GPU implementation for hyperspectral image analysis using Recursive Hierarchical Segmentation

Graphics processing units (GPUs) have recently emerged as a promising parallel architecture considering the rapid growth of its computational power in comparison with uniprocessors. In this paper, we investigate a GPU parallel implementation of Recursive Hierarchical Segmentation technique (RHSEG). RHSEG is an object-based image analysis (OBIA) segmentation technique which involves region growing and spectral clustering (non-adjacent region growing). OBIA is becoming more popular compared to traditional pixel-based image analysis because of its efficiency with high spatial resolution images. The proposed parallel RHSEG algorithm was implemented using NVidia's compute device unified architecture (CUDA). The experiments conducted show that the parallel GPU RHSEG achieved an average processing speedup of 3.5 times over the sequential CPU implementation.