关键词: 遗传算法, 谱段选择, [K]均值聚类, 高光谱数据分类

Abstract: Although hyperspectral data has been widely utilized in recognition and classification of materials, it suffers from large data size and high correlation between the bands, which may decrease the classification accuracy and hinder its applications. In this paper, previous band selection methods are analyzed for the above problem, and a new band selection algorithm for hyperspectral data based on k-means clustering and supervised classifications is proposed. Firstly K-mean classification method is used to cluster bands of hyperspectral data into several sets. Then band selection based on genetic algorithm is developed by using the classification accuracy as the cost function criterion. Hyperspectral data of leaves is used for classification to testify the effectiveness of this band selection algorithm. As shown in the experimental results, the proposed method can achieve high performance for classification applications.

Key words: genetic algorithm, band selection, K-mean clustering, classification of hyperspectral data