Dimensionality Reduction for Hyperspectral Images Based on Cam Weighted Distance Laplacian Eigenmap
In consideration of the information redundancy and intrinsic nonlinearities, and the irrelevancy of Laplacian Eigenmap k-nearest neighbor selected for the uneven distribution of hyperspectral image data, this paper presents an improved LE algorithm based on Cam weighted distance for hyperspectral image dimensionality reduction to compact feature representation and improve the accuracy of classification.First, the band is grouped for the removal of singular band, then the Cam weighted distance Laplacian Eigenmap is used to reduce the remaining data dimension, and finally, the results are put into the minimum distance classifier for hyperspectral image classification.By verification with the Indiana Pines data set, the experimental results show that compared with linear dimensionality reduction method of PCA and nonlinear method of LE, Cam weighted distance Laplacian Eigenmap algorithm gets higher classification accuracy.
NETL
NSTL
万方数据
维普
