Latent subclass learning-based unsupervised ensemble feature extraction method for hyperspectral image classification

Abstract

A novel unsupervised ensemble feature learning method for hyperspectral image classification is proposed in this study. Firstly, we randomly sample multiple discriminative subsets from a hyperspectral image with the novel spatially constrained similarity measurement. Each subset consists of a small amount of representative pixels. Each pixel in the subset was assigned with a latent-subclass/pseudo label. Multiple multinomial logistic regression classifiers are then adopted to build relations between pixels and their latent subclass labels, where each classifier is trained with one subset. Finally, the predicted results of different classifiers for a given pixel are assembled as its ensemble feature. More discriminative features are extracted by the proposed method compared with features extracted by traditional unsupervised methods such as principal component analysis and non-negative matrix factorization. Experimental results on hyperspectral image classification demonstrate the effectiveness of the proposed method.