Hyperspectral Image Classification based on Co-training

Abstract

The abundant information available in hyperspectral image has provided important opportunities for land-cover classification and recognition. However, "Curse of dimensionality" and small training sample set are two difficulties which hinder the improvement of computational efficiency and classification precision. In this paper, we present a co-training based method on hyperspectral image classification. Firstly, two views of samples are generated through two kinds of dimensionality reduction methods. After that, the co-training process is viewed as combinative label propagation over two independent views. Experimental results on real hyperspectral image show that the proposed method has better performance than the other state-of-the-art methods.