The high spatial dimensionality of the remote sensing images that are captured by modern hyperspectral sensors prevents many algorithms from being computationally feasible. Superpixel segmentation is a process that groups pixels into connected regions that are uniform according to one or more similarity measures. WaterPixel (WP) segmentation is a particular case of superpixel segmentation based on the watershed transform. In this paper an efficient implementation of the WP algorithm for the segmentation of remote sensing hyperspectral images on multi-core CPUs and programmable GPUs is explored. The proposed approach focuses on reducing the cost of the morphological gradient and the watershed segmen-tation, which are the two most costly steps of the algorithm.
Keywords: remote sensing, hyperspectral, superpixel segmentation, watershed, GPU, multicore, CUDA