Sparse unmixing of hyperspectral data using spectral a priori information

Wei Tang, Zhenwei Shi, Ying Wu, Changshui Zhang

Research output: Contribution to journalArticle

71 Scopus citations

Abstract

Given a spectral library, sparse unmixing aims at finding the optimal subset of endmembers from it to model each pixel in the hyperspectral scene. However, sparse unmixing still remains a challenging task due to the usually high mutual coherence of the spectral library. In this paper, we exploit the spectral a priori information in the hyperspectral image to alleviate this difficulty. It assumes that some materials in the spectral library are known to exist in the scene. Such information can be obtained via field investigation or hyperspectral data analysis. Then, we propose a novel model to incorporate the spectral a priori information into sparse unmixing. Based on the alternating direction method of multipliers, we present a new algorithm, which is termed sparse unmixing using spectral a priori information (SUnSPI), to solve the model. Experimental results on both synthetic and real data demonstrate that the spectral a priori information is beneficial to sparse unmixing and that SUnSPI can exploit this information effectively to improve the abundance estimation.

Original languageEnglish (US)
Article number6840362
Pages (from-to)770-783
Number of pages14
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume53
Issue number2
DOIs
StatePublished - Feb 2015

Keywords

  • Alternating direction method of multipliers (ADMM)
  • hyperspectral unmixing
  • sparse unmixing
  • spectral a priori information

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

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