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Remote Sensing
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Spectral Unmixing of Hyperspectral Remote Sensing Imagery II
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Spectral Unmixing of Hyperspectral Remote Sensing Imagery II

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 (10 May 2023) | Viewed by 6241

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
Special Issues, Collections and Topics 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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hengchao Li

E-Mail Website
Guest Editor
School of Information Science & Technology, Southwest Jiaotong University, Chengdu 610031, China
Interests: statistical analysis of synthetic aperture radar (SAR) images; remote sensing image processing; pattern recognition; remote sensing applications; applied Earth observations and remote sensing; hyperspectral image classification; geoscience and remote sensing

Special Issue Information

Dear Colleagues,

Hyperspectral imaging measures the objects on the Earth’s surface in hundreds or thousands of spectral channels, and offers thereby far better ability to identify the class of land cover materials which are often indistinguishable in the visible domain. However, due to the typical low spatial resolution of hyperspectral images (HSIs) and the resulting homogeneously mixed materials, the acquired spectrum of a single pixel may be a combination of the spectral signatures of multiple materials, resulting in 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 modelling theories, methodologies, algorithms and optimizations that are developed for the 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;
  • Robust unmixing to spectral variability or outlier;
  • New data sets with reference data for validation of unmixing models;
  • Methods of abundance estimation.

Dr. Shaoguang Huang
Prof. Dr. Hongyan Zhang
Prof. Dr. Hengchao Li
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

  • endmember extraction
  • hyperspectral images
  • remote sensing
  • spectral unmixing
  • inverse problems
  • optimization
  • machine learning
  • deep learning
  • blind unmixing
  • spectral libraries

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Published Papers (3 papers)

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Research

24 pages, 9240 KiB  
Article
Robust Dual Spatial Weighted Sparse Unmixing for Remotely Sensed Hyperspectral Imagery
by Chengzhi Deng, Yonggang Chen, Shaoquan Zhang, Fan Li, Pengfei Lai, Dingli Su, Min Hu and Shengqian Wang
Remote Sens. 2023, 15(16), 4056; https://doi.org/10.3390/rs15164056 - 16 Aug 2023
Cited by 5 | Viewed by 1485
Abstract
Sparse unmixing plays a crucial role in the field of hyperspectral image unmixing technology, leveraging the availability of pre-existing endmember spectral libraries. In recent years, there has been a growing trend in incorporating spatial information from hyperspectral images into sparse unmixing models. There [...] Read more.
Sparse unmixing plays a crucial role in the field of hyperspectral image unmixing technology, leveraging the availability of pre-existing endmember spectral libraries. In recent years, there has been a growing trend in incorporating spatial information from hyperspectral images into sparse unmixing models. There is a strong spatial correlation between pixels in hyperspectral images (that is, the spatial information is very rich), and many sparse unmixing algorithms take advantage of this to improve the sparse unmixing effect. Since hyperspectral images are susceptible to noise, the feature separability of ground objects is reduced, which makes most sparse unmixing methods and models face the risk of degradation or even failure. To address this challenge, a novel robust dual spatial weighted sparse unmixing algorithm (RDSWSU) has been proposed for hyperspectral image unmixing. This algorithm effectively utilizes the spatial information present in the hyperspectral images to mitigate the impact of noise during the unmixing process. For the proposed RDSWSU algorithm, which is based on ℓ1 sparse unmixing framework, a pre-calculated superpixel spatial weighting factor is used to smooth the noise, so as to maintain the original spatial structure of hyperspectral images. The RDSWSU algorithm, which builds upon the ℓ1 sparse unmixing framework, employs a pre-calculated spatial weighting factor at the superpixel level. This factor aids in noise smoothing and helps preserve the inherent spatial structure of hyperspectral images throughout the unmixing process. Additionally, another spatial weighting factor is utilized in the RDSWSU algorithm to capture the local smoothness of abundance maps at the sub-region level. This factor helps enhance the representation of piecewise smooth variations within different regions of the hyperspectral image. Specifically, the combination of these two spatial weighting factors in the RDSWSU algorithm results in an enhanced sparsity of the abundance matrix. The RDSWSU algorithm, which is a sparse unmixing model, offers an effective solution using the alternating direction method of multiplier (ADMM) with reduced requirements for tuning the regularization parameter. The proposed RDSWSU method outperforms other advanced sparse unmixing algorithms in terms of unmixing performance, as demonstrated by the experimental results on synthetic and real hyperspectral datasets. Full article
(This article belongs to the Special Issue Spectral Unmixing of Hyperspectral Remote Sensing Imagery II)
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18 pages, 13699 KiB  
Article
Estimation and Validation of Sub-Pixel Needleleaf Cover Fraction in the Boreal Forest of Alaska to Aid Fire Management
by Anushree Badola, Santosh K. Panda, David R. Thompson, Dar A. Roberts, Christine F. Waigl and Uma S. Bhatt
Remote Sens. 2023, 15(10), 2484; https://doi.org/10.3390/rs15102484 - 9 May 2023
Cited by 1 | Viewed by 2710
Abstract
Wildfires, which are a natural part of the boreal ecosystem in Alaska, have recently increased in frequency and size. Environmental conditions (high temperature, low precipitation, and frequent lightning events) are becoming favorable for severe fire events. Fire releases greenhouse gasses such as carbon [...] Read more.
Wildfires, which are a natural part of the boreal ecosystem in Alaska, have recently increased in frequency and size. Environmental conditions (high temperature, low precipitation, and frequent lightning events) are becoming favorable for severe fire events. Fire releases greenhouse gasses such as carbon dioxide into the environment, creating a positive feedback loop for warming. Needleleaf species are the dominant vegetation in boreal Alaska and are highly flammable. They burn much faster due to the presence of resin, and their low-lying canopy structure facilitates the spread of fire from the ground to the canopy. Knowing the needleleaf vegetation distribution is crucial for better forest and wildfire management practices. Our study focuses on needleleaf fraction mapping using a well-documented spectral unmixing approach: multiple endmember spectral mixture analysis (MESMA). We used an AVIRIS-NG image (5 m), upscaled it to 10 m and 30 m spatial resolutions, and applied MESMA to all three images to assess the impact of spatial resolution on sub-pixel needleleaf fraction estimates. We tested a novel method to validate the fraction maps using field data and a high-resolution classified hyperspectral image. Our validation method produced needleleaf cover fraction estimates with accuracies of 73%, 79%, and 78% for 5 m, 10 m, and 30 m image data, respectively. To determine whether these accuracies varied significantly across different spatial scales, we used the McNemar statistical test and found no significant differences between the accuracies. The findings of this study enhance the toolset available to fire managers to manage wildfire and for understanding changes in forest demography in the boreal region of Alaska across the high-to-moderate resolution scale. Full article
(This article belongs to the Special Issue Spectral Unmixing of Hyperspectral Remote Sensing Imagery II)
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18 pages, 3886 KiB  
Article
Energy-Based Unmixing Method for Low Background Concentration Oil Spills at Sea
by Huimin Lu, Ying Li and Bingxin Liu
Remote Sens. 2023, 15(8), 2079; https://doi.org/10.3390/rs15082079 - 14 Apr 2023
Viewed by 1396
Abstract
Marine oil spills have caused severe environmental pollution with long-term toxic effects on marine ecosystems and coastal habitants. Hyperspectral remote sensing is currently used in efforts to respond to oil spills. Spectral unmixing plays a key role in hyperspectral imaging because of its [...] Read more.
Marine oil spills have caused severe environmental pollution with long-term toxic effects on marine ecosystems and coastal habitants. Hyperspectral remote sensing is currently used in efforts to respond to oil spills. Spectral unmixing plays a key role in hyperspectral imaging because of its ability to extract accurate fractional abundances of constituent materials from spectrums collected by sensors. However, multiple oil-propagating processes provide different mixing states of oil and water, thereby involving complicated, nonlinear mixing effects between in-depth elements in water, especially those with a low concentration. Therefore, an accurate inversion of material abundance remains a challenging yet fundamental task. This study proposes an unmixing method with normalizers in a combined polynomial and sine model to resolve overfitting problems. An energy information-based wavelet package scheme effectively highlights the latent information of the concerned material. Experimental analyses of synthetic and real data indicate that the proposed method shows superior unmixing performance, especially in delivering more accurate abundance estimations of different background oil concentration levels as low as a fractional abundance of 10−5, and can be used for long-term monitoring of oil propagation. Full article
(This article belongs to the Special Issue Spectral Unmixing of Hyperspectral Remote Sensing Imagery II)
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