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Advances in Unmixing of Spectral Imagery

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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 (31 December 2019) | Viewed by 16116

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Special Issue Editors

Prof. Dr. Miguel Velez-Reyes

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Guest Editor

Electrical and Computer Engineering Department, The University of Texas at El Paso, 500W University Avenue, El Paso, TX 79968, USA
Interests: information extraction using hyper/multispectral remote sensing; advanced mathematical, computational and machine learning approaches for spectral image exploitation; applications of hyper/multispectral remote sensing

Prof. Dr. David W. Messinger

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Guest Editor

Professor and Director, Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, 54 Lomb Memorial Drive, Rochester, NY 14623, USA
Interests: remote sensing image exploitation; advanced mathematical approaches for spectral image processing; target detection in hyperspectral imagery
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Special Issue Information

Dear Colleagues

Hyperspectral remote sensing collects fully registered spatial and spectral information that allows discrimination between remotely sensed objects on the ground due to their unique spectral signatures. One important issue in the imaging process is that the collected radiation represented by a single pixel in the hyperspectral image rarely comes from the interaction with a single homogeneous material. The mixed signature may be caused by multiple objects in the sensor IFOV or by sensing of a heterogeneous surface. The high spectral resolution, however, enables the detection, identification, and classification of constituent materials inside the pixel from their contribution to the measured spectral signal. A standard approach to subpixel information extraction is spectral unmixing, where the measured pixel spectrum is decomposed into a collection of constituent spectra, or endmembers, and information about their abundances. This is an ill-posed problem and its solution heavily depends on the modeling assumptions about the mixing process.

The primary goal of this Special Issue of Remote Sensing is to provide a forum for the discussion of the latest advances in modeling theories, methodologies and techniques, and applications of spectral unmixing. A list of topics of interest includes, but not limited, to the following

Spectral mixing modeling (linear, nonlinear)
Endmember extraction algorithms and approaches for learning endmembers from data
Novel algorithms for abundance estimation
Unsupervised and semi-supervised algorithms for unmixing
Probabilistic methods for unmixing
Feature extraction and dimensionality reduction for unmixing
Partial unmixing and subpixel material detection
Methodologies to quantify the accuracy of unmixing results
Development of spectral libraries
Data sets with reference data for testing and validation of unmixing algorithms
Experimental approaches for unmixing
Spatial resolution enhancement by fusing unmixing results and high spatial resolution multispectral data
Applications of unmixing (e.g. urban, agriculture, environment, land cover, benthic habitat mapping, space situational awareness, extraterrestrial space exploration, etc.)

Authors are encouraged to share data sets and codes for other researchers to replicate results to enable collaborations and future developments.

Authors are requested to check and follow the Instructions to Authors, see https://0-www-mdpi-com.brum.beds.ac.uk/journal/remotesensing/instructions.

We look forward to receiving your submissions in this interesting topic.

Prof. Dr. Miguel Velez-Reyes
Prof. Dr. David Messinger
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 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

Spectral remote sensing
Linear and nonlinear mixing modeling
Spectral unmixing algorithms
Abundance estimation
Spectral libraries
Applications of unmixing
Subpixel analysis
Unmixing applications

Published Papers (5 papers)

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23 pages, 3308 KiB

Open AccessArticle

A Supervised Method for Nonlinear Hyperspectral Unmixing

by Bikram Koirala, Mahdi Khodadadzadeh, Cecilia Contreras, Zohreh Zahiri, Richard Gloaguen and Paul Scheunders

Remote Sens. 2019, 11(20), 2458; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11202458 - 22 Oct 2019

Cited by 13 | Viewed by 4754

Abstract

Due to the complex interaction of light with the Earth’s surface, reflectance spectra can be described as highly nonlinear mixtures of the reflectances of the material constituents occurring in a given resolution cell of hyperspectral data. Our aim is to estimate the fractional abundance maps of the materials from the nonlinear hyperspectral data. The main disadvantage of using nonlinear mixing models is that the model parameters are not properly interpretable in terms of fractional abundances. Moreover, not all spectra of a hyperspectral dataset necessarily follow the same particular mixing model. In this work, we present a supervised method for nonlinear spectral unmixing. The method learns a mapping from a true hyperspectral dataset to corresponding linear spectra, composed of the same fractional abundances. A simple linear unmixing then reveals the fractional abundances. To learn this mapping, ground truth information is required, in the form of actual spectra and corresponding fractional abundances, along with spectra of the pure materials, obtained from a spectral library or available in the dataset. Three methods are presented for learning nonlinear mapping, based on Gaussian processes, kernel ridge regression, and feedforward neural networks. Experimental results conducted on an artificial dataset, a data set obtained by ray tracing, and a drill core hyperspectral dataset shows that this novel methodology is very promising. Full article

(This article belongs to the Special Issue Advances in Unmixing of Spectral Imagery)

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30 pages, 10469 KiB

Open AccessArticle

Improved Spatial-Spectral Superpixel Hyperspectral Unmixing

by Mohammed Q. Alkhatib and Miguel Velez-Reyes

Remote Sens. 2019, 11(20), 2374; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11202374 - 13 Oct 2019

Cited by 10 | Viewed by 3295

Abstract

In this paper, an unsupervised unmixing approach based on superpixel representation combined with regional partitioning is presented. A reduced-size image representation is obtained using superpixel segmentation where each superpixel is represented by its mean spectra. The superpixel image representation is then partitioned into regions using quadtree segmentation based on the Shannon entropy. Spectral endmembers are extracted from each region that corresponds to a leaf of the quadtree and combined using clustering into endmember classes. The proposed approach is tested and validated using the HYDICE Urban and ROSIS Pavia data sets. Different levels of qualitative and quantitative assessments are performed based on the available reference data. The proposed approach is also compared with global (no-regional quadtree segmentation) and with pixel-based (no-superpixel representation) unsupervised unmixing approaches. Qualitative assessment was based primarily on agreement with spatial distribution of materials obtained from a reference classification map. Quantitative assessment was based on comparing classification maps generated from abundance maps using winner takes it all with a 50% threshold and a reference classification map. High agreement with the reference classification map was obtained by the proposed approach as evidenced by high kappa values (over 70%). The proposed approach outperforms global unsupervised unmixing approaches with and without superpixel representation that do not account for regional information. The agreement performance of the proposed approach is slightly better when compared to the pixel-based approached using quadtree segmentation. However, the proposed approach resulted in significant computational savings due to the use of the superpixel representation. Full article

(This article belongs to the Special Issue Advances in Unmixing of Spectral Imagery)

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24 pages, 621 KiB

Open AccessArticle

Improved Estimation of the Intrinsic Dimension of a Hyperspectral Image Using Random Matrix Theory

by Mark Berman

Remote Sens. 2019, 11(9), 1049; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11091049 - 03 May 2019

Cited by 6 | Viewed by 2591

Abstract

Many methods have been proposed in the literature for estimating the number of materials/endmembers in a hyperspectral image. This is sometimes called the “intrinsic” dimension (ID) of the image. A number of recent papers have proposed ID estimation methods based on various aspects of random matrix theory (RMT), under the assumption that the errors are uncorrelated, but with possibly unequal variances. A recent paper, which reviewed a number of the better known methods (including one RMT-based method), has shown that they are all biased, especially when the true ID is greater than about 20 or 30, even when the error structure is known. I introduce two RMT-based estimators (

R M T_{G}

, which is new, and

R M T_{K N}

, which is a modification of an existing estimator), which are approximately unbiased when the error variances are known. However, they are biased when the error variance is unknown and needs to be estimated. This bias increases as ID increases. I show how this bias can be reduced. The results use semi-realistic simulations based on three real hyperspectral scenes. Despite this, when applied to the real scenes,

R M T_{G}

and

R M T_{K N}

are larger than expected. Possible reasons for this are discussed, including the presence of errors which are either deterministic, spectrally and/or spatially correlated, or signal-dependent. Possible future research into ID estimation in the presence of such errors is outlined. Full article

(This article belongs to the Special Issue Advances in Unmixing of Spectral Imagery)

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23 pages, 1212 KiB

Open AccessArticle

Parameterized Nonlinear Least Squares for Unsupervised Nonlinear Spectral Unmixing

by Risheng Huang, Xiaorun Li, Haiqiang Lu, Jing Li and Liaoying Zhao

Remote Sens. 2019, 11(2), 148; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11020148 - 14 Jan 2019

Cited by 7 | Viewed by 2446

Abstract

This paper presents a new parameterized nonlinear least squares (PNLS) algorithm for unsupervised nonlinear spectral unmixing (UNSU). The PNLS-based algorithms transform the original optimization problem with respect to the endmembers, abundances, and nonlinearity coefficients estimation into separate alternate parameterized nonlinear least squares problems. Owing to the Sigmoid parameterization, the PNLS-based algorithms are able to thoroughly relax the additional nonnegative constraint and the nonnegative constraint in the original optimization problems, which facilitates finding a solution to the optimization problems . Subsequently, we propose to solve the PNLS problems based on the Gauss–Newton method. Compared to the existing nonnegative matrix factorization (NMF)-based algorithms for UNSU, the well-designed PNLS-based algorithms have faster convergence speed and better unmixing accuracy. To verify the performance of the proposed algorithms, the PNLS-based algorithms and other state-of-the-art algorithms are applied to synthetic data generated by the Fan model and the generalized bilinear model (GBM), as well as real hyperspectral data. The results demonstrate the superiority of the PNLS-based algorithms. Full article

(This article belongs to the Special Issue Advances in Unmixing of Spectral Imagery)

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17 pages, 10500 KiB

Open AccessLetter

A Novel Hyperspectral Endmember Extraction Algorithm Based on Online Robust Dictionary Learning

by Xiaorui Song and Lingda Wu

Remote Sens. 2019, 11(15), 1792; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11151792 - 31 Jul 2019

Cited by 5 | Viewed by 2387

Abstract

Due to the sparsity of hyperspectral images, the dictionary learning framework has been applied in hyperspectral endmember extraction. However, current endmember extraction methods based on dictionary learning are not robust enough in noisy environments. To solve this problem, this paper proposes a novel endmember extraction approach based on online robust dictionary learning, termed EEORDL. Because of the large scale of the hyperspectral image (HSI) data, an online scheme is introduced to reduce the computational time of dictionary learning. In the proposed algorithm, a new form of the objective function is introduced into the dictionary learning process to improve the robustness for noisy HSI data. The experimental results, conducted with both synthetic and real-world hyperspectral datasets, illustrate that the proposed EEORDL outperforms the state-of-the-art approaches under different signal-to-noise ratio (SNR) conditions, especially for high-level noise. Full article

(This article belongs to the Special Issue Advances in Unmixing of Spectral Imagery)

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