A nonlinear regression classification algorithm with small sample set for hyperspectral image

Author

Jiayi Li ; Hongyan Zhang ; Liangpei Zhang

Author_Institution

State Key Lab. of Inf. Eng. in Surveying, Mapping, & Remote Sensing, Wuhan Univ., Wuhan, China

fYear

2013

fDate

21-26 July 2013

Firstpage

441

Lastpage

444

Abstract

A column generation kernel technology based nonlinear regression classification method for hyperspectral image is proposed in this paper. The nonlinear extension for the collaborative representation regression is utilized in the joint collaboration model framework. The proposed algorithm is tested on two hyperspectral images. Experimental results suggest that the proposed nonlinear algorithm shows superior performance over other linear regression-based algorithms and the classical hyperspectral classifier SVM.

Keywords

geophysical image processing; hyperspectral imaging; remote sensing; SVM; classical hyperspectral classifier; collaborative representation regression; column generation kernel technology; hyperspectral image; joint collaboration model framework; linear regression-based algorithms; nonlinear algorithm superior performance; nonlinear extension; nonlinear regression classification algorithm; nonlinear regression classification method; small sample set; Classification algorithms; Collaboration; Hyperspectral imaging; Joints; Kernel; Training; collaborative representation; column generation; hyperspectral image classification; kernel;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International

Conference_Location

Melbourne, VIC

ISSN

2153-6996

Print_ISBN

978-1-4799-1114-1

Type

conf

DOI

10.1109/IGARSS.2013.6721187

Filename

6721187