基于卷积神经网络的遥感图像舰船目标检测

doi:10.13700/j.bh.1001-5965.2016.0755

北京航空航天大学学报 ›› 2017, Vol. 43 ›› Issue (9): 1841-1848.doi: 10.13700/j.bh.1001-5965.2016.0755

基于卷积神经网络的遥感图像舰船目标检测

黄洁^1,2, 姜志国^1,2, 张浩鹏^1,2, 姚远^1,2

1. 北京航空航天大学宇航学院, 北京 100083;
2. 北京航空航天大学数字媒体北京市重点实验室, 北京 100083

收稿日期:2016-09-26 出版日期:2017-09-20 发布日期:2017-04-07
通讯作者: E-mail:jiangzg@buaa.edu.cn E-mail:jiangzg@buaa.edu.cn
作者简介:黄洁,女,硕士研究生;主要研究方向:图像目标检测;姜志国,男,博士,教授,博士生导师;主要研究方向:遥感图像处理、医学图像处理;张浩鹏,男,博士,讲师;主要研究方向:目标检测识别、三维重建及姿态测量;姚远,男,博士研究生;主要研究方向:遥感图像目标检测与识别
基金资助:
国家重点研发计划（2016YFB0501300，2016YFB0501302）；国家自然科学基金（61501009，61371134，61071137）；航天科技创新基金

Ship object detection in remote sensing images using convolutional neural networks

HUANG Jie^1,2, JIANG Zhiguo^1,2, ZHANG Haopeng^1,2, YAO Yuan^1,2

1. School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing 100083, China;
2. Beijing Key Laboratory of Digital Media, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

Received:2016-09-26 Online:2017-09-20 Published:2017-04-07
Supported by:
National Key Research and Development Program of China (2016YFB0501300, 2016YFB0501302); National Natural Science Foundation of China (61501009, 61371134, 61071137); Aerospace Science and Technology Innovation Fund of CASC

摘要/Abstract

摘要： 针对遥感图像背景复杂、受环境因素影响大的问题，提出一种将卷积神经网络（CNN）与支持向量机（SVM）相结合的舰船目标检测方法，利用卷积神经网络可自主提取图像特征并进行学习的优点，避免了复杂的特征选择和提取过程，在复杂海况背景图像的处理中体现出较优的性能；同时，由于军舰样本获取难度大，应用迁移学习的概念，利用大量民船样本辅助军舰目标的检测，取得较好的效果。通过参数调整与实验验证，此方法在自行建立的测试集上检测率达到90.59%，对光照、环境等外界因素具有一定程度的鲁棒性。

关键词: 卷积神经网络(CNN), 支持向量机(SVM), 舰船检测, 特征提取, 迁移学习

Abstract: Object detection in remote sensing images is mostly suffered from complex background and multiple interferences of environment. In this paper, a new method of ship detection is proposed, which combines convolutional neural networks (CNN) and support vector machine (SVM) to complete the ship detection task. Convolutional layers were adopted for feature extraction, taking advantages of independent feature extraction of CNNs and avoiding the process of complicated feature selection and extraction, which leads to better detection performance in complex background images. Meanwhile, since the samples of warship are difficult to acquire, samples of civil ship were employed as assistant samples for warship detection based on transfer learning theory. And this transfer learning method is proved to be effective by the experimental results, which performs better than the model trained only with warship samples. According to the parameter tuning and experimental validation, this method achieves a precision of 90.59% on testing dataset established by ourselves. In conclusion, this method possesses feasibility and robustness under different conditions of illumination and environment.

Key words: convolutional neural networks (CNN), support vector machine (SVM), ship detection, feature extraction, transfer learning

中图分类号:

TP391.4

黄洁, 姜志国, 张浩鹏, 姚远. 基于卷积神经网络的遥感图像舰船目标检测[J]. 北京航空航天大学学报, 2017, 43(9): 1841-1848.

HUANG Jie, JIANG Zhiguo, ZHANG Haopeng, YAO Yuan. Ship object detection in remote sensing images using convolutional neural networks[J]. JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2017, 43(9): 1841-1848.

参考文献

[1] 陈韬亦,陈金勇,赵和鹏.基于Ecogniton的光学遥感图像舰船目标检测[J].无线电工程,2013,43(11):11-13.CHEN T Y,CHEN J Y,ZHAO H P.Ecognition-based ship detection on optical remote sensing images[J].Radio Engineering,2013,43(11):11-13(in Chinese).
[2] 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).
[3] ELDHUSET K.Automatic ship and ship wake detection in space borne SAR images from coastal regions[C]//Remote Sensing:Moving Toward the 21st Century.Piscataway,NJ:IEEE Press,1988,3:1529-1533.
[4] ZHANG W,BIAN C,ZHAO X,et al.Ship target segmentation and detection in complex optical remote sensing image based on component tree characteristics discrimination[C]//Optoelectronic Imaging and Multimedia Technology Ⅱ.Bellingham,WA:SPIE,2012.
[5] 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 sensing image[J].Application Research of Computers,2011,28(1):29-36(in Chinese).
[6] QI S,MA J,LIN J,et al.Unsupervised ship detection based on saliency and S-HOG descriptor from optical satellite images[J].Geoscience and Remote Sensing Letters,2015,12(7):1451-1455.
[7] TANG J,DENG C,HUANG G,et al.Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(3):1174-1185.
[8] PROIA N,PAGE V.Characterization of a Bayesian ship detection method in optical satellite images[J].Geoscience and Remote Sensing Letters,2010,7(2):226-230.
[9] SHI Z,YU X,JIANG Z,et al.Ship detection in high-resolution optical imagery based on anomaly detector and local shape feature[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(8):4511-4523.
[10] KRIZHEVSKY A,SUTSKEVER I,HINTON G.ImageNet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems.Stateline:NIPS,2012:1097-1105.
[11] BOUVRIE J.Notes on convolutional neural networks[R/OL].Cambridge:Massachusetts Institute of Technology,2006[2016-09-15].http://cogprints.org/5869/1/cnn_tutorial.pdf.
[12] HUBEL D H,WIESEL T N.Receptive fields,binocular interaction and functional architecture in the cat's visual cortex[J].The Journal of Physiology,1962,160(1):106-154.
[13] FUKUSHIMA K.A hierarchical neural network model for associative memory[J].Biological Cybemetics,1984,50(2):105-113.
[14] GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE Press,2014:580-587.
[15] CORTES C,VAPNIK V.Support-vector networks[J].Machine Learning,1995,20(3):273-297.

基于卷积神经网络的遥感图像舰船目标检测

Ship object detection in remote sensing images using convolutional neural networks

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	孙宇航, 曾国奇, 刘春辉, 张多纳. 基于深度学习的无人机数据链信噪比估计算法[J]. 北京航空航天大学学报, 2019, 45(9): 1855-1863.
[2]	谭治学, 钟诗胜, 林琳. 多源数据融合的民航发动机修后性能预测[J]. 北京航空航天大学学报, 2019, 45(6): 1106-1113.
[3]	孙波, 梁勇, 汉牟田, 杨磊, 荆丽丽, 俞永庆. 基于GA-SVM的GNSS-IR土壤湿度反演方法[J]. 北京航空航天大学学报, 2019, 45(3): 486-492.
[4]	张新峰, 郭宇桐, 蔡轶珩, 孙萌. 基于DCNN和全连接CRF的舌图像分割算法[J]. 北京航空航天大学学报, 2019, 45(12): 2364-2374.
[5]	翟宇轩, 刘怡桑, 徐艺文, 陈忠辉, 房颖, 赵铁松. 基于HTTP自适应流媒体传输的3D视频质量评价[J]. 北京航空航天大学学报, 2019, 45(12): 2456-2462.
[6]	张梦琴, 孟权令, 张维刚. 基于深度视觉语义嵌入的视频缩略图推荐[J]. 北京航空航天大学学报, 2019, 45(12): 2479-2486.
[7]	佟骏超, 吴熙林, 丁丹丹. 时空域上下文学习的视频多帧质量增强方法[J]. 北京航空航天大学学报, 2019, 45(12): 2506-2513.
[8]	张振良, 刘君强, 黄亮, 张曦. 基于半监督迁移学习的轴承故障诊断方法[J]. 北京航空航天大学学报, 2019, 45(11): 2291-2300.
[9]	贾瑞明, 刘圣杰, 李锦涛, 王赟豪, 潘海侠. 基于编解码双路卷积神经网络的视觉自定位方法[J]. 北京航空航天大学学报, 2019, 45(10): 1965-1972.
[10]	曹惠玲, 高升, 薛鹏. 基于多分类AdaBoost的航空发动机故障诊断[J]. 北京航空航天大学学报, 2018, 44(9): 1818-1825.
[11]	陈唯实, 闫军, 张洁, 李敬. 基于支持向量机的机场智能驱鸟决策[J]. 北京航空航天大学学报, 2018, 44(7): 1547-1553.
[12]	王俊, 郑彤, 雷鹏, 张原, 樵明朗. 基于卷积神经网络的手势动作雷达识别方法[J]. 北京航空航天大学学报, 2018, 44(6): 1117-1123.
[13]	韩京冶, 许福, 陈志泊, 刘辉. 一种基于深度学习的交互式电话号码识别方法[J]. 北京航空航天大学学报, 2018, 44(5): 1074-1080.
[14]	韩冬, 张学军, 聂尊礼, 管祥民. 一种基于SVM的低空飞行冲突探测算法[J]. 北京航空航天大学学报, 2018, 44(3): 576-582.
[15]	王星, 呙鹏程, 田元荣, 王玉冰. 基于BDS-GD的低截获概率雷达信号识别[J]. 北京航空航天大学学报, 2018, 44(3): 583-592.