Latent Relationship Guided Stacked Sparse Autoencoder for Hyperspectral Imagery Classification

Abstract

Classification is an important application of hyperspectral image (HSI). However, it is also a challenging research topic due to the spatial variability of spectral signature and limited training samples. To address these problems, a novel unsupervised feature learning method called latent relationship guided the stacked sparse autoencoder (LRSSAE) is developed in this article, which can effectively exploit the latent relationship under feature space to improve the ability of feature learning. Moreover, the superpixels constraint is employed on the feature representation to avoid the “salt-and-pepper” problem, and it is enforced on the latent relationship to enhance the latent relationship learning additionally. In LRSSAE, combining the stacked sparse autoencoder (SSAE) with the graph regularizations of latent relationship in each hidden layer and the superpixel constraints in the top layer, we extract feature representation in an unsupervised manner. And then, we present a customized iterative algorithm to optimize the LRSSAE. We evaluate the proposed method on three widely used HSI data sets comprehensively. The results demonstrate that our method achieves promising classification performance on these data sets and obtains improvements of 5.06%, 5.77%, and 2.11% in overall accuracy compared to the best SSAE method.