18 November 2014 GPU-based acceleration of the hyperspectral band selection by SNR estimation using wavelet transform
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Abstract
Band selection provides performance improvement in hyperspectral applications such as target detection, spectral unmixing and classification. Signal-to-noise ratio estimation (SNRe) as a method can be adjusted for different specific applications. SNRe is usually used to remove some low SNR bands from original hyperspectral data in a preprocessing stage and then other band selection methods are applied for the remaining high SNR bands of hyperspectral data to make the operations more efficient. In this paper, we take advantage of SNRe to select the bands which contain the largest amount of information. The wavelet transform is first used to realize the signal-noise separation and get the noise standard deviation of each band, and then the SNRs of all bands are calculated orderly. Considering some time-consuming operations in SNRe algorithm which can’t satisfy some real time applications are very suitable for high performance computing(HPC) in parallel, we design a new massively parallel algorithm to accelerate the SNR estimation algorithm on graphics processing units(GPUs) using the compute device unified architecture(CUDA) language. In addition the implementation of our GPU-based SNRe algorithm has extremely explored the possible parallelism in the C code and been debugged carefully to verify its correctness and efficiency. Experiments are conducted on two sets of real hyperspectral images and considerable acceleration is obtained.
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Junpeng Zheng, Liaoying Zhao, Xiaorun Li, Xin Zhou, Jing Li, "GPU-based acceleration of the hyperspectral band selection by SNR estimation using wavelet transform", Proc. SPIE 9263, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V, 92630C (18 November 2014); doi: 10.1117/12.2068811; https://doi.org/10.1117/12.2068811
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