基于视觉显著性的无监督海面舰船检测与识别

徐芳; 刘晶红; 曾冬冬; 王宣

doi:10.3788/OPE.20172505.1300

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基于视觉显著性的无监督海面舰船检测与识别

信息科学 | 更新时间：2020-07-07

- 基于视觉显著性的无监督海面舰船检测与识别
- Detection and identification of unsupervised ships and warships on sea surface based on visual saliency
- 光学精密工程 2017年25卷第5期页码：1300-1311
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所, 航空光学成像与测量重点实验室, 吉林长春 130033
  2.中国科学院大学, 北京 100049
- 作者简介：
  
  [ "徐芳 (1987-), 女, 山东日照人, 博士研究生, 2011年于吉林大学获得学士学位, 2014年于中国科学院长春光学精密机械与物理研究所获得硕士学位, 主要从事航空航天成像目标检测与识别等方面的研究。E-mail:xufang59@126.com" ]
  刘晶红 (1967-), 女, 吉林长春人, 研究员, 博士生导师, 1990年于长春理工大学获得学士学位, 2005年于中国科学院长春光学精密机械与物理研究所获得硕士学位, 主要从事机载光电成像测量设备方面的研究。E-mail:liu1577@126.com LIU Jing-hong, E-mail:liu1577@126.com
- 基金信息：
  
  国家自然科学基金资助项目(60902067);吉林省重大科技攻关项目(11ZDGG001)
- DOI：10.3788/OPE.20172505.1300
  中图分类号： TP391.4
- 收稿日期：2016-08-24，
  
  录用日期：2016-10-13，
  
  纸质出版日期：2017-05-25
- 稿件说明：
移动端阅览
徐芳, 刘晶红, 曾冬冬, 等. 基于视觉显著性的无监督海面舰船检测与识别[J]. 光学精密工程, 2017,25(5):1300-1311.

Fang XU, Jing-hong LIU, Dong-dong ZENG, et al. Detection and identification of unsupervised ships and warships on sea surface based on visual saliency[J]. Optics and precision engineering, 2017, 25(5): 1300-1311.
徐芳, 刘晶红, 曾冬冬, 等. 基于视觉显著性的无监督海面舰船检测与识别[J]. 光学精密工程, 2017,25(5):1300-1311. DOI： 10.3788/OPE.20172505.1300.

Fang XU, Jing-hong LIU, Dong-dong ZENG, et al. Detection and identification of unsupervised ships and warships on sea surface based on visual saliency[J]. Optics and precision engineering, 2017, 25(5): 1300-1311. DOI： 10.3788/OPE.20172505.1300.

摘要

在航天航空光学遥感舰船目标检测中，受大气、光照、云雾和海岛等海面不确定条件的影响，传统的舰船检测方法存在检测效率低和可靠性差等问题，因此，本文提出一种无监督海面舰船目标自动检测方法。该方法以视觉显著性为依据，结合多显著性检测模型快速搜索海面目标，生成显著图后对其进行粗分割，对提取的目标切片做标记并进行精细分割，利用改进的Hough变换旋转目标主轴以保证目标对Y轴的对称性；对可能检测到的厚重云层和岛屿等伪目标使用梯度方向特征进行鉴别，通过判定目标在360°范围内8个区间的梯度幅度统计值，确认舰船目标及去除伪目标。实验结果表明，该舰船检测方法能够成功提取海面上大小不同，位置随机分布的舰船目标，准确获取舰船目标的数量和位置信息，在大量真实光学遥感图像上的测试结果显示，本文方法检测准确率高于93%，通过目标鉴别处理，剔除伪目标后，虚警率可低于4%，鲁棒性较强。

Abstract

In target detection on aerospace optical remote sensing

due to the interference of uncertain conditions on sea surface such as atmosphere

solar radiation

cloud and mist

islands and others

traditional ship detection methods always have some defects such as low detection efficiency

poor reliability. Therefore

the author proposed an unsupervised ship automatic detection method. In this method

visual saliency was combined with multi-saliency detection model for fast searching of sea-surface targets; after saliency map was generated

a rough segmentation was conducted on it

then extracted target slice was marked and fine segmentation was implemented

subsequently

improved Hough transformation was used to rotate principal axis of target for ensuring the symmetry of targets to Y axis; the characteristics of gradient direction was applied to recognize phony targets such as thick clouds layer

islands and others that may be detected

the gradient and amplitude statistical value of those targets in 8 intervals on all directions were judged to identify target ships and warships and eliminate phony targets. The experimental result indicates that the detection method of ships and warships can be used to successfully extract target ships and warships which are in different size and random distributed on sea surface for obtaining accurate quantity and location information about them. In test on a large number of authentic optical remote sensing pictures

the detection accuracy rate of proposed method is higher than 93%

while the false alarm rate is lower than 4% through target identification and treatment and phony target elimination

which has strong robustness.

关键词

Keywords

references

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