Special Section on High-Performance Computing in Applied Remote Sensing: Part 3

Real-time implementation of optimized maximum noise fraction transform for feature extraction of hyperspectral images

[+] Author Affiliations
Yuanfeng Wu

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, No. 9 Dengzhuang South Road, Beijing 100094, China

Lianru Gao

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, No. 9 Dengzhuang South Road, Beijing 100094, China

Bing Zhang

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, No. 9 Dengzhuang South Road, Beijing 100094, China

Haina Zhao

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, No. 9 Dengzhuang South Road, Beijing 100094, China

University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China

Jun Li

Sun Yat-sen University, No. 135 Xingang Xi Road, Guangzhou 510275, China

J. Appl. Remote Sens. 8(1), 084797 (Aug 13, 2014). doi:10.1117/1.JRS.8.084797
History: Received May 27, 2014; Revised July 13, 2014; Accepted July 16, 2014
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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.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Yuanfeng Wu ; Lianru Gao ; Bing Zhang ; Haina Zhao and Jun Li
"Real-time implementation of optimized maximum noise fraction transform for feature extraction of hyperspectral images", J. Appl. Remote Sens. 8(1), 084797 (Aug 13, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.084797


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