Share Email Print

Proceedings Paper

Feature level fusion for hyperspectral images
Author(s): Chengzhe Xu; Intaek Kim; Seong G. Kong
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

This paper presents a new method for detecting poultry skin tumors based on serial feature fusion in hyperspectral images. First, some transform methods, including principal component analysis, discrete wavelet transform and band ratio method, are used to generate largely independent datasets in the hyperspectral fluorescence images. Then, the kernel discriminant analysis is utilized to extract features from each represented dataset for the purpose of classification; another set of features are extracted from hyperspectral reflectance images by using kernel discriminant analysis. Finally, new fused features are made by combining aforementioned features. The experimental result based on the proposed method shows the better performance in detecting tumors compared with previous works.

Paper Details

Date Published: 27 April 2009
PDF: 6 pages
Proc. SPIE 7315, Sensing for Agriculture and Food Quality and Safety, 73150N (27 April 2009); doi: 10.1117/12.819393
Show Author Affiliations
Chengzhe Xu, Myongji Univ. (Korea, Republic of)
Intaek Kim, Myongji Univ. (Korea, Republic of)
Seong G. Kong, Temple Univ. (United States)

Published in SPIE Proceedings Vol. 7315:
Sensing for Agriculture and Food Quality and Safety
Moon S. Kim; Shu-I Tu; Kaunglin Chao, Editor(s)

© SPIE. Terms of Use
Back to Top