Study on the Combined Application of CFAR and Deep Learning in Ship Detection

被引：24

作者：

Wang, Ruifu ^{[1
,2
]}

Li, Jie ^{[3
]}

Duan, Yaping ^{[1
]}

Cao, Hongjun ^{[1
]}

Zhao, Yingjie ^{[1
]}

机构：

[1] Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Peoples R China

[2] SBSM, Key Lab Surveying & Mapping Technol Isl & Reef, Qingdao 266590, Peoples R China

[3] Geomat Ctr Zhejiang, Hangzhou 310012, Zhejiang, Peoples R China

来源：

JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING | 2018年 / 46卷 / 09期

关键词：

CFAR; CNN; Ship detection; Deep learning; SAR; SCHEME;

D O I：

10.1007/s12524-018-0787-x

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

To maintain national socio-economic development and maritime rights and interests, it is necessary to obtain the space location information of various ships. Therefore, it is important to detect the locations of ships accurately and rapidly. At present, ship detection is mainly carried out by combining satellite remote sensing imaging with constant false alarm rate (CFAR) detection. However, with the rapid development of satellite remote sensing technology, remote sensing data have gradually begun to show the characteristics of "big data"; additionally, the accuracy and speed of ship detection can be improved by analysing big data, such as by deep learning. Thus, a ship detection algorithm that combines CFAR and CNN is proposed based on the CFAR global detection algorithm and image recognition with the CNN model. Compared with the multi-level CFAR algorithm that is based on multithreading, the algorithm in this paper is more suitable for application to ship detection systems.

引用

页码：1413 / 1421

页数：9

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