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17 April 2006 Interference and noise adjusted principal components analysis for hyperspectral remote sensing image compression
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Abstract
Hyperspectral remote sensing images have high spectral resolution that enables accurate object detection, classification, and identification. But its vast data volume brings about problems in data transmission, data storage, and data analysis. How to reduce the data volume while keeping the important information for the following data exploitation 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 maximization. But 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 Principal Components analysis (NAPCA). It is also known that interference is a very serious problem in hyperspectral remote sensing images, induced by many unknown and unwanted signal sources extracted by hyperspectral sensors. Signal-to-interference-plus-noise (SINR) was proposed as a more appropriate ranking criterion. The resulting PCA is referred to as Interference and Noise Adjusted PCA (INAPCA). In this paper, we will investigate the application of INAPCA to hyperspectral image compression, and compare it with the PCA and NAPCA-based 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 even higher compression ratio. The results will be compared with popular wavelet-based compression methods, such as JPEG 2000, SPIHT, and SPECK.
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Qian Du and Harold Szu "Interference and noise adjusted principal components analysis for hyperspectral remote sensing image compression", Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 62470A (17 April 2006); https://doi.org/10.1117/12.665088
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