PCA与移动窗小波变换的高光谱决策融合分类

PCA and windowed wavelet transform for hyperspectral decision fusion classification

目的高光谱数据具有较高的谱间分辨率和相关性,给分类处理带来了一定的困难。为了提高分类精度,提出一种结合PCA与移动窗小波变换的高光谱决策融合分类算法。方法首先,利用相关系数矩阵对原始高光谱数据进行波段分组;然后,利用主成分分析对每组数据进行谱间降维;再根据提出的移动窗小波变换法进行空间特征提取;最后,采用线性意见池(LOP)决策融合规则对多分类器的分类结果进行融合。结果采用两组来自不同传感器的数据进行实验,所提算法的分类精度和Kappa系数均高于已有的5种分类算法。与SVM-RBF算法相比,本文算法的分类精度高出了8%左右。结论实验结果表明,本文算法充分挖掘了高光谱图像的谱间-空间信息,能有效提高分类正确率,在小样本情况下和噪声环境中也具有良好的分类性能。

Objective High spectral resolution and correlation hinder the application of classification in hyperspectral data. To improve classification accuracy, a hyperspeetral decision fusion classification method based on principal component anal- ysis （PCA） and windowed wavelet transform is proposed in this study. Method A correlation coefficient matrix is used to group original hyperspectral data. PCA is applied to reduce the spectral dimensions of data for each group. The proposed windowed wavelet transform method is used to extract spatial features. Linear opinion pool is employed to fuse the classifica- tion results from multi-classifiers. Result Using two hyperspectral data sets from different sensors, the proposed algorithm obtain higher classification accuracy and Kappa coefficient than five existing algorithms. The classification accuracy of the proposed algorithm outperforms that of support vector machine-radial basis function （SVM-RBF） by approximately 8%. Conclusion Experimental results show that the proposed method can explore spectral-spatial information from hyperspectral imagery, improve classification accuracy efficiently, and provide outstanding classification performance under a small sam- ple size and noisy environments.