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Remote Sensing
Special Issues
Recent Applications and Techniques of Hyperspectral Imaging in Remote Sensing
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Special Issue "Recent Applications and Techniques of Hyperspectral Imaging in 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: closed (15 June 2023) | Viewed by 908

Special Issue Editor

Dr. Shih-Yu Chen

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Douliu 64002, Taiwan
Interests: hyperspectral imaging; target detection; smart agriculture; deep learning

Special Issue Information

Dear Colleagues,

Hyperspectral imaging (HSI) has been regarded as an emerging and rapidly developing advanced technology, and its related research and development have also been widely used in various fields, such as agricultural food safety and inspection, land use, land cover classification, ecological/environmental monitoring, forest, and military defense. The main advantage offered by hyperspectral sensors is increased spectral resolution with up to 200 contiguous spectral bands. Thus, hyperspectral sensors can be utilized to detect, classify, differentiate, identify, and quantify small objects and substances. Unfortunately, HSI in some areas is still in the early stages of development in this field, as people have not yet understood its applications.

The aim of this Special Issue is to focus on:

(1) Recent applications of HSI: smart agriculture including detection of diseases, crop growth monitoring, pest and disease identification, farming management, quality evaluation, smart manufacturing including defect detection and product grading, environmental monitoring including toxic wastes and water pollution, food safety including grading and inspection, forest and plantation including species classification, etc.

(2) Techniques of HSI including algorithm design, architecture, and implementation: anomaly detection, target detection, band selection, dimensionality reduction, sparse representation, deep learning, image classification, hyperspectral unmixing, etc.

Research articles, review articles, as well as short communications are all invited.

Dr. Shih-Yu Chen
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. 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 imaging (HSI)
  • smart agriculture
  • smart manufacturing
  • target detection
  • deep learning

Published Papers (1 paper)

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Research

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Article
Multispectral and Hyperspectral Image Fusion Based on Joint-Structured Sparse Block-Term Tensor Decomposition
by
Hao Guo
,
Wenxing Bao
,
Wei Feng
,
Shasha Sun
,
Chunhui Mo
and
Kewen Qu
Remote Sens. 2023, 15(18), 4610; https://doi.org/10.3390/rs15184610 - 19 Sep 2023
Viewed by 481
Abstract
Multispectral and hyperspectral image fusion (MHF) aims to reconstruct high-resolution hyperspectral images by fusing spatial and spectral information. Unlike the traditional canonical polyadic decomposition and Tucker decomposition models, the block-term tensor decomposition model is able to improve the quality of fused images using [...] Read more.
Multispectral and hyperspectral image fusion (MHF) aims to reconstruct high-resolution hyperspectral images by fusing spatial and spectral information. Unlike the traditional canonical polyadic decomposition and Tucker decomposition models, the block-term tensor decomposition model is able to improve the quality of fused images using known endmember and abundance information. This paper presents an improved hyperspectral image fusion algorithm. Firstly, the two abundance matrices are combined into a single bulk matrix to promote structural sparsity by introducing the L2,1-norm to eliminate the scaling effects present in the model. Secondly, the counter-scaling effect is eliminated by adding the L2-norm to the endmember matrix. Finally, the chunk matrix and the endmember matrix are coupled together, and the matrix is reorganized by adding the L2,1-norm to the matrix to facilitate chunk elimination and solved using an extended iterative reweighted least squares (IRLS) method, focusing on the problem of the inability to accurately estimate the tensor rank in the chunk-term tensor decomposition model and the noise/artifact problem arising from overestimation of rank. Experiments are conducted on standard and local datasets, and the fusion results are compared and analyzed in four ways: visual result analysis, metric evaluation, time of the algorithm, and classification results, and the experimental results show that the performance of the proposed method is better than the existing methods. An extensive performance evaluation of the algorithms is performed by conducting experiments on different datasets. The experimental results show that the proposed algorithm achieves significant improvements in terms of reconstruction error, signal-to-noise ratio, and image quality compared with the existing methods. Especially in the case of a low signal-to-noise ratio, the proposed algorithm shows stronger robustness and accuracy. These results show that the proposed algorithm has significant advantages in dealing with multispectral high-resolution hyperspectral data. Full article
(This article belongs to the Special Issue Recent Applications and Techniques of Hyperspectral Imaging in Remote Sensing)
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