随着遥感技术和成像光谱仪的发展,高光谱遥感图像的分辨率不断提高,其庞大的数据量在提高其遥感探测能力的同时,也给分析和处理带来了很大的困难.高光谱波段选择可以有效减少数据冗余,提高分类识别精度和处理效率.因此如何从多达数百个波段的高光谱图像中选择出具有较好分类识别能力的波段组合是亟待解决的问题.针对上述问题,采用基于图论的谱聚类算法,将原始高光谱图像中的波段作为待聚类的数据点,利用互信息描述两两波段间的相似度,生成相似度矩阵.再根据图谱划分理论,将相似度矩阵生成的非规范化图拉普拉斯矩阵进行谱分解,得到类间相似度小且类内相似度大的类簇;然后根据地物类型计算各波段的类间可分性因子,将其作为类簇内进一步选择代表性波段的参考指标,达到降维的目的;最后通过支持向量机与最小距离分类方法对波段选择后的图像分类.该方法区别于传统的无监督聚类方法,采用基于图论的谱聚类算法,并根据先验知识计算类间可分性因子来选择波段.通过与自适应波段选择算法和基于自动子空间划分的波段指数算法的对比实验,结果表明:两组实验当聚类数目达到相对最佳时,该波段选择方法支持向量机图像总分类精度达到94.08%和94.24%以上,最小距离分类图像总分类精度达到87.98%和89.09%以上,有效保留了光谱信息,提高了分类精度.

With the development of remote sensing technology and imaging spectrometer ,the resolution of hyperspectral remote sensing image has been continually improved,its vast amount of data not only improves the ability of the remote sensing detection but also brings great difficulties for analyzing and processing at the same time.Band selection of hyperspectral imagery can effectively reduce data redundancy and improve classification accuracy and efficiency.So how to select the optimum band combination from hundreds of bands of hyperspectral images is a key issue.In order to solve these problems,we use spectral clustering algorithm based on graph theory.Firstly,taking of the original hyperspectral image bands as data points to be clustered ,mutual information between every two bands is calculated to generate the similarity matrix.Then according to the graph partition theory,spectral decomposition of the non-normalized Laplacian matrix generated by the similarity matrix is used to get the clusters,which the similarity between is small and the similarity within is large.In order to achieve the purpose of dimensionality reduction,the inter-class separability factor of feature types on each band is calculated,which is as the reference index to choose the representative bands in the clusters furthermore.Finally,the support vector machine and minimum distance classification methods are employed to classify the hyperspectral image after band selection.The method in this paper is different from the traditional unsupervised clustering method,we employ spectral clustering algorithm based on graph theory and compute the inter-class separability factor based on a priori knowledge to select bands.Comparing with traditional adaptive band selection algorithm and band index based on automatically subspace divided algorithm,the two sets of experiments results show that the overall accuracy of SVM is about 94.08% and 94.24% and the overall accuracy of MDC is about 87.98% and 89.09%,when the band selection achieves a relatively optimal number of clusters using the method propoesd in this paper .It effectively remains spectral information and improves the classification accuracy.