The Parallel Design And Implementation Of Hyperspe-ctral Remote Sensing Mineral Mapping Algorithm

Hyperspectral remote sensing image classification method occupies a very importantposition in hyperspectral technology research. As the continuous improvement of thespatial and spectral resolution, hyperspectral remote sensing image data becomesenormous. Remote sensing images with large amount of data has seriously hampered theapplication of hyperspectral remote sensing image classification techniques in actualproduction and living. To Improve remote sensing image classification algorithmperformance is becoming more and more urgent. Hyperspectral remote sensing imageparallel processing technology becomes a development trend.GPU has a powerful parallel processing capabilities.GPGPU is very hot in recentyears. This paper introduces some basic knowledge of the CUDA architecture:Configuration of CUDA,CUDA programming model, CUDA kernel function calls andsome basic CUDA function. Then introduced the four commonly used hyperspectralremote sensing image classification method:SAM(Spectral Angle Mapping),SCM(SpctralCorrelation Mapping),SIDM(Spectral Information Divergence Mapping),SWM(SpectralWave Mapping). This paper finishs the four algorithms parallel design work.This paper designs the four algorithms’ parallel method combining with the CUDAprogramming model.The four parallel algorithms,writed with VS2008and CUDA,aretested by real hyperspectral remote sensing image data.We can make a conelusion that:GPU has very obvious advantages in Process Pixellevel data Parallel Problems,especially for high compute density one,such as:superviseclassify.