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Computing Efficiently Spectral-Spatial Classification of Hyperspectral Images on Commodity GPUs

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Recent Advances in Knowledge-based Paradigms and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 234))

Abstract

The high computational cost of the techniques for segmentation and classification of hyperspectral images makes them good candidates for parallel processing, in particular, for computing on Graphics Processing Units (GPUs). In this paper an efficient projection on the GPUs for the spectral–spatial classification of hyperspectral images using the Compute Unified Device Architecture (CUDA) for NVIDIA devices is presented. A watershed transform is applied after reducing the hyperspectral image to one band through the calculation of a morphological gradient, while the spectral classification is carried out by Support Vector Machine (SVMs). The results are combined with an adaptive majority vote. The different computational stages are concatenated in a pipeline that minimizes the data transfer between the main memory of the host computer and the global memory of the graphics device to maximize the computational throughput. The memory hierarchy and the thousands of threads available in this architecture are efficiently exploited. It is possible to study different data partitioning strategies and thread block arrangements in order to promote concurrent execution of a large number of threads. The objective is to efficiently exploit commodity hardware with the aim of achieving real-time execution for on-board processing.

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Notes

  1. 1.

    See CUBLAS at https://developer.nvidia.com/cublas

  2. 2.

    Hyperspectral Remote Sensing Scenes available at http://www.ehu.es/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes

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Acknowledgments

This work was supported in part by the Ministry of Science and Innovation, Government of Spain, cofounded by the FEDER funds of European Union, under contract TIN 2010-17541, and by Xunta de Galicia, Program for Consolidation of Competitive Research Groups ref. 2010/28. Pablo acknowledges financial support from the Ministry of Science and Innovation, Government of Spain, under a MICINN-FPI grant.

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  1. Centro de Investigación en Tecnoloxías da Información (CITIUS), University of Santiago de Compostela, Rúa de Jenaro de la Fuente Domínguez, Santiago de Compostela, 15842, Spain

    Pablo Quesada-Barriuso, Francisco Argüello & Dora B. Heras

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  1. Pablo Quesada-Barriuso
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  2. Francisco Argüello
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Correspondence to Pablo Quesada-Barriuso .

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  1. Defence Science and Technology Organisation, Aerospace Division, Edinburgh, South Australia, Australia

    Jeffrey W. Tweedale

  2. Faculty of Education, Science, Technology & Mathematics, University of Canberra, Canberra, Australia

    Lakhmi C. Jain

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Quesada-Barriuso, P., Argüello, F., Heras, D.B. (2014). Computing Efficiently Spectral-Spatial Classification of Hyperspectral Images on Commodity GPUs. In: Tweedale, J., Jain, L. (eds) Recent Advances in Knowledge-based Paradigms and Applications. Advances in Intelligent Systems and Computing, vol 234. Springer, Cham. https://doi.org/10.1007/978-3-319-01649-8_2

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  • DOI: https://doi.org/10.1007/978-3-319-01649-8_2

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