Multiple Kernel Learning Based on Discriminative Kernel Clustering for Hyperspectral Band Selection

In hyperspectral images, band selection plays a crucial role for land-cover classification. Multiple kernel learning (MKL) is a popular feature selection method by selecting the relevant features and classifying the images simultaneously. Unfortunately, a large number of spectral bands in hyperspect...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2016-11, Vol.54 (11), p.6516-6530
Main Authors: Feng, Jie, Jiao, Licheng, Sun, Tao, Liu, Hongying, Zhang, Xiangrong
Format: Article
Language:English
Subjects:
Affinity propagation (AP)
Classification
Complexity theory
discriminative kernel alignment (KA) (DKA)
hyperspectral band selection
Hyperspectral imaging
Kernel
kernel synergy
multiple kernel learning (MKL)
Rademacher complexity
Redundancy
Support vector machines
Training
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Summary:In hyperspectral images, band selection plays a crucial role for land-cover classification. Multiple kernel learning (MKL) is a popular feature selection method by selecting the relevant features and classifying the images simultaneously. Unfortunately, a large number of spectral bands in hyperspectral images result in excessive kernels, which limit the application of MKL. To address this problem, a novel MKL method based on discriminative kernel clustering (DKC) is proposed. In the proposed method, a discriminative kernel alignment (KA) (DKA) is defined. Traditional KA measures kernel similarity independently of the current classification task. Compared with KA, DKA measures the similarity of discriminative information by introducing the comparison of intraclass and interclass similarities. It can evaluate both kernel redundancy and kernel synergy for classification. Then, DKA-based affinity-propagation clustering is devised to reduce the kernel scale and retain the kernels having high discrimination and low redundancy for classification. Additionally, an analysis of necessity for DKC in hyperspectral band selection is provided by empirical Rademacher complexity. Experimental results on several hyperspectral images demonstrate the effectiveness of the proposed band selection method in terms of classification performance and computation efficiency.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2016.2585961