Interference- and noise-adjusted principal component analysis for hyperspectral image compression

Qian Du

doi:10.1117/12.579799

14 December 2004 Interference- and noise-adjusted principal component analysis for hyperspectral image compression

Qian Du

Proceedings Volume 5584, Chemical and Biological Standoff Detection II; (2004) https://doi.org/10.1117/12.579799
Event: Optics East, 2004, Philadelphia, Pennsylvania, United States

Abstract

Hyperspectral images have high spectral resolution that enables accurate object classification. But its vast data volume brings about problems in data transmission and data storage. How to reduce the data volume while keeping the important information for the following data analysis is a challenging task. Principal Components Analysis (PCA) is a typical method for data compression, which re-arranges image information into the first several principal component images in terms of variance. But we know variance is not a good criterion to rank images. Instead, signal-to-noise ratio (SNR) is a more reasonable criterion, and the resulting PCA is called noise adjusted PCA (NAPCA). We also know that interference is a very serious problem in hyperspectral images, and have proposed signal-to-interference-plus-noise (SINR) as a ranking criterion. The resulting PCA is referred to as interference and noise adjusted PCA (INAPCA). In this paper, we will investigate the NAPCA and INAPCA to hyperspectral image compression. The focus is the analysis of their impacts on the following data exploitation (such as detection and classification). It is expected that using NAPCA and INAPCA higher detection and classification rates can be achieved with a comparable or higher compression ratio, compared to PCA-based compression.

Citation Download Citation

Qian Du "Interference- and noise-adjusted principal component analysis for hyperspectral image compression", Proc. SPIE 5584, Chemical and Biological Standoff Detection II, (14 December 2004); https://doi.org/10.1117/12.579799

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