Special Issue "Hyperspectral Imaging 2020"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (26 January 2020).

Special Issue Editors

Prof. Stephen Marshall
Website
Guest Editor
Deputy Head of Electronic and Electrical Engineering, Director of the Hyperspectral Imaging (HSI) Centre; University of Strathclyde, U.K.
Interests: digital image processing and computer vision; hyperspectral Imaging; non-linear image processing; mathematical morphology
Dr. Wenzhi Liao
Website
Guest Editor
Lecturer, Department of Electronic and Electrical Engineering, University of Strathclyde, UK
Interests: remote sensing, hyperspectral imaging, image processing, machine learning, data fusion
Special Issues and Collections in MDPI journals
Prof. Zheng Wang
Website
Guest Editor
Associate Professor, College of Intelligence and Computing, Tianjin University, Tianjin, China
Interests: hyperspectral imaging, video analytics, machine learning
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

As one of the fastest-developing research areas in image processing, hyperspectral imaging (HSI) has facilitated a wide range of applications. These span from remote sensing-based mining, precision agriculture, and environmental monitoring, to more industry-based applications, including food/drink, smart manufacturing and pharmaceutical quality inspection/grading, medical applications, and even artwork authentication. With the increasing demands from numerous industrial fields, more efficient and effective algorithms, as well as data analysis techniques, that can handle vast amounts of data for hyperspectral imagery under various conditions, become more pressing.

Particular challenges in the processing of hyperspectral data are how to deal with the high-volume data, yet with limited spatial or spectral (for multispectral systems) resolutions, especially for reducing the data, enhancing the (spatial) resolution and fusion of the spatial and spectral information for improved data prediction (i.e., classification and regression analysis). Many algorithms and techniques have been proposed and are continuously needed for addressing such challenges.

The goal of this Special Issue is to provide a premier forum for researchers working on the aforementioned HSI challenges and related applications, as well as to provide an important opportunity for multidisciplinary development. Future trends and perspectives will be a particular focus of this Special Issue. The topics of interest include, but are not limited to, the following:

  • New sensors and sensing techniques;
  • Optimized data sampling and acquisition;
  • Techniques for effective pre-processing (e.g., denoising/deblurring and data compression);
  • Sparse representation and data reduction;
  • Feature transform and feature extraction;
  • Band selection and optimization;
  • Spectral unmixing and super-resolution;
  • Visualization and retrieval;
  • New techniques for data classification, target detection, and so on;
  • Emerging applications in precision agriculture, nondestructive inspection, urban planning, medical imaging, and so on.

Dr. Jinchang Ren
Prof. Stephen Marshall
Dr. Wenzhi Liao
Prof. Zheng Wang
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 papers will be 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. Applied Sciences 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 1800 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 classification
  • Compressed sensing
  • Deep learning
  • Spatial–spectral fusion
  • Sparse representation
  • Remote sensing
  • Precision agriculture
  • Nondestructive inspection
  • Data visualization…

Published Papers (1 paper)

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Research

Open AccessArticle
Joint Spatial-Spectral Smoothing in a Minimum-Volume Simplex for Hyperspectral Image Super-Resolution
Appl. Sci. 2020, 10(1), 237; https://doi.org/10.3390/app10010237 - 27 Dec 2019
Cited by 1
Abstract
The limitations of hyperspectral sensors usually lead to coarse spatial resolution of acquired images. A well-known fusion method called coupled non-negative matrix factorization (CNMF) often amounts to an ill-posed inverse problem with poor anti-noise performance. Moreover, from the perspective of matrix decomposition, the [...] Read more.
The limitations of hyperspectral sensors usually lead to coarse spatial resolution of acquired images. A well-known fusion method called coupled non-negative matrix factorization (CNMF) often amounts to an ill-posed inverse problem with poor anti-noise performance. Moreover, from the perspective of matrix decomposition, the matrixing of remotely-sensed cubic data results in the loss of data’s structural information, which causes the performance degradation of reconstructed images. In addition to three-dimensional tensor-based fusion methods, Craig’s minimum-volume belief in hyperspectral unmixing can also be utilized to restore the data structure information for hyperspectral image super-resolution. To address the above difficulties simultaneously, this article incorporates the regularization of joint spatial-spectral smoothing in a minimum-volume simplex, and spatial sparsity—into the original CNMF, to redefine a bi-convex problem. After the convexification of the regularizers, the alternating optimization is utilized to decouple the regularized problem into two convex subproblems, which are then reformulated by separately vectorizing the variables via vector-matrix operators. The alternating direction method of multipliers is employed to split the variables and yield the closed-form solutions. In addition, in order to solve the bottleneck of high computational burden, especially when the size of the problem is large, complexity reduction is conducted to simplify the solutions with constructed matrices and tensor operators. Experimental results illustrate that the proposed algorithm outperforms state-of-the-art fusion methods, which verifies the validity of the new fusion approach in this article. Full article
(This article belongs to the Special Issue Hyperspectral Imaging 2020)
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