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https://doi.org/10.1109/IGARSS.2012.6351726
Title: | Implmentation of a covariance-based principal component analysis algorithm for hyperspectral imaging applications with multi-threading in both CPU and GPU | Authors: | Zhang, J. Lim, K.H. |
Keywords: | CUDA GPU Hyperspectral PCA real-time |
Issue Date: | 2012 | Citation: | Zhang, J., Lim, K.H. (2012). Implmentation of a covariance-based principal component analysis algorithm for hyperspectral imaging applications with multi-threading in both CPU and GPU. International Geoscience and Remote Sensing Symposium (IGARSS) : 4264-4266. ScholarBank@NUS Repository. https://doi.org/10.1109/IGARSS.2012.6351726 | Abstract: | Principle component analysis (PCA) [1] is widely utilized in hyperspectral image analysis [3, 4, 5]. There are three major approaches of principle component analysis: singular value decomposition (SVD) [2], covariance-matrix and iterative method (NIPALS) [6, 7]. In our previous work [9], we have demonstrated the advantage of the GPU implementation of covariance method for medium-sized hyperspectral images. In this paper, we present an improvement which combines the multithreading in CPU, GPU and CUDA's graphics interoperability [8]. It is found that this combined framework approaches real-time processing much further. © 2012 IEEE. | Source Title: | International Geoscience and Remote Sensing Symposium (IGARSS) | URI: | http://scholarbank.nus.edu.sg/handle/10635/112871 | DOI: | 10.1109/IGARSS.2012.6351726 |
Appears in Collections: | Staff Publications |
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