Feature level fusion for hyperspectral images

Chengzhe Xu; Intaek Kim; Seong G. Kong

doi:10.1117/12.819393

27 April 2009 Feature level fusion for hyperspectral images

Chengzhe Xu, Intaek Kim, Seong G. Kong

Proceedings Volume 7315, Sensing for Agriculture and Food Quality and Safety; 73150N (2009) https://doi.org/10.1117/12.819393
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Chengzhe Xu, Intaek Kim, and Seong G. Kong "Feature level fusion for hyperspectral images", Proc. SPIE 7315, Sensing for Agriculture and Food Quality and Safety, 73150N (27 April 2009); https://doi.org/10.1117/12.819393

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