1 June 2005 Compression technique for plume hyperspectral images
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The authors recently developed a hyperspectral image output option for a standardized government code designed to predict missile exhaust plume infrared signatures. Typical predictions cover the 2- to 5-m wavelength range (2000 to 5000 cm-1) at 5 cm-1 spectral resolution, and as a result the hyperspectral images have several hundred frequency channels. Several hundred hyperspectral plume images are needed to span the full operational envelope of missile altitude, Mach number, and aspect angle. Since the net disk storage space can be as large as 100 GB, a Principal Components Analysis is used to compress the spectral dimension, reducing the volume of data to just a few gigabytes. The principal challenge was to specify a robust default setting for the data compression routine suitable for general users, who are not necessarily specialists in data compression. Specifically, the objective was to provide reasonable data compression efficiency of the hyperspectral imagery while at the same time retaining sufficient accuracy for infrared scene generation and hardware-in-the-loop test applications over a range of sensor bandpasses and scenarios. In addition, although the end users of the code do not usually access the detailed spectral information contained in these hyperspectral images, this information must nevertheless be of sufficient fidelity so that atmospheric transmission losses between the missile plume and the sensor could be reliably computed as a function of range. Several metrics were used to determine how far the plume signature hyperspectral data could be safely compressed while still meeting these end-user requirements.
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B. K. Feather, B. K. Feather, S. A. Fulkerson, S. A. Fulkerson, J. H. Jones, J. H. Jones, R. A. Reed, R. A. Reed, M. A. Simmons, M. A. Simmons, D. G. Swann, D. G. Swann, W. E. Taylor, W. E. Taylor, L. S. Bernstein, L. S. Bernstein, } "Compression technique for plume hyperspectral images", Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); doi: 10.1117/12.601904; https://doi.org/10.1117/12.601904

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