editor
1. School of Science and Technology, Faculty of SABL, University of New England, Armidale, NSW 2351, Australia
2. Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, Ultimo, NSW 2007, Australia
3. Griffith Business School, Griffith University, Brisbane, QLD 4111, Australia
Interests: image analysis; deep learning; optimisation; decision support
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Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issue “Sensors for Hyperspectral Imaging: Technologies, Methods and Data Processing”, we are pleased to announce the next in the series, entitled “Sensors for Hyperspectral Imaging: Technologies, Methods and Data Processing—Second Edition”.

Hyperspectral imaging is a key image modality with significant application potential in diverse domains, including solving image application issues beyond visual, thermal, and multispectral images. In this Special Issue, we aim to bring together novel tools and technologies for the acquisition, processing, and analysis of hyperspectral images. We also aim to compile applications of hyperspectral imaging in different domains. This Special Issue has a two-fold focus:

New tools and techniques for image acquisition and processing, including (but not limited to) the following:

  • Band selection, data compression, information fusion, and data visualization;
  • Model/algorithm development using machine learning/deep learning;
  • New sensor technology and hardware.

Applications of hyperspectral imaging in different domains, including (but not limited to) the following:

  • Agriculture: disease, pests, food quality, land condition assessment, etc.;
  • Environment: forests, lakes, and damage assessment;
  • Defence: surveillance, search and rescue, and targeting;
  • Medical: lesion and tissue condition analysis;
  • Other: mining, archeology, and geology surveys.

Dr. Subrata Chakraborty
Guest Editor

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. Sensors 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 2600 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 imaging
  • band selection, data compression, information fusion, and data visualization
  • model/algorithm development using machine learning/deep learning
  • new sensor technology and hardware

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Published Papers (1 paper)

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Research

24 pages, 4433 KiB  
Article
ARM-Net: A Tri-Phase Integrated Network for Hyperspectral Image Compression
by Qizhi Fang, Zixuan Wang, Jingang Wang and Lili Zhang
Sensors 2025, 25(6), 1843; https://doi.org/10.3390/s25061843 - 16 Mar 2025
Viewed by 397
Abstract
Most current hyperspectral image compression methods rely on well-designed modules to capture image structural information and long-range dependencies. However, these modules tend to increase computational complexity exponentially with the number of bands, which limits their performance under constrained resources. To address these challenges, [...] Read more.
Most current hyperspectral image compression methods rely on well-designed modules to capture image structural information and long-range dependencies. However, these modules tend to increase computational complexity exponentially with the number of bands, which limits their performance under constrained resources. To address these challenges, this paper proposes a novel triple-phase hybrid framework for hyperspectral image compression. The first stage utilizes an adaptive band selection technique to sample the raw hyperspectral image, which mitigates the computational burden. The second stage concentrates on high-fidelity compression, efficiently encoding both spatial and spectral information within the sampled band clusters. In the final stage, a reconstruction network compensates for sampling-induced losses to precisely restore the original spectral details. The proposed framework, known as ARM-Net, is evaluated on seven mixed hyperspectral datasets. Compared to state-of-the-art methods, ARM-Net achieves an overall improvement of approximately 1–2 dB in both the peak signal-to-noise ratio and multiscale structural similarity index measure, as well as a reduction in the average spectral angle mapper of approximately 0.1. Full article
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