A New Feature Selection Method for Hyperspectral Image Classification Based on Simulated Annealing Genetic Algorithm and Choquet Fuzzy Integral. (5th November 2013)

• Record Type: Journal Article
• Authors: Gao, Hongmin; Xu, Lizhong; Li, Chenming; Shi, Aiye; Huang, Fengchen; Ma, Zhenli
• Other Names: Ranzi Gianluca Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2013(2013)
• Publication Date: 2013-11-05
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2013/537268
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Hyperspectral remote sensing technology is a rapidly developing new integrated technology that is widely used in numerous areas. Rich spectral information from hyperspectral images can aid in the classification and recognition of the ground objects. However, the high dimensions of hyperspectral images cause redundancy in information. Hence, the high dimensions of hyperspectral data must be reduced. This paper proposes a hybrid feature selection strategy based on the simulated annealing genetic algorithm (SAGA) and the Choquet fuzzy integral (CFI). The band selection method is proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then, the selecting bands are further refined by CFI. Experimental results show that the proposed method can achieve higher classification accuracy than traditional methods.