Research on remote sensing image ship detection and identification based on Faster R-CNN

doi:10.13474/j.cnki.11-2246.2021.339

Abstract

Abstract: With the development of remote sensing, remote sensing big data is playing an increasingly important role in people's lives, but due to the large amount of remote sensing data, data processing is difficult. Machine learning technology with the development of today's hardware technology, makes its own computational processing capacity has been greatly improved, and thus is widely used in various fields. Due to the increase in data and computer capabilities, deep learning is now widely used in remote sensing, while it has also been proved to be an extremely powerful tool in other fields. Combining the characteristics of large remote sensing data, this paper uses the Faster R-CNN method to achieve fast detection of ships and achieves a high detection rate.

Key words: remote sensing, big data, deep learning, faster R-CNN

CLC Number:

P237

ZHAO Zhenqiang, HE Shuiyuan, LIANG Yongzhi. Research on remote sensing image ship detection and identification based on Faster R-CNN[J]. Bulletin of Surveying and Mapping, 2021, 0(11): 59-64.

References

[1] 明冬萍, 骆剑承, 沈占锋,等.高分辨率遥感影像信息提取与目标识别技术研究[J]. 测绘科学, 2005, 30(3):18-20.
[2] 杨瑾文, 赖文奎. 深度学习算法在遥感影像分类识别中的应用现状及其发展趋势[J]. 测绘与空间地理信息, 2020, 43(4):114-117,120.
[3] 王巍, 袁涛, 周伟,等. 一种高分辨率遥感影像汽车识别检测方法[J]. 测绘通报, 2013(10):87-90.
[4] 赵亮, 王晓峰, 袁逸涛. 基于深度卷积神经网络的船舶识别方法研究[J]. 舰船科学技术, 2016,38(15):119-123.
[5] 王斌, 范冬林. 深度学习在遥感影像分类与识别中的研究进展综述[J]. 测绘通报, 2019(2):99-102,136.
[6] REN S, HE K, GIRSHICK R, et al. Faster R-cnn:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149.
[7] GIRSHICK R. Fast R-cnn[C]//Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV). Santiago:IEEE, 2015.
[8] DENG Z, SUN H, ZHOU S, et al. Learning deep ship detector in SAR images from scratch[J]. IEEE Transactions on Geoence and Remote Sensing, IEEE, 2019, 57(6):4021-4039.
[9] HE K, ZHANG X, REN S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9):1904-1916.
[10] GIRSHICK R. Fast R-CNN[J]. Computer Vision Foundation, 2015:1440-1448.
[11] ZHANG N, DONAHUE J, GIRSHICK R, et al. Part-based R-Cnns for fine-grained category detection[C]//European Conference on Computer Vision. Zurich:Springer, Cham, 2014.
[12] LONG J, SHELHAMER E, DARRELL T. Fully convolu-tional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 39(4):640-651.
[13] GALLEGO A J, PERTUSA A, GIL P, et al. Detection of bodies in maritime rescue operations using unmanned aerial vehicles with multispectral cameras[J]. Journal of Field Robotics, 2019,36(4):782-796.
[14] ZHAO J P, ZHANG Z H, YU W X, et al. A cascade coupled convolutional neural network guided visual attention method for ship detection from SAR Images[J]. IEEE Access, 2018, 6:50693-50708.
[15] 史文中, 朱长青, 王昱. 从遥感影像提取道路特征的方法综述与展望[J]. 测绘学报, 2001, 30(3):257-262.
[16] 杨存建, 周成虎. 基于知识的遥感图像分类方法的探讨[J]. 地理学与国土研究, 2001, 17(1):72-77.