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Journal of Applied Remote Sensing

Real-time implementation of optimized maximum noise fraction transform for feature extraction of hyperspectral images
Author(s): Yuanfeng Wu; Lianru Gao; Bing Zhang; Haina Zhao; Jun Li
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Paper Abstract

We present a parallel implementation of the optimized maximum noise fraction (G-OMNF) transform algorithm for feature extraction of hyperspectral images on commodity graphics processing units (GPUs). The proposed approach explored the algorithm data-level concurrency and optimized the computing flow. We first defined a three-dimensional grid, in which each thread calculates a sub-block data to easily facilitate the spatial and spectral neighborhood data searches in noise estimation, which is one of the most important steps involved in OMNF. Then, we optimized the processing flow and computed the noise covariance matrix before computing the image covariance matrix to reduce the original hyperspectral image data transmission. These optimization strategies can greatly improve the computing efficiency and can be applied to other feature extraction algorithms. The proposed parallel feature extraction algorithm was implemented on an Nvidia Tesla GPU using the compute unified device architecture and basic linear algebra subroutines library. Through the experiments on several real hyperspectral images, our GPU parallel implementation provides a significant speedup of the algorithm compared with the CPU implementation, especially for highly data parallelizable and arithmetically intensive algorithm parts, such as noise estimation. In order to further evaluate the effectiveness of G-OMNF, we used two different applications: spectral unmixing and classification for evaluation. Considering the sensor scanning rate and the data acquisition time, the proposed parallel implementation met the on-board real-time feature extraction.

Paper Details

Date Published: 13 August 2014
PDF: 16 pages
J. Appl. Remote Sens. 8(1) 084797 doi: 10.1117/1.JRS.8.084797
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Yuanfeng Wu, Institute of Remote Sensing and Digital Earth (China)
Lianru Gao, Institute of Remote Sensing and Digital Earth (China)
Bing Zhang, Institute of Remote Sensing and Digital Earth (China)
Haina Zhao, Institute of Remote Sensing and Digital Earth (China)
Univ. of Chinese Academy of Sciences (China)
Jun Li, Sun Yat-Sen Univ. (China)


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