Graph Regularized Autoencoder Based Feature Extraction for Hyperspectral Image Classification

Abstract

We present a novel stacked autoencoder framework for feature extraction to improve classification of hyperspectral image, leveraging graph regularization to address the shortcomings of classical autoencoder that mainly focuses on learning spectral features. In the proposed method, we firstly construct a graph to represent the spectral-spatial similarity between pixels in a hyperspectral image by measuring their spatial and spectral distances. And then the graph regularized autoencoder is learned to transform the original spectral signatures of pixels into a new feature space used for the downstream pixel classification or other tasks. Our feature extraction method can preserve the intrinsic spectral-spatial distribution in a hyperspectral image and obtain more discriminative and robust features. The experiments on pixel classification show the competitive performance compared with classical autoencoder based and manifold learning based feature extraction approaches.