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基于DPM和R-CNN的高分二号遥感影像船只检测方法 预览

Ship detection in GaoFen-2 remote sensing imagery based on DPM and R-CNN
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摘要 提出了基于可变形部件模型(deformable part model,DPM)的高分二号(GaoFen-2,GF2)遥感影像船只检测方法,并与区域卷积网络(regional convolutional neural network,R-CNN)进行比较。先将遥感影像分段以获得船只的粗略感兴趣区域(regions of interest,ROI),然后在ROI内计算方向梯度直方图(histogram of oriented gradients,HOG)和卷积特征,再分别由DPM和R-CNN采用HOG和卷积特征。为测试R-CNN的最佳性能,将具有5个卷积层(ZF网)和具有13个卷积层(VGG网)的网络应用于船只检测。使用8张GF2遥感影像的3 523艘船只的实验结果表明,DPM和R-CNN都能以高召回率和正确率检测水中的船只,但对于聚集船只而言,DPM的效果优于R-CNN。基于HOG+DPM,ZF网和VGG网的方法平均精度分别为95.031%,93.282%和93.683%。 A method of ship detection for GaoFen-2 (GF2) imagery is proposed based on deformable part model (DPM) and the comparison with the regional convolutional neural network (R-CNN) is carried out. The GF2 images are firstly segmented to obtain the rough regions of interest (ROI) of ships. Then the histogram of oriented gradients (HOG) features and multi-layer convolutional features are computed within the ROIs. The HOG and convolutional features are then adopted by the DPM and the R-CNN respectively. To test the best performance of the R-CNN, a shallower network (ZF-net) with five convolutional layers and a deeper one (VGG-net) with 13 convolutional layers are applied to the ship detection. The experiments results using eight GF2 images with 3523 ships show that the DPM and the R-CNN can detect the ships surrounded by water with a high recall rate and precision. However, for the ships staying together and surrounded tightly by other ships, the DPM performs better than the R-CNN. The average precision of the methods based on HOG+DPM, ZF-net and VGG-net are 95.031%, 93.282% and 93.683% respectively.
作者 楼立志 张涛 张绍明 LOU Lizhi;ZHANG Tao;ZHANG Shaoming(College of Surveying, Mapping and Geo-Informatics, Tongji University, Shanghai 200092, China)
出处 《系统工程与电子技术》 EI CSCD 北大核心 2019年第3期509-514,共6页 Systems Engineering and Electronics
基金 国家自然科学基金(60831001) 国防基金(9140A31010109HK0101)资助课题.
关键词 船只检测 可变形部件模型 区域卷积网络 高分二号遥感影像 ship detection mixture of deformable part models regional convolutional neural network (R-CNN) GaoFen-2 (GF2) remote sensing imagery
作者简介 楼立志(1971-),男,副教授,博士,主要研究方向为图像处理、信号处理与大地测量数据处理。E-mail:llz@tongji.edu.cn;张涛(1994-),男,硕士研究生,主要研究方向为计算机视觉与遥感。E-mail:taozhang03@outlook.com;张绍明(1979-),通信作者,男,副教授,博士,主要研究方向为计算机视觉与遥感。E-mail:Sheva2003@gmail.com.
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