Differential weights-based band selection for hyperspectral image classification

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Liu, Yun
Wang, Chen
Wang, Yang
Bai, Xiao
Zhou, Jun
Bai, Lu
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2017
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Abstract

Band selection plays a key role in the hyperspectral image classification since it helps to reduce the expensive cost of computation and storage. In this paper, we propose a supervised hyperspectral band selection method based on differential weights, which depict the contribution degree of each band for classification. The differential weights are obtained in the training stage by calculating the sum of weight differences between positive and negative classes. Using the effective one-class Support Vector Machine (SVM), the bands corresponding to large differential weights are extracted as discriminative features to make the classification decision. Moreover, label information from training data is further exploited to enhance the classification performance. Finally, experiments on three public datasets, as well as comparison with other popular feature selection methods, are carried out to validate the proposed method.

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International Journal of Wavelets, Multiresolution and Information Processing

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15

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Electronic version of an article published in International Journal of Wavelets, Multiresolution and Information Processing, Volume 15, Issue 06, 1750065 (2017), https://doi.org/10.1142/S0219691317500655. Copyright World Scientific Publishing Company http://www.worldscientific.com/worldscinet/ijwmip

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Mathematical sciences

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