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A Novel Geo-Stat Endmember Extraction Algorithm
TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON), 2019The blind hyperspectral unmixing chain can be divided into three sub-problems. First is subspace identification, second is endmember extraction and third is abundance estimation. Endmember extraction is a very challenging problem in this chain. There are mainly three approaches to extract endmembers: Sparse, Statistical and Geometrical.
Dharambhai Shah, Tanish Zaveri
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An improved full automated endmember extraction algorithm based on endmember independence
SPIE Proceedings, 2015Current algorithms of endmember extraction generally need to determine the number of endmembers manually. However, the number of endmembers is unknown in practical application, so an automated and iterative endmember extraction algorithm is put forward in this paper to solve the problem.
Yiran Wang, Shengwei Zhong, Ye Zhang
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2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009
Fractional abundances predicted for a given pixel using spectral mixture analysis (SMA) are most accurate when only the spectral endmembers that comprise it are used, with larger errors occurring if inappropriate endmembers are included in the mixing process.
Benoit Rivard +3 more
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Fractional abundances predicted for a given pixel using spectral mixture analysis (SMA) are most accurate when only the spectral endmembers that comprise it are used, with larger errors occurring if inappropriate endmembers are included in the mixing process.
Benoit Rivard +3 more
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The extension of endmember extraction to multispectral scenes
SPIE Proceedings, 2004A multiple simplex endmember extraction method has been developed. Unlike convex methods that rely on a single simplex, the number of endmembers is not restricted by the number of linearly independent spectral channels. The endmembers are identified as the extreme points in the data set. The algorithm for finding the endmembers can simultaneously find
John H. Gruninger +2 more
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A New Preprocessing Technique for Fast Hyperspectral Endmember Extraction
IEEE Geoscience and Remote Sensing Letters, 2013Q1
Sebastián López +5 more
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Comparison of hyperspectral endmember extraction algorithms
Journal of Applied Remote Sensing, 2013In hyperspectral imagery, endmember extraction is the process of finding a pure spectrum set within the materials present in a hyperspectral scene. However, various endmember extraction algorithms (EEAs) can yield different endmember spectrum sets. This research presents a comparison of four EEAs: pixel purity index, automatic target generation process
Jee-cheng Wu, Gwo-chyang Tsuei
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Extraction of Endmembers from Spectral Mixtures
Remote Sensing of Environment, 1999Abstract Linear spectral mixture modeling (LSMM) divides each ground resolution element into its constituent materials using endmembers which represent the spectral characteristics of the cover types. However, it is difficult to identify and estimate the spectral signature of pure components or endmembers which form the scene, since they vary with ...
F.J. Garcı́a-Haro +2 more
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Algorithm Research on Endmember Extraction Combined With Distribution Statistics
2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2018The spatial resolution of hyperspectral sensor is limited and the surface features are complicated, and each pixel contains more material information, resulting in the existence of a large number of mixed pixels. Therefore, the research on endmember extraction method have been becoming a hotspot in the hyperspectral field.
Meiping Song, Ming Xu, Chein-I Chang
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Statistical convex partitioning for endmember extraction
SPIE Proceedings, 2010Endmember extraction is the process of selecting a collection of pure signature spectra of the materials present in a hyperspectral scene. Most of the spectral-based endmember extraction methods relay on the ability to discriminate between pixels based on their spectral characteristics and the assumption that pure pixels exist in the image.
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