GPU Parallel Implementation of Spatially Adaptive Hyperspectral Image Classification

Image classification is a very important tool for remotely sensed hyperspectral image processing. Techniques able to exploit the rich spectral information contained in the data, as well as its spatial-contextual information, have shown success in recent years. Due to the high dimensionality of hyperspectral data, spectral-spatial classification techniques are quite demanding from a computational viewpoint. In this paper, we present a computationally efficient parallel implementation for a spectral-spatial classification method based on spatially adaptive Markov random fields (MRFs). The method learns the spectral information from a sparse multinomial logistic regression classifier, and the spatial information is characterized by modeling the potential function associated with a weighted MRF as a spatially adaptive vector total variation function. The parallel implementation has been carried out using commodity graphics processing units (GPUs) and the NVIDIA's Compute Unified Device Architecture. It optimizes the work allocation and input/output transfers between the central processing unit and the GPU, taking full advantages of the computational power of GPUs as well as the high bandwidth and low latency of shared memory. As a result, the algorithm exploits the massively parallel nature of GPUs to achieve significant acceleration factors (higher than 70x) with regards to the serial and multicore versions of the same classifier on an NVIDIA Tesla K20C platform.