Computing Efficiently Spectral-Spatial Classification of Hyperspectral Images on Commodity GPUs
The high computational cost of the techniques for segmentation and classification of hyperspectral images makes them good candidates for parallel processing, in particular, for computing on Graphics Processing Units (GPUs). In this paper an efficient projection on the GPUs for the spectral–spatial classification of hyperspectral images using the Compute Unified Device Architecture (CUDA) for NVIDIA devices is presented. A watershed transform is applied after reducing the hyperspectral image to one band through the calculation of a morphological gradient, while the spectral classification is carried out by Support Vector Machine (SVMs). The results are combined with an adaptive majority vote. The different computational stages are concatenated in a pipeline that minimizes the data transfer between the main memory of the host computer and the global memory of the graphics device to maximize the computational throughput. The memory hierarchy and the thousands of threads available in this architecture are efficiently exploited. It is possible to study different data partitioning strategies and thread block arrangements in order to promote concurrent execution of a large number of threads. The objective is to efficiently exploit commodity hardware with the aim of achieving real-time execution for on-board processing.
keywords: Hyperspectral images, Watershed, Classification, CUDA