Hierarchical feature learning with dropout k-means for hyperspectral image classification

A huge volume of high spatial resolution hyperspectral imagery (HSI) data sets can currently be acquired. However, making full use of the information within the HSI is still a huge problem. The exploitation of spatial information is playing a more and more important role in the classification of remote sensing data. How to efficiently extract the spatial feature for HSI has become a critical task. In this paper, we propose a dropout k-means based framework to extract an effective hierarchical spatial feature for HSI. This paper focuses on unsupervised hierarchical feature learning representation. The proposed framework was tested on two HSIs. The extensive experimental results clearly show that the proposed dropout k-means based framework achieves a superior classification performance.