GPUs for parallel on-board hyperspectral image radiometric normalization

Yuanfeng Wu; Bing Zhang; Haina Zhao; Jianwei Gao; Li Ni; Wei Yang

doi:10.1117/12.2015561

18 May 2013 GPUs for parallel on-board hyperspectral image radiometric normalization

Yuanfeng Wu, Bing Zhang, Haina Zhao, Jianwei Gao, Li Ni, Wei Yang

Proceedings Volume 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX; 874322 (2013) https://doi.org/10.1117/12.2015561
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

This paper proposed a GPU-based implementation of radiometric normalization algorithms, which is used as a representative case study of on-board data processing techniques for hyperspectral image. Three algorithms of radiometric normalization based on the column average and standard deviation of raw image statistical characteristics were implemented and applied to real hyperspectral images for evaluating their performance. These algorithms have been implemented using the compute device unified architecture (CUDA), and tested on the NVidia Tesla C2075 architecture. The airborne Pushbroom Hyperspectral Imager (PHI) was flown to acquire the spectrally contiguous images as experimental datasets. The results show that MN worked best among the three methods and the speedups achieved by the GPU implementation over their CPU counterparts are outstanding.

Citation Download Citation

Yuanfeng Wu, Bing Zhang, Haina Zhao, Jianwei Gao, Li Ni, and Wei Yang "GPUs for parallel on-board hyperspectral image radiometric normalization", Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 874322 (18 May 2013); https://doi.org/10.1117/12.2015561

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