Hyperspectral image compression and target detection using nonlinear principal component analysis

Qian Du; Wei Wei; Ben Ma; Nicolas H. Younan

doi:10.1117/12.2022959

24 September 2013 Hyperspectral image compression and target detection using nonlinear principal component analysis

Qian Du, Wei Wei, Ben Ma, Nicolas H. Younan

Proceedings Volume 8871, Satellite Data Compression, Communications, and Processing IX; 88710S (2013) https://doi.org/10.1117/12.2022959
Event: SPIE Optical Engineering + Applications, 2013, San Diego, California, United States

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.

Citation Download Citation

Qian Du, Wei Wei, Ben Ma, and Nicolas H. Younan "Hyperspectral image compression and target detection using nonlinear principal component analysis", Proc. SPIE 8871, Satellite Data Compression, Communications, and Processing IX, 88710S (24 September 2013); https://doi.org/10.1117/12.2022959

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