Abstract
Synthetic aperture radar (SAR) interferometry has rapidly evolved in the last decade and can be considered today as a mature technology, which incorporates computationally intensive and data-intensive tasks. In this paper, a perspective on the state-of-the-art of high performance computing (HPC) methodologies applied to spaceborne SAR interferometry (InSAR) is presented, and the different parallel algorithms for interferometric processing of SAR data are critically discussed at different levels. Emphasis is placed on the key processing steps, which typically occur in the interferometric techniques, categorized according to their computational relevance. Existing implementations of the different InSAR stages using diverse parallel strategies and architectures are examined and their performance discussed. Furthermore, some InSAR computational schemes selected in the literature are analyzed at the level of the entire processing chain, thus emphasizing their potentialities and limitations. Therefore, the survey focuses on the inherent computational approaches enabling large-scale interferometric SAR processing, thus offering insight into some open issues, and outlining future trends in the field.
Highlights
High performance computing (HPC) is concerned with computing systems that can solve extremely complex and demanding problems in physics and engineering
The Advanced Land Observation Satellite 2 (ALOS-2) is the second generation of ALOS satellite constructed by the Japan Aerospace Exploration Agency (JAXA) and is equipped with a phased array synthetic aperture radar operating at L-band (PALSAR)
We have presented a perspective on the state of the art of the application of high performance computing (HPC)
Summary
High performance computing (HPC) is concerned with computing systems that can solve extremely complex and demanding problems in physics and engineering. Current InSAR SAR applications involve large-region coverage, multitemporal, multi-band datasets processing to achieve accurate and up-to-date information about Earth’s surface at the regional and global scale. As large-scale SAR data processing problems are concerned, it is clear the crucial role that HPC has in achieving timely up-to-date information of interest without sacrificing accuracy. We present the state-of-the-art and the most recent developments in exploiting HPC methodologies and architectures in the context of interferometric SAR processing techniques.
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