Paper
6 October 1989 Spectral Image Compression
Corinne Mailhes, Paul Vermande, Francis Castanie
Author Affiliations +
Proceedings Volume 1129, Advanced Optical Instrumentation for Remote Sensing of the Earth's Surface from Space; (1989) https://doi.org/10.1117/12.961492
Event: 1989 International Congress on Optical Science and Engineering, 1989, Paris, France
Abstract
This paper deals with a new instrument under development at the CNES (the French Space Agency) : a Fourier Transform spectro imager. Based on the principle of the Michelson interferometer, it will perform on-board satellite high spectral resolution remote sensing. This instrument gives images in which each pixel is, in fact, a set of about 200 points : each pel is the whole spectrum of the observed point on Earth. After a description of the spectro-imager, we present the solution brought to the problem of transmission of these "vector" images. Obviously, the straight transmission implies a very high bit rate (estimated at 126Mbits/s). So we apply techniques of signal processing to the signals of each pixel (interferograms) : we model the interferogram by an AutoRegressive process, then we get a set of parameters to be coded. We show in the paper that the most efficient set is defined by : the p first points of the interferogram, {y(n)}n=0,...,p-1,p partial correlation coefficients {ki}i=1 and the model error {e(n)}n=p,...,N(N = 200, p = model order = 10). After these pfocessings, we reach a compression ratio ofd with a very good spectral fidehty. In conclusion, we present several examples of actual reflectances and their compressed versions, exhibiting the very low degradation of the spectra obtained even with high compression ratio.
© (1989) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Corinne Mailhes, Paul Vermande, and Francis Castanie "Spectral Image Compression", Proc. SPIE 1129, Advanced Optical Instrumentation for Remote Sensing of the Earth's Surface from Space, (6 October 1989); https://doi.org/10.1117/12.961492
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KEYWORDS
Autoregressive models

Signal processing

Charge-coupled devices

Remote sensing

Information technology

Quantization

Sensors

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