Superpixel Segmentation of Remote Sensing Images using Waterpixels in Commodity Hardware

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