基于谱残差和梯度纹理融合特征的舰船检测

李庆峰; 何斌; 王文胜; 苏畅; 韩玺钰; 梁怀丹

doi:10.3788/YJYXS20193408.0803

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基于谱残差和梯度纹理融合特征的舰船检测

图像处理 | 更新时间：2020-12-14

- 基于谱残差和梯度纹理融合特征的舰船检测
- Ship detection based on spectral residual and gradient texture fusion features
- 液晶与显示 2019年34卷第8期页码：803-815
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
  2.中国科学院大学, 北京 100049
  3.北京信息科技大学机电工程学院, 北京 100192
- 作者简介：
  
  [ "李庆峰(1994-), 男, 安徽合肥人, 硕士研究生, 2016年于四川大学获得学士学位, 研究方向为图像处理, 模式识别, 目标检测。E-mail:lqingfeng_9157@qq.com" ]
  [ "何斌(1961-), 男, 吉林长春人, 博士, 研究员, 1990年于北京理工大学获工学博士学位, 2001年于中科院长春光机所获得理学博士学位, 研究方向为遥感图像压缩处理, 卫星成像技术。E-mail:Hbemail@163.com" ]
- 基金信息：
  
  国家自然科学基金(61801455);National Natural Science Foundation of China(61801455)
- DOI：10.3788/YJYXS20193408.0803
  中图分类号： TP394.1
- 收稿日期：2019-02-25，
  
  录用日期：2019-5-24，
  
  纸质出版日期：2019-08-05
- 稿件说明：
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李庆峰, 何斌, 王文胜, 等. 基于谱残差和梯度纹理融合特征的舰船检测[J]. 液晶与显示, 2019,34(8):803-815.

Qing-feng LI, Bin HE, Wen-sheng WANG, et al. Ship detection based on spectral residual and gradient texture fusion features[J]. Chinese journal of liquid crystals and displays, 2019, 34(8): 803-815.
李庆峰, 何斌, 王文胜, 等. 基于谱残差和梯度纹理融合特征的舰船检测[J]. 液晶与显示, 2019,34(8):803-815. DOI： 10.3788/YJYXS20193408.0803.

Qing-feng LI, Bin HE, Wen-sheng WANG, et al. Ship detection based on spectral residual and gradient texture fusion features[J]. Chinese journal of liquid crystals and displays, 2019, 34(8): 803-815. DOI： 10.3788/YJYXS20193408.0803.

摘要

本文提出一种谱残差和梯度纹理融合特征来检测可见光遥感图像中复杂海面背景下的舰船目标。首先，使用谱残差模型定位疑似舰船目标，并采用自适应动态阈值法获取候选目标区域。然后，根据舰船的形状特点，对梯度方向直方图特征进行改进，设计出表征舰船特性的梯度方向特征。同时，将提取候选目标区域的统一化LBP特征的方差以及灰度共生矩阵特征相结合来描述舰船的纹理信息，得到30维特征向量。最后，通过训练好的AdaBoost分类器来完成舰船目标鉴别。本文的检测算法，针对尺寸为1 024×1 024的可见光遥感图像，检测时间为4.792 6 s，检测精度为95.51%，召回率为96.65%。实验结果表明：本文算法能准确提取海面舰船目标，获取舰船目标的数量和位置信息，从检测时间和精度上来看，可以作为实际工程参考。

Abstract

A spectral residual and gradient texture fusion feature is proposed to detect ship targets in complex sea backgrounds in visible-light-remote sensing image. Firstly

the spectral residual model is used to locate the suspected ship target

while the adaptive dynamic threshold method is used to obtain the candidate target region. Then

according to the shape characteristics of the ship

the gradient direction histogram features are improved to design the gradient direction characteristics that show the feature of the ship. Meanwhile

the variance of the uniform LBP features and the gray level co-occurrence matrix features of the candidate target regions are extracted to describe the ship's texture information

and the 30-dimensional feature vector is obtained. Finally

the ship's target identification is accomplished through the trained AdaBoost classifier. For visible-light-remote sensing image with size 1 024×1 024

the detection time is 4.792 6 s

the detection accuracy is 95.51% and the recall rate is 96.65% via using the detection algorithm of this paper. The experimental results show that the proposed algorithm can successfully extract the surface targets of ships

and obtain the number and position information of ship targets accurately. From the perspective of detection time and accuracy

it can be used as a reference for practical engineering.

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references

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