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GPU-Based Nonlocal Filtering for Large Scale SAR Processing

Gerald Baier, Xiao Xiang Zhu
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Oberpfaffenhofen, 82234 Wessling, Germany
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016

@inproceedings{baier2016gpu,

   title={GPU-based nonlocal filtering for large scale SAR processing},

   author={Baier, Gerald and Zhu, Xiao Xiang},

   booktitle={Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International},

   pages={7608–7611},

   year={2016},

   organization={IEEE}

}

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In the past few years nonlocal filters have emerged as a serious contender for denoising synthetic aperture radar (SAR) images, offering superior noise reduction and detail preservation compared to many other filters. In this manuscript we analyze how nonlocal filters, whose computational costs were so far prohibitive for large scale processing, can be implemented efficiently on graphics processing units (GPU). As a case study NL-SAR, a state of the art SAR filter, is implemented to run on a NVIDIA Tesla K40. We describe the appeal of GPUs, or any other coprocessor, for nonlocal filters. Nonlocal filtering of TanDEM-X interferograms for generating digital elevation models with a higher resolution and accuracy is given as an application that benefits from efficient and fast nonlocal filtering.
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