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

被引：1
作者：
Ruifu Wang
Jie Li
Yaping Duan
Hongjun Cao
Yingjie Zhao
机构：
[1] Shandong University of Science and Technology,College of Geomatics
[2] SBSM,The Key Lab of Surveying and Mapping Technology on Island and Reef
[3] Geomatics Center of Zhejiang,undefined
来源：
Journal of the Indian Society of Remote Sensing | 2018年 / 46卷
关键词：
CFAR; CNN; Ship detection; Deep learning;
D O I：
暂无
中图分类号：
学科分类号：
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.
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收藏
页码：1413 / 1421
页数：8
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