高光谱图像具有光谱分辨率高、波段连续、数据量大、图谱合一等特点。然而较高的光谱分辨率会造成波段间相关性强，信息冗余多。所以如何从数百个高光谱波段中选出有利于识别或分类的波段组合成为了高光谱应用需要解决的问题。文章针对相邻波段间相关性较大的特点，提出一种改进的对波段相关矩阵进行全局搜索的子空间划分的波段选择方法。该方法克服了传统只利用相关向量对波段进行划分的缺陷，利用整个相关矩阵进行全局搜索划分，再在划分后的子空间内进行波段选择，从而降低了波段之间的相关性。文章最后使用上述方法对AVIRIS数据进行波段选择，并通过SVM方法对其进行地物分类，结果表明该方法较不进行子空间划分的波段选择方法有较高的分类精度。

Hyperspectral image has hundreds of successively narrow bands, which brings serious problems such as large correlation and redundant information. The selection of the optimal bands, which are suited for classification or recognition, has become a difficult work that needs to be overcome. In order to solve the problem of the large correlation among bands, a band selection method based on improved subspace partition through global search on correlation matrix was proposed. Through a global search, the band correlation matrix was divided into a series of subspace, from which the optimal bands were finally selected. The proposed method provides a band selection which has small correlation between each other. The result of an experiment which used Support Vector Machine(SVM) on an AVIRIS image shows that the proposed method is valid.