计算机工程与应用2017，Vol.53Issue(5)：154-158,5.DOI:10.3778/j.issn.1002-8331.1508-0074
面向分类应用的高光谱谱段选择方法
Band selection of hyperspectral data for application classification
王雅超 1武刚 1丁丽霞2
作者信息
- 1. 北京林业大学 信息学院,北京 100083
- 2. 浙江农林大学 环境与资源学院,浙江 临安 311300
- 折叠
摘要
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.关键词
遗传算法/谱段选择/K均值聚类/高光谱数据分类Key words
genetic algorithm/band selection/K-mean clustering/classification of hyperspectral data分类
信息技术与安全科学引用本文复制引用
王雅超,武刚,丁丽霞..面向分类应用的高光谱谱段选择方法[J].计算机工程与应用,2017,53(5):154-158,5.
