ICE: a new method for the multivariate curve resolution of hyperspectral images

被引：16

作者：

Berman, Mark ^{[2
]}

Phatak, Aloke ^{[1
]}

Lagerstrom, Ryan ^{[2
]}

Wood, Bayden R. ^{[3
]}

机构：

[1] CSIRO Math & Informat Sci, Wembley, WA 6913, Australia

[2] CSIRO Math & Informat Sci, N Ryde, NSW 1670, Australia

[3] Monash Univ, Sch Chem, Ctr Biospect, Clayton, Vic 3800, Australia

来源：

JOURNAL OF CHEMOMETRICS | 2009年 / 23卷 / 1-2期

基金：

英国医学研究理事会;

关键词：

multivariate curve resolution; MNF transform; unmixing; hyperspectral; simplex; convex geometry; STATISTICAL APPROACH; FEASIBLE SOLUTIONS; LOCAL RANK; SPECTROSCOPY; PROTEINS;

D O I：

10.1002/cem.1198

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The iterated constrained endmembers (ICE) algorithm is a new method of unmixing hyperspectral images that combines aspects of multivariate curve resolution (MCR) methods in chemometrics and unmixing algorithms in remote sensing. Like many MCR methods, ICE also estimates pure components, or endmembers, via alternating least squares; however, it is explicitly based on a convex geometry model and estimation is carried out in a subspace of reduced dimensionality defined by the minimum noise fraction (MNF) transform. In this paper, we describe the ICE algorithm and its properties. We also illustrate its use on a hyperspectral image of cervical tissue. The unmixing of hyperspectral images presents some unique challenges, and we also outline where further development is required. Copyright (C) 2008 John Wiley & Sons, Ltd.

引用

页码：101 / 116

页数：16

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