Efficient segmentation of hyperspectral images on commodity GPUs

The techniques for segmentation and classification of hyperspectral images are very costly, which makes them good candidates for parallel and, in particular, GPU processing. In this paper we present a GPU implementation of a segmentation strategy for hyperspectral images consisting in the calculation of a morphological gradient operator that reduces the dimensionality of the hyperspectral image followed by the calculation of a watershed transform over the resulting 2D image. We have studied the main issues for the efficient implementation of the algorithms in GPU: the exploitation of thousands of threads available in this architecture and the adequate use of the device bandwidth. The tests show the efficiency of the GPU implementation indicating that the processing of hyperspectral images can be performed in real-time even on commodity GPUs like the one used in the experiments.