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Metrics to evaluate compression algorithms for raw SAR data
Author(s) -
Pieterse Chané,
Plessis Warren P.,
Focke Richard W.
Publication year - 2019
Publication title -
iet radar, sonar and navigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 82
eISSN - 1751-8792
pISSN - 1751-8784
DOI - 10.1049/iet-rsn.2018.5213
Subject(s) - raw data , computer science , algorithm , data compression , compression (physics) , data mining , programming language , materials science , composite material
Modern synthetic aperture radar (SAR) systems have size, weight, power and cost (SWAP‐C) limitations since platforms are becoming smaller while SAR operating modes are becoming more complex. Thus, SAR systems are producing an ever‐increasing volume of data that needs to be transferred to a ground station for processing. A compression algorithm seeks to reduce the data volume of the raw data; however, the algorithm can cause degradation and losses that may degrade the effectiveness of the SAR mission. This work addresses the lack of standardised quantitative performance metrics so that the performance of SAR data‐compression algorithms can be objectively quantified. Therefore, metrics are established in two different domains, namely the data domain and the image domain. Since different levels of degradation are acceptable for different SAR applications, a trade‐off can be made between the data reduction and the degradation caused by the algorithm. Due to SWAP‐C limitations, there remains a trade‐off between the performance and the computational complexity of the compression algorithm.

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