SAR target detection based on PSIFT feature clustering

Lina Zeng; Deyun Zhou; Qian Pan; Chao Lu; Ying Zhou

doi:10.1109/IGARSS.2019.8900284

SAR target detection based on PSIFT feature clustering

Lina Zeng, Deyun Zhou, Qian Pan, Chao Lu, Ying Zhou

School of Electronics and Information

Northwestern Polytechnical University Xian

Research output: Contribution to conference › Paper › peer-review

3 Scopus citations

Abstract

Aiming at the problem that it is difficult to obtain a big SAR target set with different angles, a sample-free SAR target detection method is proposed in this paper. This new method adopts PSIFT features with rotation invariance to describe the texture of potential targets, and divides the features of potential targets into target regions and non-target regions through NCM clustering. This method proposed of this paper can realize the automatic detection of SAR targets, and is also effective for targets with different orientation. The experimental results verify the feasibility and validity of the proposed method.

Original language	English
Pages	17-20
Number of pages	4
DOIs	https://doi.org/10.1109/IGARSS.2019.8900284
State	Published - 2019
Event	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan Duration: 28 Jul 2019 → 2 Aug 2019

Conference

Conference	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/Territory	Japan
City	Yokohama
Period	28/07/19 → 2/08/19

Keywords

NCM clustering
PSIFT feature
SAR images
Target detection

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10.1109/IGARSS.2019.8900284

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title = "SAR target detection based on PSIFT feature clustering",

abstract = "Aiming at the problem that it is difficult to obtain a big SAR target set with different angles, a sample-free SAR target detection method is proposed in this paper. This new method adopts PSIFT features with rotation invariance to describe the texture of potential targets, and divides the features of potential targets into target regions and non-target regions through NCM clustering. This method proposed of this paper can realize the automatic detection of SAR targets, and is also effective for targets with different orientation. The experimental results verify the feasibility and validity of the proposed method.",

keywords = "NCM clustering, PSIFT feature, SAR images, Target detection",

author = "Lina Zeng and Deyun Zhou and Qian Pan and Chao Lu and Ying Zhou",

note = "Publisher Copyright: {\textcopyright}2019 IEEE; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",

year = "2019",

doi = "10.1109/IGARSS.2019.8900284",

language = "英语",

pages = "17--20",

}

TY - CONF

T1 - SAR target detection based on PSIFT feature clustering

AU - Zeng, Lina

AU - Zhou, Deyun

AU - Pan, Qian

AU - Lu, Chao

AU - Zhou, Ying

PY - 2019

Y1 - 2019

N2 - Aiming at the problem that it is difficult to obtain a big SAR target set with different angles, a sample-free SAR target detection method is proposed in this paper. This new method adopts PSIFT features with rotation invariance to describe the texture of potential targets, and divides the features of potential targets into target regions and non-target regions through NCM clustering. This method proposed of this paper can realize the automatic detection of SAR targets, and is also effective for targets with different orientation. The experimental results verify the feasibility and validity of the proposed method.

AB - Aiming at the problem that it is difficult to obtain a big SAR target set with different angles, a sample-free SAR target detection method is proposed in this paper. This new method adopts PSIFT features with rotation invariance to describe the texture of potential targets, and divides the features of potential targets into target regions and non-target regions through NCM clustering. This method proposed of this paper can realize the automatic detection of SAR targets, and is also effective for targets with different orientation. The experimental results verify the feasibility and validity of the proposed method.

KW - NCM clustering

KW - PSIFT feature

KW - SAR images

KW - Target detection

UR - http://www.scopus.com/inward/record.url?scp=85113892248&partnerID=8YFLogxK

U2 - 10.1109/IGARSS.2019.8900284

DO - 10.1109/IGARSS.2019.8900284

M3 - 论文

AN - SCOPUS:85113892248

SP - 17

EP - 20

T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019

Y2 - 28 July 2019 through 2 August 2019

ER -

SAR target detection based on PSIFT feature clustering

Abstract

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Keywords

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