Principal Component Analysis of Hyperspectral Remote Sensing in Parallel Computing Based on CUDA

Hyperspectral remote sensing has been wildly used in land and resource management.To address the problems of large amount of data and long time calculation,this paper discusses the parallel strategy of hyperspectral remote sensing data algorithms and studies the application mode of parallel computing in dealing with hyperspectral remote sensing data.The paper optimizes the Principal Component Analysis in hyperspectral remote sensing and through experiments confirms the effectiveness of the method.The experimental statistics have proved the feasibility of the parallel computing in dealing with the problems of large amount of data and long time calculation in hyperspectral remote sensing data processing.