卫星影像匹配的深度卷积神经网络方法
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摘要
本文侧重于智能化摄影测量深度学习的第一个方面:深度卷积方法。传统的影像同名点对提取算法通常利用人工设计的特征描述符及其最短距离作为匹配准则进行匹配,其匹配结果易陷入局部极值,造成部分正确匹配点对的遗漏。针对这一问题,本文引入深度学习方法,设计了一种基于空间尺度卷积层的两通道深度卷积神经网络,采用其进行影像间的匹配模式学习,实现了基于深度卷积神经网络的卫星影像匹配。试验表明,在处理异源、多时相、多分辨率的卫星影像情况下,本文方法比传统匹配方法能提取到更为丰富的影像同名点对,且最终匹配提纯结果正确率优于90%。
This article focuses on the first aspect of the album of deep learning:the deep convolution method.The traditional matching point extraction algorithm usually uses the manually-designed feature descriptor and the shortest distance between them to match as the matching criterion.The matching result is easy to fall into the local extreme value,which causes the missing of the partial matching point.Aiming at this problem,we introduce a two-channel deep convolution neural network based on spatial scale convolution,and performs matching pattern learning between images to realize the satellite image matching based on deep convolution neural network.The experimental results show that the method can extract the richer matching point in the case of heterogeneous,multi-temporal and multi-resolution satellite images,compared with the traditional matching method.And the accuracy of the final matching results can be maintained at above 90%.
This article focuses on the first aspect of the album of deep learning:the deep convolution method.The traditional matching point extraction algorithm usually uses the manually-designed feature descriptor and the shortest distance between them to match as the matching criterion.The matching result is easy to fall into the local extreme value,which causes the missing of the partial matching point.Aiming at this problem,we introduce a two-channel deep convolution neural network based on spatial scale convolution,and performs matching pattern learning between images to realize the satellite image matching based on deep convolution neural network.The experimental results show that the method can extract the richer matching point in the case of heterogeneous,multi-temporal and multi-resolution satellite images,compared with the traditional matching method.And the accuracy of the final matching results can be maintained at above 90%.
引文
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[2]李德仁,张良培,夏桂松.遥感大数据自动分析与数据挖掘[J].测绘学报,2014,43(12):1211-1216.DOI:10.13485/j.cnki.11-2089.2014.0187.LI Deren,ZHANG Liangpei,XIA Guisong.Automatic Analysis and Mining of Remote Sensing Big Data[J].Acta Geodaetica et Cartographica Sinica,2014,43(12):1211-1216.DOI:10.13485/j.cnki.11-2089.2014.0187.
[3]严国萍,何俊峰.高斯-拉普拉斯边缘检测算子的扩展研究[J].华中科技大学学报(自然科学版),2006,34(10):21-23.YAN Guoping,HE Junfeng.Extended Laplacian of Gaussian Operator for Edge Detection[J].Journal of Huazhong University of Science and Technology(Nature Science Edition),2006,34(10):21-23.
[4]赵万金,龚声蓉,刘纯平,等.一种自适应的Harris角点检测算法[J].计算机工程,2008,34(10):212-214,217.ZHAO Wanjin,GONG Shengrong,LIU Chunping,et al.Adaptive Harris Corner Detection Algorithm[J].Computer Engineering,2008,34(10):212-214,217.
[5]LOWE D G.Distinctive Image Features from Scale-invariant Keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[6]KE Yan,SUKTHANKAR R.PCA-SIFT:A More Distinctive Representation for Local Image Descriptors[C]∥Proceedings of the IEEE Computer Society Computer Vision and Pattern Recognition.Washington,DC:IEEE,2004:506-513.
[7]BAY H,TUYTELAARS T,VAN GOOL L.SURF:Speeded up Robust Features[M]∥LEONARDIS A,BISCHOF H,PINZ A.Computer Vision-ECCV 2006.Berlin:Springer,2006:404-417.
[8]CALONDER M,LEPETIT V,STRECHA C,et al.BRIEF:Binary Robust Independent Elementary Features[M]∥DANIILIDIS K,MARAGOS P,PARAGIOS N.Computer Vision-ECCV 2010.Berlin:Springer,2010:778-792.
[9]CALONDER M,LEPETIT V,OZUYSAL M,et al.BRIEF:Computing a Local Binary Descriptor Very Fast[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(7):1281-1298.
[10]许允喜,陈方.局部图像描述符最新研究进展[J].中国图象图形学报,2015,20(9):1133-1150.XU Yunxi,CHEN Fang.Recent Advances in Local Image Descriptor[J].Journal of Image and Graphics,2015,20(9):1133-1150.
[11]董杨,范大昭,纪松,等.主成分分析的匹配点对提纯方法[J].测绘学报,2017,46(2):228-236.DOI:10.11947/j.AGCS.2017.20160250.DONG Yang,FAN Dazhao,JI Song,et al.The Purification Method of Matching Points Based on Principal Component Analysis[J].Acta Geodaetica et Cartographica Sinica,2017,46(2):228-236.DOI:10.11947/j.AGCS.2017.20160250.
[12]BONTAR J,LECUN Y.Computing the Stereo Matching Cost with a Convolutional Neural Network[C]∥IEEE Conference on Computer Vision and Pattern Recognition.Boston,MA:IEEE,2015:1592-1599.
[13]ZAGORUYKO S,KOMODAKIS N.Learning to Compare Image Patches via Convolutional Neural Networks[C]∥IEEE Conference on Computer Vision and Pattern Recognition.Boston,MA:IEEE,2015:4353-4361.
[14]HAN Xufeng,LEUNG T,JIA Yangqing,et al.MatchNet:Unifying Feature and Metric Learning for Patch-based Matching[C]∥IEEE Conference on Computer Vision and Pattern Recognition.Boston,MA:IEEE,2015:3279-3286.
[15]PAL S K,MITRA S.Multilayer Perceptron,Fuzzy Sets,and Classification[J].IEEE Transactions on Neural Networks,1992,3(5):683-697.
[16]TRAPPEY A J C,HSU F C,TRAPPEY C V,et al.Development of a Patent Document Classification and Search Platform Using a Back-propagation Network[J].Expert Systems with Applications,2006,31(4):755-765.
[17]SZEGEDY C,LIU Wei,JIA Yangqing,et al.Going Deeper with Convolutions[C]∥IEEE Conference on Computer Vision and Pattern Recognition.Boston,MA:IEEE,2015:1-9.
[18]BONTAR J,LECUN Y.Stereo Matching by Training A Convolutional Neural Network to Compare Image Patches[J].The Journal of Machine Learning Research,2016,17(1):2287-2318.
[19]COLLOBERT R,KAVUKCUOGLU K,FARABET C.Torch7:A Matlab-like Environment for Machine Learning[C]∥Neural Information Processing Systems Workshop.[s.l]:BigLearn,2011.
[20]CHEN Tianqi,LI Mu,LI Yutian,et al.MXNet:A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems[R].Nanjin:Neural Information Processing Systems,Workshop on Machine Learning Systems,2015.
[21]BROWN M,HUA Gang,WINDER S.Discriminative Learning of Local Image Descriptors[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(1):43-57.
[22]杜玉龙,李建增,张岩,等.基于深度交叉CNN和免交互GrabCut的显著性检测[J].计算机工程与应用,2017,53(3):32-40.DU Yulong,LI Jianzeng,ZHANG Yan,et al.Saliency Detection Based on Deep Cross CNN and Non-interaction GrabCut[J].Computer Engineering and Applications,2017,53(3):32-40.
[23]ALCANTARILLA P F,BARTOLI A,DAVISON A J.KAZE Features[M]∥FITZGIBBON A,LAZEBNIK S,PERONA P,et al.Computer Vision-ECCV 2012.Berlin,Heidelberg:Springer,2012.
[24]ALCANTARILLA P F,NUEVO J,BARTOLI A.Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces[C]∥Proceedings British Machine Vision Conference 2013.Bristol,UK:British Machine Vision Conference,2013.
[25]RUBLEE E,RABAUD V,KONOLIGE K,et al.ORB:An efficient Alternative to SIFT or SURF[C]∥IEEE International Conference on Computer Vision.Barcelona,Spain:IEEE,2012.
[26]LEUTENEGGER S,CHLI M,SIEGWART R Y.BRISK:Binary Robust Invariant Scalable Keypoints[C]∥IEEE International Conference on Computer Vision.Barcelona,Spain:IEEE,2012.
[27]ROSTEN E,DRUMMOND T.Machine Learning for HighSpeed Corner Detection[M]∥LEONARDIS A,BISCHOF H,PINZ A.Computer Vision-ECCV 2006.Berlin,Heidelberg:Springer,2006.
[28]MAIR E,HAGER G D,BURSCHKA D,et al.Adaptive and Generic Corner Detection Based on the Accelerated Segment Test[M]∥DANIILIDIS K,MARAGOS P,PARAGIOS N.Computer Vision-ECCV 2010.Berlin:Springer,2010.
[29]HARRIS C,STEPHENS M.A Combined Corner and Edge Detector[C]∥Proceedings of the 4th Alvey Vision Conference.Manchester:Springer,1988:147-151.
[30]SHI Jianbo,TOMASI.Good Features to Track[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Seattle,WA:IEEE,1994.