Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 263-267, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/263/2016/
doi:10.5194/isprs-archives-XLI-B7-263-2016
 
21 Jun 2016
PARALLEL IMPLEMENTATION OF MORPHOLOGICAL PROFILE BASED SPECTRAL-SPATIAL CLASSIFICATION SCHEME FOR HYPERSPECTRAL IMAGERY
B. Kumar1 and O. Dikshit2 1Department of Computer Science & Information Technology, MJP Rohilkhand University, Bareilly, India
2Department of Civil Engineering, Indian Institute of Technology Kanpur, India
Keywords: Hyperspectral, Extended Morphological Profile (EMP), spectral-spatial classification, parallel processing, GPU Abstract. Extended morphological profile (EMP) is a good technique for extracting spectral-spatial information from the images but large size of hyperspectral images is an important concern for creating EMPs. However, with the availability of modern multi-core processors and commodity parallel processing systems like graphics processing units (GPUs) at desktop level, parallel computing provides a viable option to significantly accelerate execution of such computations. In this paper, parallel implementation of an EMP based spectralspatial classification method for hyperspectral imagery is presented. The parallel implementation is done both on multi-core CPU and GPU. The impact of parallelization on speed up and classification accuracy is analyzed. For GPU, the implementation is done in compute unified device architecture (CUDA) C. The experiments are carried out on two well-known hyperspectral images. It is observed from the experimental results that GPU implementation provides a speed up of about 7 times, while parallel implementation on multi-core CPU resulted in speed up of about 3 times. It is also observed that parallel implementation has no adverse impact on the classification accuracy.
Conference paper (PDF, 1284 KB)


Citation: Kumar, B. and Dikshit, O.: PARALLEL IMPLEMENTATION OF MORPHOLOGICAL PROFILE BASED SPECTRAL-SPATIAL CLASSIFICATION SCHEME FOR HYPERSPECTRAL IMAGERY, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 263-267, doi:10.5194/isprs-archives-XLI-B7-263-2016, 2016.

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