期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 54, 期 11, 页码 6516-6530出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2016.2585961
关键词
Affinity propagation (AP); discriminative kernel alignment (KA) (DKA); hyperspectral band selection; kernel synergy; multiple kernel learning (MKL); Rademacher complexity
类别
资金
- National Basic Research Program (973 Program) of China [2013CB329402]
- Program for Cheung Kong Scholars and Innovative Research Team in University [IRT_15R53]
- Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) [B07048]
- National Natural Science Foundation of China [61502369, 61501353]
- China Postdoctoral Science Foundation [2015M570816, 2016T90892]
- Natural Science Basic Research Plan in Shaanxi Province of China [2016JQ6047]
- Postdoctoral Research Program in Shaanxi Province of China
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.
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