Unsupervised Land-Cover Segmentation Using Accelerated Balanced Deep Embedded Clustering

In this letter, we address the issue of the automatic labeling of remote sensing datasets using a novel deep learning clustering algorithm. The proposed algorithm addresses the inherent susceptibility of the deep embedded clustering (DEC) algorithm to data imbalance using additional search and extraction steps. Furthermore, the proposed algorithm is highly parallelizable. A graphics processing unit (GPU) implementation is shown to achieve 40X to 2600X of performance speedup and improved clustering accuracy with respect to DEC and other clustering approaches.