Paper
11 July 1985 On-Board Data Compression For Advanced Landsat
Carl Schueler, Chris de Boer, Barbara Marks, Mark Stegall
Author Affiliations +
Proceedings Volume 0534, Architectures and Algorithms for Digital Image Processing II; (1985) https://doi.org/10.1117/12.946573
Event: 1985 Los Angeles Technical Symposium, 1985, Los Angeles, United States
Abstract
Advanced Landsat Sensor (ALS) technology has produced requirements for increasing data rates that may exceed space to ground data link capacity, so that identification of appropriate data compression techniques is of interest. Unlike many other applications, Landsat requires information lossless compression. DPCM, Interpolated DPCM, and error-correcting successive-difference PCM (ESPCM) are compared, leading to the conclusion that ESPCM is a practical, real-time (on-board) compression algorithm. ESPCM offers compression ratios approaching DPCM with no information loss and little or no increase in complexity. Moreover, adaptive ESPCM (AESPCM) yields an average compression efficiency of 84% relative to successive difference entropy, and 97% relative to scene entropy. Compression ratios vary from a low of 1.18 for a high entropy (6.64 bits/pixel) mountain scene to a high of 2.38 for low entropy (2.54 bits/pixel) ocean data. The weighted average lossless compression ratio to be expected, using a representative selection of Landsat Thematic Mapper eight-bit data as a basis, appears to be approximately 2.1, for an average compressed data rate of about 3.7 bits/pixel.
© (1985) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Carl Schueler, Chris de Boer, Barbara Marks, and Mark Stegall "On-Board Data Compression For Advanced Landsat", Proc. SPIE 0534, Architectures and Algorithms for Digital Image Processing II, (11 July 1985); https://doi.org/10.1117/12.946573
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KEYWORDS
Image compression

Earth observing sensors

Landsat

Neodymium

Agriculture

Data compression

Digital image processing

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