期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 53, 期 5, 页码 2956-2969出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2014.2367022
关键词
Clonal selection algorithm (CSA); hyperspectral band selection; multivariable mutual information (MMI); semi-supervised learning
类别
资金
- National Basic Research Program (973 Program) of China [2013CB329402]
- Program for Cheung Kong Scholars and Innovative Research Team in University [IRT1170]
- Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) [B07048]
- National Natural Science Foundation of China [61173090, 61272282, 61403304]
- Fundamental Research Funds for the Central Universities [JB140317]
- National Research Foundation for the Doctoral Program of Higher Education of China [20110203110006]
- Program for New Century Excellent Talents in University [NCET-13-0948]
The large number of spectral bands in hyperspectral images provides abundant information to distinguish different land covers. However, these spectral bands have much redundancy and bring an extra computational burden. Thus, band selection is important for hyperspectral images. Since the labeled samples are difficult to obtain, a semi-supervised criterion based on maximum discrimination and information (MDI) is defined by using both limited labeled samples and sufficient unlabeled samples. This MDI criterion aims to select the most highly discriminative and informative bands, but it is hard to accurately calculate. Therefore, a novel criterion based on high discrimination, high information, and low redundancy (DIR) is proposed as its low-order approximation. Moreover, from an information theory perspective, a theoretical proof is given that many traditional semi-supervised feature selection criteria are the low-order approximations of this MDI criterion. Compared with them, the proposed criterion needs more relaxed approximation conditions. To search and optimize the proposed criterion, a novel clonal selection algorithm is proposed, where the adaptive clone and mutation operators are devised to speed up the convergence. Experimental results on hyperspectral images demonstrate the effectiveness of the proposed semi-supervised band selection method.
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