Ship Detection in SAR Imagery Based on Density and Clustering

被引：0
|
作者：
Hao, Mengxi [1 ]
Luo, Yang [2 ]
Zhai, Wenjing [1 ]
Jin, Songzhi [1 ]
机构：
[1] Beijing Aerosp Automat Control Inst, Natl Key Lab Sci & Technol Aerosp Intelligent Con, Beijing, Peoples R China
[2] HiWing Gen Aviat Equipment Co Ltd, Lab UAV Flight Control & Flight Qual, Beijing, Peoples R China
来源：
2020 CHINESE AUTOMATION CONGRESS (CAC 2020) | 2020年
关键词：
SAR imagery; ship detection; density; clustering;
D O I：
10.1109/CAC51589.2020.9327011
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, an algorithm for ship detection based on density and clustering is proposed for SAR imagery. The algorithm includes density map generation, local extremums extraction, target quasi-centers classification, suspected area identification, and detection results determination based on the quasi-centers and the suspected area. The proposed algorithm can effectively reduce the impact of sea clutter, outspread shadows and crossed sidelobes in SAR imagery, and provide more accurate detection results. Finally, the effecttiveness of the algorithm is verified by simulation.
引用
收藏
页码：6974 / 6977
页数：4
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