high performance computing on graphics processing units: hgpu.org

Image segmentation is a computationally expensive task that continuously presents performance challenges due to the increasing volume of available high resolution remote sensing images. Nowadays, Graphics Processing Units (GPUs) are emerging as an attractive computing platform for general purpose computations due to their extremely high floating-point processing performance and their comparatively low cost. In the image analysis context, the use of GPUs can accelerate the segmentation process. This work presents a parallel implementation of a region growing algorithm for GPUs. The parallel algorithm is based on processing each pixel as a different thread so as to take advantage of the fine-grain parallel capability of the GPU. In addition to the parallel algorithm, the paper also suggests a modification to the heterogeneity computation that improves the segmentation performance. The experiments results demonstrate that the parallel algorithm achieve significant performance gains, running up to 6.8 times faster than the sequential approach.