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Proceedings Paper

Hyperspectral image compression and target detection using nonlinear principal component analysis
Author(s): Qian Du; Wei Wei; Ben Ma; Nicolas H. Younan
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Paper Abstract

The widely used principal component analysis (PCA) is implemented in nonlinear by an auto-associative neural network. Compared to other nonlinear versions, such as kernel PCA, such a nonlinear PCA has explicit encoding and decoding processes, and the data can be transformed back to the original space. Its data compression performance is similar to that of PCA, but data analysis performance such as target detection is much better. To expedite its training process, graphics computing unit (GPU)-based parallel computing is applied.

Paper Details

Date Published: 24 September 2013
PDF: 6 pages
Proc. SPIE 8871, Satellite Data Compression, Communications, and Processing IX, 88710S (24 September 2013); doi: 10.1117/12.2022959
Show Author Affiliations
Qian Du, Mississippi State Univ. (United States)
Wei Wei, Mississippi State Univ. (United States)
Ben Ma, Mississippi State Univ. (United States)
Nicolas H. Younan, Mississippi State Univ. (United States)


Published in SPIE Proceedings Vol. 8871:
Satellite Data Compression, Communications, and Processing IX
Bormin Huang; Antonio J. Plaza; Chein-I Chang, Editor(s)

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