[1]方梦梁,黄 刚.一种光学遥感图像船舶目标检测技术[J].计算机技术与发展,2019,29(08):136-141.[doi:10. 3969 / j. issn. 1673-629X. 2019. 08. 026]
 FANG Meng-liang,HUANG Gang.A Ship Detection Technique for Optical Remote Sensing Images[J].,2019,29(08):136-141.[doi:10. 3969 / j. issn. 1673-629X. 2019. 08. 026]
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一种光学遥感图像船舶目标检测技术()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年08期
页码:
136-141
栏目:
应用开发研究
出版日期:
2019-08-10

文章信息/Info

Title:
A Ship Detection Technique for Optical Remote Sensing Images
文章编号:
1673-629X(2019)08-0142-05
作者:
方梦梁;?黄 刚
南京邮电大学 计算机学院,江苏 南京 210023
Author(s):
FANG Meng-liang;?HUANG Gang
School of Computer,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
关键词:
深度学习;?卷积神经网络;?光学遥感图像;?船舶目标检测
Keywords:
deep learning;?convolutional neural network;?optical remote sensing image;?ship target detection
分类号:
TP301
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 08. 026
摘要:
遥感图像目标检测与识别是遥感图像分析处理中的研究热点之一,具有十分重要的科研和应用价值。 基于光学遥感图像的海面船舶目标检测是其中一个重要的应用方向。 传统的遥感图像船舶目标检测方法精度不足、适用范围有限;因此,文中引入自然图片目标检测任务中表现优异的基于深度学习的 Faster R-CNN 算法。 由于光学遥感图像中海面船舶目标尺寸小以及自然图片与卫星遥感图像差异明显,直接应用原始的 Faster R-CNN 算法检测效果较差。 针对此问题,提出一种将图像上采样与特征金字塔网络结合的改进策略,以提高海面船舶检测性能,尤其是小尺寸目标的召回率和准确性。 通过在自制数据集上合理的对比实验验证了自然图片中的深度学习目标检测算法迁移至遥感图像处理的可行性和所提出方法的先进性。
Abstract:
Remote sensing image target detection and recognition is one of the research hotspots in remote sensing image analysis and processing,which has very important scientific research and application value. Ship detection based on optical remote sensing image is one of the applications in the field of remote sensing image analysis and processing. The traditional remote sensing image ship target detection method has insufficient accuracy and limited application range. Therefore,the deep learning-based Faster R-CNN is applied,which is excellent in natural picture target detection tasks. Due to the small size of ships on the sea surface in optical remote sensing images and the obvious difference between natural images and satellite remote sensing images,the direct application of the original Faster R-CNN leads to the poor detection. For this,the improved strategy of combining image upsampling with feature pyramid network is proposed, which significantly improves the ship detection performance,especially the recall rate and accuracy of small size targets. The feasibility of transferring the deep learning target detection algorithm in natural pictures to remote sensing image processing and the advancement of the proposed method are verified by a reasonable comparison experiment on the self-made data set.

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更新日期/Last Update: 2019-08-10