The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Apr. 13, 2021

Filed:

Aug. 02, 2019
Applicant:

Wuyi University, Guangdong, CN;

Inventors:

Yikui Zhai, Guangdong, CN;

Cuilin Yu, Guangdong, CN;

Zhiyong Hong, Guangdong, CN;

Yanyang Liang, Guangdong, CN;

Tianlei Wang, Guangdong, CN;

Zhongxin Yu, Guangdong, CN;

Wenbo Deng, Guangdong, CN;

Junying Gan, Guangdong, CN;

Zilu Ying, Guangdong, CN;

Junying Zeng, Guangdong, CN;

Assignee:

WUYI University, Guangdong, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2006.01); G01S 13/90 (2006.01); G06K 9/00 (2006.01); G06K 9/32 (2006.01); G06K 9/46 (2006.01); G06T 3/40 (2006.01); G06T 3/60 (2006.01); G06T 5/00 (2006.01);
U.S. Cl.
CPC ...
G06K 9/629 (2013.01); G01S 13/9027 (2019.05); G06K 9/0063 (2013.01); G06K 9/3233 (2013.01); G06K 9/46 (2013.01); G06K 9/6256 (2013.01); G06K 9/6262 (2013.01); G06T 3/40 (2013.01); G06T 3/60 (2013.01); G06T 5/001 (2013.01); G06T 2207/20081 (2013.01);
Abstract

Disclosed are method and apparatus for SAR image recognition based on multi-scale features and broad learning. A region of interest of an original SAR image is extracted by centroid localization, the image is rotated and added with noise for enhancing the data volume, the image is downsampled, LBP features and PPQ features are extracted, an LBP feature vector Xand an LPQ feature vector Xare cascaded to achieve dimension reduction by principal component analysis to obtain a fusion feature data X, the fusion feature data Xis input to a broad learning network for image recognition and a recognition result is output. By fusing the LBP features and the LPQ features, complementary information is fully utilized and redundant information is reduced. The broad learning network is used to improve the training speed and reduce the time cost. As a result, the recognition effect is more stable, robust and reliable.


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