Ship detection based on spectral residual and gradient texture fusion features

Qing-feng LI; Bin HE; Wen-sheng WANG; Chang SU; Xi-yu HAN; Huai-dan LIANG

doi:10.3788/YJYXS20193408.0803

您当前的位置：

首页 >

文章列表页 >

Ship detection based on spectral residual and gradient texture fusion features

Image Processing | 更新时间：2020-12-14

- Ship detection based on spectral residual and gradient texture fusion features
- Chinese Journal of Liquid Crystals and Displays Vol. 34, Issue 8, Pages: 803-815(2019)
- 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：
- 基金信息：
- DOI：10.3788/YJYXS20193408.0803
  CLC： TP394.1
- Received：25 February 2019，
  
  Accepted：24 May 2019，
  
  Published：05 August 2019
- 稿件说明：
移动端阅览
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：

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.

关键词

Keywords

references

王彦情, 马雷, 田原.光学遥感图像舰船目标检测与识别综述[J].自动化学报, 2011, 37(9):1029-1039.

WANG Y Q, MA L, TIAN Y. State-of-the-art of ship detection and recognition in optical remotely sensed imagery[J]. Acta Automatica Sinica , 2011, 37(9):1029-1039. (in Chinese)

唐沐恩, 林挺强, 文贡坚.遥感图像中舰船检测方法综述[J].计算机应用研究, 2011, 28(1):29-36.

TANG M E, LIN T Q, WEN G J. Overview of ship detection methods in remote sensingimage[J]. Application Research of Computers , 2011, 28(1):29-36. (in Chinese)

ZHU C R, ZHOU H, WANG R S, et al . A novel hierarchical method of ship detection from spaceborne optical image based on shape and texture features[J]. IEEE Transactions on Geoscience and Remote Sensing , 2010, 48(9):3446-3456.

苏丽, 庞迪.全景海域图像中的圆形海天线提取[J].光学精密工程, 2015, 23(11):3279-3288.

SU L, PANG D. Circle sea-sky-line extraction in panoramic images[J]. Optics and Precision Engineering , 2015, 23(11):3279-3288. (in Chinese)

LI Z M, YANG D Q, CHEN Z Z. Multi-layer sparse coding based ship detection for remote sensing images[C]// Proceedings of 2015 IEEE International Conference on Information Reuse and Integration . San Francisco, CA, USA: IEEE, 2015: 122-125.

SONG Z N, SUI H G, WANG Y J. Automatic ship detection for optical satellite images based on visual attention model and LBP[C]// Proceedings of 2014 IEEE Workshop on Electronics, Computer and Applications . Ottawa, ON, Canada: IEEE, 2014: 722-725.

TING N, BIN H, GUO L B, et al . A method of ship detection under complex background[J]. International Journal of Geo - Information , 2017, 6(9):159.

王慧利, 朱明, 蔺春波, 等.光学遥感图像中复杂海背景下的舰船检测[J].光学精密工程, 2018, 26(3):723-732.

WANG H L, ZHU M, LIN C B, et al . Ship detection of complex sea background in optical remote sensing images[J]. Optics and Precision Engineering , 2018, 26(3):723-732. (in Chinese)

QI S X, MA J, LIN J, et al . Unsupervised ship detection based on saliency and S-HOG descriptor from optical satellite images[J]. IEEE Geoscience and Remote Sensing Letters , 2015, 12(7):1451-1455.

HOU X D. ZHANG L P. Saliency detection: a spectral residual approach[C]// Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis: IEEE, 2007: 1-8.

OJALA T, PIETIKAINEN M, MAENPAA T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2002, 24(7):971-987.

王民, 王静, 王羽筌.一种多尺度灰度共生矩阵的纹理特征提取算法[J].液晶与显示, 2016, 31(10):967-972.

WANG M, WANG J, WANG Y S. Multi-scale algorithm of texture feature extraction based on gray-level co-occurrence matrix[J]. Chinese Journal of Liquid Crystals and Displays , 2016, 31(10):967-972. (in Chinese)

HARALICK R M, SHANMUGAM K, DINSTEIN I. Textural features for image classification[J]. IEEE Transactions on Systems , Man , and Cybernetics , 1973, SMC-3(6):610-621.

ULABY F T, KOUYATE F, BRISCO B, et al . Textural infornation in SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing , 1986, GE-24(2):235-245.

DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]// Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . San Diego, CA, USA: IEEE, 2005: 886-893.

杜慧, 张涛, 张叶, 等.基于空频域结合的显著目标检测[J].液晶与显示, 2016, 31(9):913-920.

DU H, ZHANG T, ZHANG Y, et al . Saliency detection based on frequency domain combined with spatial domain[J]. Chinese Journal of Liquid Crystals and Displays , 2016, 31(9):913-920. (in Chinese)

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Vane pump assembly quality detection based on gabor and gray level co-occurrence matrix hybrid characteristics

Related Author

Kang LIU

Xiao-lin CHEN

Yan-jun LIU

Hao LIANG

Related Institution

No data

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China 130033 Postal code：130033
Tel：0431-86176059 Email：yjxs@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰