基于自动子空间划分的高光谱数据特征提取

针对遥感高光谱图像数据量大、维数高的特点,提出了一种自动子空间划分方法用于高光谱图像数据量减小处理。该方法主要包括3个处理步骤:数据空间划分,子空间主成分分析和基于类别可分性准则的特征选择。该方法充分利用了高光谱图像各波段数据之间的局部相关性,将整个数据划分为若干个具有较强相关性的独立子空间,然后在子空间内利用主成分分析进行特征提取,根据各类地物间的类别可分性选择有效特征,最后利用地物分类来验证该方法的有效性。实验结果表明,该方法能够有效地实现高光谱图像数据维数减小和特征提取,同现有的自适应子空间分解方法和分段主成分变换方法相比,该方法所提取的特征用于分类时能获得较好的分类精度。利用该方法进行处理,当高光谱数据维数降低了90%时,9类地物分类实验的总体分类精度可以达到80.2%。

Feature Extraction Based on Automatic Subspace Partition for Hyperspectral Images

In this paper, a new data subspace partition method is proposed for reduction of hyperspectral image dimensionality. This method involves three steps: subspace partition of whole data space, feature extraction based on principal component analysis (PCA) in subspace and feature selection based on class separability criterion. The main merits of the proposed method are that it much more makes full use of neighboring correlation of hyperspectral data bands than those existing methods, and it realizes automatic subspace partition. In order to testify the effectiveness of the proposed method, classification experiments of hyperspectral images are conducted on AVIRIS data. The experiment investigation shows that the classification result in our new method is improved compared with both segmented principal component transformation (SPCT) and adaptive subspace decomposition method (ASD). When the data dimensionality is reduced 90% by using the proposed method, the overall classification accuracy of nine classes of ground cover reaches 80.2%.