Dimensionality reduction for hyperspectral image based on manifold learning

Abstract

A novel dimensionality reduction method named spectral angle and geodesic distance-based locality preserving projection (SAGD-LPP) was proposed in this paper. Considering the physical characters of hyperspectral imagery, the proposed method primarily select neighbor pixels in the image based on spectral angle distance. Then, using the geodesic distance matrix construct a weighted matrix between pixels. Finally, based on this weighted matrix, the idea of locality preserving projection algorithm is applied to reduce the dimensions of hyperspectral image data. The use of spectral angle to measure the distance between pixels can effectively overcome the spectral amplitude error caused by the uncertainty. At the same time, the use of geodesic distance to construct weight matrix can better reflect the internal structure of the data manifold than the use of Euclidean distance. Therefore, the proposed methods can reserve effectively the original characters of dataset with less loss in the useful information and less distortion on the data structure. Experimental results on real hyperspectral data demonstrate that the proposed methods have higher detection accuracy than the other methods when applied to the target detection of hyperspectral imagery after dimensionality reduction.