Linear spectral unmixing using least squares error, orthogonal projection and simplex volume for hyperspectral images

Hsiao Chi Li, Chein I. Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

Linear spectral unmixing (LSU) and Simplex Volume (SV) are closely related. The link between these two has been recognized recently by the fact that simplex can be realized by two physical abundance constraints, Abundance Sum-to-one Constraint (ASC) and Abundance Non-negativity Constraint (ANC). In other words, all data sample vectors are embraced by a simplex with vertices which are actually the set of signatures used to unmix data sample vectors where the data sample vectors outside the simplex are considered as unwanted sample vectors such as noisy samples, bad sample vectors. On the other hand, LSU is solved by Least Squares Error (LSE) which uses the principle of orthogonality to derive the solution. Therefore, LSU is also equivalent to being solved by Orthogonal Projection (OP). This paper explores applications of LSU using these criteria, simplex, LSE and OP in data unmixing.

Original languageEnglish
Title of host publication2015 7th Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2015
PublisherIEEE Computer Society
ISBN (Electronic)9781467390156
DOIs
Publication statusPublished - 2015 Jul 2
Event7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015 - Tokyo, Japan
Duration: 2015 Jun 22015 Jun 5

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume2015-June
ISSN (Print)2158-6276

Conference

Conference7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015
Country/TerritoryJapan
CityTokyo
Period15-06-0215-06-05

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing

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