Albedo recovery for hyperspectral image classification

Kun Zhan; Haibo Wang; Yuange Xie; Chutong Zhang; Yufang Min

doi:10.1117/1.JEI.26.4.043010

26 July 2017 Albedo recovery for hyperspectral image classification

Kun Zhan; Haibo Wang; Yuange Xie; Chutong Zhang; Yufang Min

Author Affiliations +

J. of Electronic Imaging, 26(4), 043010 (2017). https://doi.org/10.1117/1.JEI.26.4.043010

Abstract

Image intensity value is determined by both the albedo component and the shading component. The albedo component describes the physical nature of different objects at the surface of the earth, and land-cover classes are different from each other because of their intrinsic physical materials. We, therefore, recover the intrinsic albedo feature of the hyperspectral image to exploit the spatial semantic information. Then, we use the support vector machine (SVM) to classify the recovered intrinsic albedo hyperspectral image. The SVM tries to maximize the minimum margin to achieve good generalization performance. Experimental results show that the SVM with the intrinsic albedo feature method achieves a better classification performance than the state-of-the-art methods in terms of visual quality and three quantitative metrics.

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Kun Zhan, Kun Zhan, Haibo Wang, Haibo Wang, Yuange Xie, Yuange Xie, Chutong Zhang, Chutong Zhang, Yufang Min, Yufang Min, } "Albedo recovery for hyperspectral image classification," Journal of Electronic Imaging 26(4), 043010 (26 July 2017). https://doi.org/10.1117/1.JEI.26.4.043010 . Submission: Received: 31 March 2017; Accepted: 10 July 2017

Received: 31 March 2017; Accepted: 10 July 2017; Published: 26 July 2017

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