GPU Implementation of Graph-Regularized Sparse Unmixing With Superpixel Structures

Abstract

To enhance spectral unmixing performance, a large number of algorithms have simultaneously investigated spatial and spectral information in hyperspectral images. However, sophisticated algorithms with high computational complexity can be very time-consuming when a large amount of data are involved in processing hyperspectral images. In this paper, we first introduce a group sparse graph-regularized unmixing method with superpixel structure, to promote piece-wise consistency of abundances and reduce computational burden. Segmenting the image into several non-overlapped superpixels also enables to decompose the unmixing problem into uncoupled subproblems that can be processed in parallel. An implementation for the proposed algorithm on graphics processing units (GPUs) is then developed based on the NVIDIA Compute Unified Device Architecture (CUDA) framework. The proposed scheme achieves parallelism at both the intra-superpixel and inter-superpixel levels, where multiple concurrent streams have been used to enable multiple kernels to execute on the device simultaneously. Simulation results with a series of experiments demonstrate advantages of the proposed algorithm. The performance of the GPU implementation also illustrates that parallel scheme largely expedites the implementation.