Endmember Detection using the Dirichlet Process

被引：0

作者：

Zare, Alina ^{[1
]}

Gader, Paul D. ^{[1
]}

机构：

[1] Univ Florida, Dept Comp & Informat Sci & Engn, Gainesville, FL 32611 USA

来源：

19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6 | 2008年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An endmember detection algorithm for hyperspectral imagery using the Dirichlet process to determine the number of endmembers in a hyperspectral image is described. This algorithm provides an estimate of endmember spectra, proportion maps, and the number of endmembers needed for a scene. Updates to the proportion vector for a pixel are sampled using the Dirichlet process. As opposed to previous methods that prune unnecessary endmembers, the proposed algorithm is initialized with one endmember and new endmembers are added through sampling as needed. Results are shown on a two-dimensional dataset and a simulated dataset using endmembers selected from an AVIRIS hyperspectral image.

引用

页码：3799 / 3802

页数：4

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