A machine learning approach for finding hyperspectral endmembers

Author

Banerjee, Amit ; Burlina, Philippe ; Broadwater, Joshua

Author_Institution

Johns Hopkins Univ., Laurel

fYear

2007

fDate

23-28 July 2007

Firstpage

3817

Lastpage

3820

Abstract

A support vector algorithm for detecting endmembers in a hyperspectral image is introduced. It is a novel method for finding the spectral convexities in a high-dimensional space which addresses several limitations of previous endmember methods. A new approach for estimating the number of endmembers using rate-distortion theory is also presented. It is based upon the observation that the endmembers form a set of basis vectors for the hyperspectral datacube using the linear mixture model. The result is a fully-automatic method for endmember detection. Experimental results using the Cuprite datacube are presented.

Keywords

learning (artificial intelligence); signal processing; support vector machines; Cuprite datacube; automatic endmember detection method; high dimensional space; hyperspectral datacube basis vector; hyperspectral endmember extraction; hyperspectral image; linear mixture model; machine learning; rate distortion theory; spectral convexity; support vector algorithm; Automation; Classification algorithms; Data mining; Distributed computing; Educational institutions; Hyperspectral imaging; Machine learning; Principal component analysis; Rate-distortion; Vectors; endmember extraction; hyperspectral processing; support vector methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International

Conference_Location

Barcelona

Print_ISBN

978-1-4244-1211-2

Electronic_ISBN

978-1-4244-1212-9

Type

conf

DOI

10.1109/IGARSS.2007.4423675

Filename

4423675