Parallel Spatial–Spectral Hyperspectral Image Classification With Sparse Representation and Markov Random Fields on GPUs
Spatial-spectral classification is a very important topic in the field of remotely sensed hyperspectral imaging. In this work, we develop a parallel implementation of a novel supervised spectral-spatial classifier, which models the likelihood probability via l1 - l2 sparse representation and the spatial prior as a Gibbs distribution. This classifier takes advantage of the spatial piecewise smoothness and correlation of neighboring pixels in the spatial domain, but its computational complexity is very high which makes its application to time-critical scenarios quite limited. In order to improve the computational efficiency of the algorithm, we optimized its serial version and developed a parallel implementation for commodity graphics processing units (GPUs). Our parallel spatial-spectral classifier with sparse representation and Markov random fields (SSC-SRMRF-P) exploits the low-level architecture of GPUs. The parallel optimization of the proposed method has been carried out using the compute unified device architecture (CUDA). The performance of the parallel implementation is evaluated and compared with the serial and multicore implementations on central processing units (CPUs). In fact, the proposed method has been designed to adequately exploit the massive data parallel capacities of GPUs together with the control and logic capacities of CPUs, thus resorting to a heterogeneous CPU-GPU framework in the design of the parallel algorithm. Experimental results using real hyperspectral images demonstrate very high performance for the proposed CPU-GPU parallel method, both in terms of classification accuracy and computational performance.