12 December 2001 Noise-constrained hyperspectral data compression techniques
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Storage and transmission requirements for hyperspectral data sets are significant. In order to reduce hardware costs, well-designed compression techniques are needed to preserve information content while maximizing compression ratios. Lossless compression techniques maintain data integrity, but yield small compression ratios. This paper presents three lossy compression algorithms that use the noise statistics of the data to preserve information content while maximizing compression ratios. The Spectral Compression and Noise Suppression (SCANS) algorithm adapts a noise estimation technique to exploit band-to-band correlation for optimizing linear prediction for data compression. The Adaptive Spectral Image Compression (ASIC) algorithm uses an iterative adaptive linear unmixing compression method, constrained by the noise statistics of the hypercube. By dynamically optimizing the end-members for each pixel this method minimizes the number of components required to represent the spectrum of any given pixel, yielding high compression ratios with minimal information content loss. The Adaptive Principal Components Analysis (APCA) algorithm uses noise statistics to determine the number of significant principal components and selects only those that are required to represent each pixel to within the noise level. We demonstrate the effectiveness of these methods with AVIRIS and HYMAP datasets.
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Mary H. Sharp, Mary H. Sharp, Suzanne T. Rupert, Suzanne T. Rupert, J. L. Barkenhagen, J. L. Barkenhagen, } "Noise-constrained hyperspectral data compression techniques", Proc. SPIE 4540, Sensors, Systems, and Next-Generation Satellites V, (12 December 2001); doi: 10.1117/12.450708; https://doi.org/10.1117/12.450708

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