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Proceedings Paper

Low complexity efficient raw SAR data compression
Author(s): Shantanu Rane; Petros Boufounos; Anthony Vetro; Yu Okada
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Paper Abstract

We present a low-complexity method for compression of raw Synthetic Aperture Radar (SAR) data. Raw SAR data is typically acquired using a satellite or airborne platform without sufficient computational capabilities to process the data and generate a SAR image on-board. Hence, the raw data needs to be compressed and transmitted to the ground station, where SAR image formation can be carried out. To perform low-complexity compression, our method uses 1-dimensional transforms, followed by quantization and entropy coding. In contrast to previous approaches, which send uncompressed or Huffman-coded bits, we achieve more efficient entropy coding using an arithmetic coder that responds to a continuously updated probability distribution. We present experimental results on compression of raw Ku-SAR data. In those we evaluate the effect of the length of the transform on compression performance and demonstrate the advantages of the proposed framework over a state-of-the-art low complexity scheme called Block Adaptive Quantization (BAQ).

Paper Details

Date Published: 5 May 2011
PDF: 11 pages
Proc. SPIE 8051, Algorithms for Synthetic Aperture Radar Imagery XVIII, 80510W (5 May 2011); doi: 10.1117/12.884034
Show Author Affiliations
Shantanu Rane, Mitsubishi Electric Research Labs. (United States)
Petros Boufounos, Mitsubishi Electric Research Labs. (United States)
Anthony Vetro, Mitsubishi Electric Research Labs. (United States)
Yu Okada, Mitsubishi Electric Corp. (Japan)


Published in SPIE Proceedings Vol. 8051:
Algorithms for Synthetic Aperture Radar Imagery XVIII
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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