Balanced data driven sparsity for unsupervised deep feature learning in remote sensing images classification

Abstract

There are many attempts that utilize deep learning methods to solve the problem of classification in remote sensing images. Convolutional Neural Networks (CNN) have made very good performance for various visual tasks, and marked their important place in all deep learning models. However, for some classification tasks of remote sensing images, CNN could not demonstrate their full potential because of lacking large amounts of labeled training data. Some efforts have been made to combine CNN with unlabeled data to tackle the problem by performing unsupervised learning. In this work we propose the balanced data driven sparsity to help train CNN in an unsupervised way. The experiments over the real world remote sensing images demonstrate that the proposed method improves the performance of the recent methods.