Accelerated segmentation approach with CUDA for high spatial resolution remotely sensed imagery based on improved Mean Shift

In conventional researches, satisfying results cannot be achieved when directly applying Mean Shift segmentation onto high spatial resolution (HR) remote sensing image. The proposed method addresses this problem and extents Mean Shift clustering algorithm into high-dimensional feature space by extracting texture and shape descriptor. The dilemma in image segmentation is that the algorithms with good performance are also the ones with much computational cost. To improve the performance of the standard Mean Shift segmentation for HR remote sensing images, an accelerated segmentation approach is proposed under Compute Unified Device Architecture (CUDA) framework. The experimental results demonstrate that the CUDA-based implementation works 20-30 times faster than the original implementation in CPU.