GPU accelerated waterpixel algorithm for superpixel segmentation of hyperspectral images
The high computational cost of the superpixel segmentation algorithms for hyperspectral remote sensing images makes them ideal candidates for parallel computation. The waterpixel algorithm, in particular, extracts segmentation regions called waterpixels and consists of four stages called vectorial gradient, spatial regularization, marker selection, and watershed transform. In this paper, an efficient version of a GPU algorithm for waterpixel segmentation using the Compute Unified Device Architecture (CUDA) is presented. The algorithm extracts all the spectral information available in the bands of the hyperspectral image through the vectorial gradient. A cellular automaton is selected for the computation of the watershed transform using a block-asynchronous implementation with 8-connectivity. The experimental analysis shows high speedup values for the resulting GPU algorithm when it is compared to a multicore OpenMP implementation using 8 threads.
keywords: CUDA, Hyperspectral image, Remote sensing, Superpixel segmentation, Waterpixel segmentation, Watershed transform