Lossy data compression for next-generation imager data

Shawn W. Miller; Jeffery J. Puschell

doi:10.1117/12.562624

14 October 2004 Lossy data compression for next-generation imager data

Proceedings Volume 5548, Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an End-to-End System Perspective; (2004) https://doi.org/10.1117/12.562624
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

Abstract

In the next decade, the volume of data produced by satellite-based remote sensing instruments will increase dramatically. The success of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments validates the decisions of government agencies to seek higher spectral and spatial resolution in the next generation of polar-orbiting and geosynchronous imagers, but transmitting the resulting high amounts of data to the Earth within constraints of available bandwidth will require new approaches to onboard data compression. In particular, the limitations of bandwidth will force greater use of lossy data compression, particularly in spectral channels with high spatial resolution, such as the reflective channels proposed for the Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI), which will fly on GOES-R in 2012. In this study, we present analyses of the trade between two candidate lossy data compression algorithms, JPEG and JPEG-2000, for the encoding of reflective channel data from the ABI. These analyses include application to two types of real data: MODIS imagery and MODIS Airborne Simulator (MAS) imagery. The MODIS images are processed directly; the 50-m resolution MAS images are first run through a basic simulation of ABI spatial and radiometric response. In both cases, spectral channels corresponding to those that will be lossily compressed on ABI are available to support the performance trades between JPEG and JPEG-2000. The performance results are expressed in terms of peak signal to noise ratio (PSNR) and correlated noise, and they are placed in context with an assessment of the current technology readiness level (TRL) of the two standards.

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Shawn W. Miller and Jeffery J. Puschell "Lossy data compression for next-generation imager data", Proc. SPIE 5548, Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an End-to-End System Perspective, (14 October 2004); https://doi.org/10.1117/12.562624

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KEYWORDS

MODIS

Data compression

Image compression

Clouds

Spatial resolution

Imaging systems

Signal to noise ratio

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