Kernel regression-based background predicting method for target detection in SAR image

Yanfeng Gu; Xing Liu; Jinglong Han; Ye Zhang

doi:10.1109/IGARSS.2009.5417446

Kernel regression-based background predicting method for target detection in SAR image

Yanfeng Gu^*
, Xing Liu
, Jinglong Han
, Ye Zhang

^*Corresponding author for this work

School of Electronics and Information Engineering, Harbin Institute of Technology

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

1 Scopus citations

Abstract

Target detection with SAR image is one of important research topics in remote sensing. In this paper, a kernel regression-based predicting method is proposed for target detection in SAR image. Badly speckle noise and background clutter are two main factors which make the target detection with SAR image difficult. In the proposed method, the kernel regression on local image is used to exactly predict the background interferences and make Gaussian assumption in conventional detector better followed after kernel regression-based prediction and suppression of background clutter. Thus, final CFAR detection is performed on the background clutter-removed SAR image. Experiments conducted on real SAR image show that the proposed algorithm can effectively predict and suppress background clutters, and greatly improve the performance of the conventional CFAR detector.

Original language	English
Title of host publication	2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings
Pages	IV593-IV596
DOIs	https://doi.org/10.1109/IGARSS.2009.5417446
State	Published - 2009
Externally published	Yes
Event	2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Cape Town, South Africa Duration: 12 Jul 2009 → 17 Jul 2009

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	4

Conference

Conference	2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009
Country/Territory	South Africa
City	Cape Town
Period	12/07/09 → 17/07/09

Keywords

Background prediction
CFAR
Kernel regression
SAR images
Target detection

Access to Document

10.1109/IGARSS.2009.5417446

Cite this

Gu, Y., Liu, X., Han, J., & Zhang, Y. (2009). Kernel regression-based background predicting method for target detection in SAR image. In 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings (pp. IV593-IV596). Article 5417446 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 4). https://doi.org/10.1109/IGARSS.2009.5417446

@inproceedings{981908c37a8f4c35b6ecd5eb54b97337,

title = "Kernel regression-based background predicting method for target detection in SAR image",

abstract = "Target detection with SAR image is one of important research topics in remote sensing. In this paper, a kernel regression-based predicting method is proposed for target detection in SAR image. Badly speckle noise and background clutter are two main factors which make the target detection with SAR image difficult. In the proposed method, the kernel regression on local image is used to exactly predict the background interferences and make Gaussian assumption in conventional detector better followed after kernel regression-based prediction and suppression of background clutter. Thus, final CFAR detection is performed on the background clutter-removed SAR image. Experiments conducted on real SAR image show that the proposed algorithm can effectively predict and suppress background clutters, and greatly improve the performance of the conventional CFAR detector.",

keywords = "Background prediction, CFAR, Kernel regression, SAR images, Target detection",

author = "Yanfeng Gu and Xing Liu and Jinglong Han and Ye Zhang",

year = "2009",

doi = "10.1109/IGARSS.2009.5417446",

language = "英语",

isbn = "9781424433957",

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

pages = "IV593--IV596",

booktitle = "2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings",

note = "2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 ; Conference date: 12-07-2009 Through 17-07-2009",

}

Gu, Y, Liu, X, Han, J & Zhang, Y 2009, Kernel regression-based background predicting method for target detection in SAR image. in 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings., 5417446, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 4, pp. IV593-IV596, 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009, Cape Town, South Africa, 12/07/09. https://doi.org/10.1109/IGARSS.2009.5417446

Kernel regression-based background predicting method for target detection in SAR image. / Gu, Yanfeng; Liu, Xing; Han, Jinglong et al.
2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings. 2009. p. IV593-IV596 5417446 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 4).

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

TY - GEN

T1 - Kernel regression-based background predicting method for target detection in SAR image

AU - Gu, Yanfeng

AU - Liu, Xing

AU - Han, Jinglong

AU - Zhang, Ye

PY - 2009

Y1 - 2009

N2 - Target detection with SAR image is one of important research topics in remote sensing. In this paper, a kernel regression-based predicting method is proposed for target detection in SAR image. Badly speckle noise and background clutter are two main factors which make the target detection with SAR image difficult. In the proposed method, the kernel regression on local image is used to exactly predict the background interferences and make Gaussian assumption in conventional detector better followed after kernel regression-based prediction and suppression of background clutter. Thus, final CFAR detection is performed on the background clutter-removed SAR image. Experiments conducted on real SAR image show that the proposed algorithm can effectively predict and suppress background clutters, and greatly improve the performance of the conventional CFAR detector.

AB - Target detection with SAR image is one of important research topics in remote sensing. In this paper, a kernel regression-based predicting method is proposed for target detection in SAR image. Badly speckle noise and background clutter are two main factors which make the target detection with SAR image difficult. In the proposed method, the kernel regression on local image is used to exactly predict the background interferences and make Gaussian assumption in conventional detector better followed after kernel regression-based prediction and suppression of background clutter. Thus, final CFAR detection is performed on the background clutter-removed SAR image. Experiments conducted on real SAR image show that the proposed algorithm can effectively predict and suppress background clutters, and greatly improve the performance of the conventional CFAR detector.

KW - Background prediction

KW - CFAR

KW - Kernel regression

KW - SAR images

KW - Target detection

UR - https://www.scopus.com/pages/publications/77951267890

U2 - 10.1109/IGARSS.2009.5417446

DO - 10.1109/IGARSS.2009.5417446

M3 - 会议稿件

AN - SCOPUS:77951267890

SN - 9781424433957

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - IV593-IV596

BT - 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings

T2 - 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009

Y2 - 12 July 2009 through 17 July 2009

ER -

Kernel regression-based background predicting method for target detection in SAR image

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this