Special Issue "Spectral Unmixing of Hyperspectral Remote Sensing Imagery"

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

Deadline for manuscript submissions: 1 October 2021.

Special Issue Editors

Dr. Shaoguang Huang
E-Mail Website
Guest Editor
Department of Telecommunications and Information Processing, Ghent University, 9000 Gent, Belgium
Interests: sparse modelling; classification; clustering; image processing; machine learning; remote sensing
Prof. Dr. Aleksandra Pizurica
E-Mail Website
Guest Editor
Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium
Interests: statistical image modeling; sparse representation; image restoration and reconstruction; analysis of high-dimensional data; machine learning
Special Issues and Collections in MDPI journals
Prof. Dr. Hongyan Zhang
E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: image reconstruction; hyperspectral image processing; sparse representation; low rank representation; remote sensing; machine learning; deep learning
Dr. Mauro Dalla Mura
E-Mail Website
Guest Editor
GIPSA-Lab, Grenoble Institute of Technology, 38402 Saint Martin d'Hères, France
Interests: remote sensing; image processing; signal processing; machine learning; mathematical morphology; data fusion; multivariate data analysis; hyperspectral imaging; pansharpening
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

A large number of hyperspectral images (HSIs) are acquired on a daily basis from various Earth observation airborne and spaceborne systems. They measure the objects on the Earth’s surface in hundreds or thousands of spectral channels and thus offer a far better ability to identify the class of land cover materials which are often indistinguishable in the visible domain. This makes HSIs an essential tool in remote sensing finding numerous applications such as in environmental monitoring, precision agriculture, defense and security, etc. However, due to the typical low spatial resolution of HSIs and resulting homogeneously mixed materials, the acquired spectrum of a single pixel may be a combination of spectral signatures of multiple materials, resulting in a mixed spectrum. This makes the processing, analysis, and interpretation of HSIs difficult tasks.

Spectral unmixing addresses this problem by identifying the constituent pure materials, also called endmembers, and their corresponding fractional abundances present in the pixel. Unmixing is an ill-posed inverse problem. Although the spectral unmixing problem has been widely studied over the last fifty years, it remains an active and important research topic in the fields of remote sensing. The goal of this Special Issue of Remote Sensing is to track the latest progress in modeling theories, methodologies, algorithms, and optimizations that are developed for spectral unmixing of hyperspectral remote sensing images. Authors are invited to submit high-quality, original research papers on the topics including, but not limited to, the following:

  • Endmember extraction;
  • Estimating the number of endmembers;
  • Unmixing models (linear or non-linear);
  • Spectral unmixing with side information from other data sources;
  • Large-scale spectral unmixing models;
  • Spectral unmixing with deep learning;
  • Applications of spectral unmixing;
  • Blind unmixing;
  • Unmixing considering spectral variability or outlier

Dr. Shaoguang Huang
Prof. Aleksandra Pizurica
Prof. Hongyan Zhang
Prof. Mauro Dalla Mura
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. 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 2400 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

  • Endmember extraction
  • hyperspectral images
  • remote sensing
  • spectral unmixing
  • inverse problems
  • optimization
  • machine learning
  • deep learning

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Other

Article
Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing Based on Endmember Independence and Spatial Weighted Abundance
Remote Sens. 2021, 13(12), 2348; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122348 - 16 Jun 2021
Viewed by 365
Abstract
Hyperspectral image unmixing is an important task for remote sensing image processing. It aims at decomposing the mixed pixel of the image to identify a set of constituent materials called endmembers and to obtain their proportions named abundances. Recently, number of algorithms based [...] Read more.
Hyperspectral image unmixing is an important task for remote sensing image processing. It aims at decomposing the mixed pixel of the image to identify a set of constituent materials called endmembers and to obtain their proportions named abundances. Recently, number of algorithms based on sparse nonnegative matrix factorization (NMF) have been widely used in hyperspectral unmixing with good performance. However, these sparse NMF algorithms only consider the correlation characteristics of abundance and usually just take the Euclidean structure of data into account, which can make the extracted endmembers become inaccurate. Therefore, with the aim of addressing this problem, we present a sparse NMF algorithm based on endmember independence and spatial weighted abundance in this paper. Firstly, it is assumed that the extracted endmembers should be independent from each other. Thus, by utilizing the autocorrelation matrix of endmembers, the constraint based on endmember independence is to be constructed in the model. In addition, two spatial weights for abundance by neighborhood pixels and correlation coefficient are proposed to make the estimated abundance smoother so as to further explore the underlying structure of hyperspectral data. The proposed algorithm not only considers the relevant characteristics of endmembers and abundances simultaneously, but also makes full use of the spatial-spectral information in the image, achieving a more desired unmixing performance. The experiment results on several data sets further verify the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Spectral Unmixing of Hyperspectral Remote Sensing Imagery)
Show Figures

Graphical abstract

Article
Endmember Estimation with Maximum Distance Analysis
Remote Sens. 2021, 13(4), 713; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040713 - 15 Feb 2021
Cited by 1 | Viewed by 647
Abstract
Endmember estimation plays a key role in hyperspectral image unmixing, often requiring an estimation of the number of endmembers and extracting endmembers. However, most of the existing extraction algorithms require prior knowledge regarding the number of endmembers, being a critical process during unmixing. [...] Read more.
Endmember estimation plays a key role in hyperspectral image unmixing, often requiring an estimation of the number of endmembers and extracting endmembers. However, most of the existing extraction algorithms require prior knowledge regarding the number of endmembers, being a critical process during unmixing. To bridge this, a new maximum distance analysis (MDA) method is proposed that simultaneously estimates the number and spectral signatures of endmembers without any prior information on the experimental data containing pure pixel spectral signatures and no noise, being based on the assumption that endmembers form a simplex with the greatest volume over all pixel combinations. The simplex includes the farthest pixel point from the coordinate origin in the spectral space, which implies that: (1) the farthest pixel point from any other pixel point must be an endmember, (2) the farthest pixel point from any line must be an endmember, and (3) the farthest pixel point from any plane (or affine hull) must be an endmember. Under this scenario, the farthest pixel point from the coordinate origin is the first endmember, being used to create the aforementioned point, line, plane, and affine hull. The remaining endmembers are extracted by repetitively searching for the pixel points that satisfy the above three assumptions. In addition to behaving as an endmember estimation algorithm by itself, the MDA method can co-operate with existing endmember extraction techniques without the pure pixel assumption via generalizing them into more effective schemes. The conducted experiments validate the effectiveness and efficiency of our method on synthetic and real data. Full article
(This article belongs to the Special Issue Spectral Unmixing of Hyperspectral Remote Sensing Imagery)
Show Figures

Graphical abstract

Article
Shadow Detection and Restoration for Hyperspectral Images Based on Nonlinear Spectral Unmixing
Remote Sens. 2020, 12(23), 3985; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233985 - 05 Dec 2020
Cited by 3 | Viewed by 768 | Correction
Abstract
Shadows are frequently observable in high-resolution images, raising challenges in image interpretation, such as classification and object detection. In this paper, we propose a novel framework for shadow detection and restoration of atmospherically corrected hyperspectral images based on nonlinear spectral unmixing. The mixture [...] Read more.
Shadows are frequently observable in high-resolution images, raising challenges in image interpretation, such as classification and object detection. In this paper, we propose a novel framework for shadow detection and restoration of atmospherically corrected hyperspectral images based on nonlinear spectral unmixing. The mixture model is applied pixel-wise as a nonlinear combination of endmembers related to both pure sunlit and shadowed spectra, where the former are manually selected from scenes and the latter are derived from sunlit spectra following physical assumptions. Shadowed pixels are restored by simulating their exposure to sunlight through a combination of sunlit endmembers spectra, weighted by abundance values. The proposed framework is demonstrated on real airborne hyperspectral images. A comprehensive assessment of the restored images is carried out both visually and quantitatively. With respect to binary shadow masks, our framework can produce soft shadow detection results, keeping the natural transition of illumination conditions on shadow boundaries. Our results show that the framework can effectively detect shadows and restore information in shadowed regions. Full article
(This article belongs to the Special Issue Spectral Unmixing of Hyperspectral Remote Sensing Imagery)
Show Figures

Graphical abstract

Other

Jump to: Research

Correction
Correction: Zhang, G., et al. Shadow Detection and Restoration for Hyperspectral Images Based on Nonlinear Spectral Unmixing. Remote Sensing 2020, 12, 3985
Remote Sens. 2021, 13(3), 473; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030473 - 29 Jan 2021
Viewed by 482
Abstract
The authors would like to make the following correction of [...] Full article
(This article belongs to the Special Issue Spectral Unmixing of Hyperspectral Remote Sensing Imagery)
Back to TopTop