GPU-based nonlocal filtering for large scale SAR processing

Gerald Baier; Xiao Xiang Zhu

doi:10.1109/IGARSS.2016.7730984

GPU-based nonlocal filtering for large scale SAR processing

Gerald Baier, Xiao Xiang Zhu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

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.

Original language	English
Title of host publication	2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	7608-7611
Number of pages	4
ISBN (Electronic)	9781509033324
DOIs	https://doi.org/10.1109/IGARSS.2016.7730984
State	Published - 1 Nov 2016
Externally published	Yes
Event	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China Duration: 10 Jul 2016 → 15 Jul 2016

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2016-November

Conference

Conference	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Country/Territory	China
City	Beijing
Period	10/07/16 → 15/07/16

Keywords

denoising
graphics processing units (GPU)
nonlocal
synthetic aperture radar

Access to Document

10.1109/IGARSS.2016.7730984

Cite this

Baier, G., & Zhu, X. X. (2016). GPU-based nonlocal filtering for large scale SAR processing. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings (pp. 7608-7611). Article 7730984 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2016-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2016.7730984

@inproceedings{35f9a73619994da2881ff3da53ed5747,

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

abstract = "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.",

keywords = "denoising, graphics processing units (GPU), nonlocal, synthetic aperture radar",

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

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 ; Conference date: 10-07-2016 Through 15-07-2016",

year = "2016",

month = nov,

day = "1",

doi = "10.1109/IGARSS.2016.7730984",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "7608--7611",

booktitle = "2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings",

}

Baier, G & Zhu, XX 2016, GPU-based nonlocal filtering for large scale SAR processing. in 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings., 7730984, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2016-November, Institute of Electrical and Electronics Engineers Inc., pp. 7608-7611, 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016, Beijing, China, 10/07/16. https://doi.org/10.1109/IGARSS.2016.7730984

GPU-based nonlocal filtering for large scale SAR processing. / Baier, Gerald; Zhu, Xiao Xiang.
2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 7608-7611 7730984 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2016-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - GPU-based nonlocal filtering for large scale SAR processing

AU - Baier, Gerald

AU - Zhu, Xiao Xiang

PY - 2016/11/1

Y1 - 2016/11/1

N2 - 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.

AB - 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.

KW - denoising

KW - graphics processing units (GPU)

KW - nonlocal

KW - synthetic aperture radar

UR - http://www.scopus.com/inward/record.url?scp=85007420848&partnerID=8YFLogxK

U2 - 10.1109/IGARSS.2016.7730984

DO - 10.1109/IGARSS.2016.7730984

M3 - Conference contribution

AN - SCOPUS:85007420848

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 7608

EP - 7611

BT - 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016

Y2 - 10 July 2016 through 15 July 2016

ER -

GPU-based nonlocal filtering for large scale SAR processing

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this