Endmember Finding in Compressively Sensed Band Domain

Chein I. Chang, Adam Bekit

研究成果: Chapter

1 引文 斯高帕斯(Scopus)

摘要

Endmember finding is a crucial step prior to spectral unmixing for hyperspectral imagery where a well-known N-finder algorithm (N-FINDR) has been widely used for this purpose. Since N-FINDR must find all endmembers simultaneously, it requires exhausting all possible p-endmember combinations among the entire data samples where p is the total number of endmembers required to be found. Accordingly, directly implementing N-FINDR is practically infeasible. To mitigate this dilemma, two recently developed algorithms called sequential N-FINDR (SQ N-FINDR) and successive N-FINDR (SC N-FINDR) were developed to make N-FINDR numerically implementable. In addition, another remaining and challenging unsolved issue for N-FINDR is spectral dimensionality reduction. Due to the fact that a p-vertex simplex is embedded in a (p-1)-dimensional spectral data space, N-FINDR does not require full spectral dimensionality to calculate simplex volume (SV). This chapter presents a compressive sensing (CS) approach to N-FINDR that can find a p-vertex simplex with the maximal SV found by SQ/SC N-FINDR in a compressively sensed band domain (CSBD). In particular, to make this idea work, a new CS-based property, called restricted simplex volume property (RSVP), is further shown to be preserved in CSBD via a sensing matrix. It is this property to guarantee that what N-FINDR and SQ/SC N-FINDR can achieve in the original data space (ODS) can be also achieved in CSBD. To demonstrate the utility of SQ/SC N-FINDR in both ODS and CSBD preserved by RSVP, a series of experiments are conducted for performance analysis.

原文English
主出版物標題Advances in Hyperspectral Image Processing Techniques
發行者Wiley-Blackwell
頁面228-251
頁數24
ISBN(列印)9781119687788
DOIs
出版狀態Published - 2022 11月 11

All Science Journal Classification (ASJC) codes

  • 一般工程

指紋

深入研究「Endmember Finding in Compressively Sensed Band Domain」主題。共同形成了獨特的指紋。

引用此