Band Selection Based on Evolution Algorithm and Sequential Search for Hyperspectral Classification

摘要：

Band (feature) selection for multispectral or hyperspectral data is an effective method to reduce dimension for cutting down the computational cost and alleviating the Hughes phenomenon. An efficient feature selection method based on Evolution algorithm (PSO and GA) and sequential search is proposed. The method embeds the sequential search into the evolution optimization for better ability of the fine tune in local search space and thus behaves well in both global and local cases. In addition, the embed scheme guarantees the validity of solutions for the 1-st form feature selection problem. The experiments with an airborne visible/infrared imaging spectrometer (AVIRIS) data set show the effectiveness of the proposed method.

作者: Rui Huang Xianhua Li

作者单位: School of Communication and Information Engineering, Shanghai University, Shanghai, China

会议类型: 国际会议

会议名称: 2008 International Conference on Audio，Language and Image Processing(2008国际声音、语言、图像过程大会)

会议地点: 镇江

会议语种:英文

页码: 1270-1273

在线出版日期: 2008-07-07（万方平台首次上网日期，不代表论文的发表时间）