小波核局部Fisher判别分析的高光谱遥感影像特征提取

Feature Extraction for Hyperspectral Remote Sensing Image Based on Local Fisher Discriminant Analysis with Wavelet Kernel

为了提高高光谱遥感影像的分类精度,充分利用影像的光谱和局部信息,文中提出小波核局部Fisher判别分析的高光谱遥感影像特征提取方法.通过小波核函数将数据集从低维原始空间映射至高维特征空间,考虑到数据的局部信息,利用加权矩阵计算散度矩阵,对局部Fisher判别准则函数求解最优特征矩阵,使不同类别的样本在高维特征空间中的可分离性更佳.在2个公开高光谱数据集上的实验表明,文中方法的总体分类精度和Kappa系数都有所提高.

To improve classification accuracies of hyperspectral remote sensing images and make full use of local information, a feature extraction method for hyperspectral remote sensing images based on local Fisher discriminant analysis with wavelet kernel is proposed. Wavelet kernel function is introduced to map data from a low dimensional space to a high dimensional feature space, and a weighted matrix is employed to calculate scatter matrices. Local Fisher discriminant criterion function is solved to obtain the optimal feature matrix and a better separation in high-dimensional feature space. Experimental results on two open hyperspectral datasets show that the overall classification accuracy and Kappa coefficient of the proposed method are improved compared with other methods.