Parallel implementation for SAM algorithm based on GPU and distributed computing

Advances in sensor and computer technology are revolutionizing the way that remote sensing data with hundreds or even thousands of channels for the same area on the surface of the earth is collected, managed and analyzed. In this paper, the classical Spectral Angle Mapper (SAM) algorithm, which is fit for parallel and distributed computing, is implemented by using Graphic Processing Units (GPU) and distributed cluster respectively to accelerate the computations. A quantitative performance comparison between Compute Unified Device Architecture (CUDA) and Matlab platform is given by analyzing result of different parallel architectures' implementation of the same SAM algorithm.