卫星影像匹配的深度卷积神经网络方法

doi:10.11947/j.AGCS.2018.20170627

测绘学报 ›› 2018, Vol. 47 ›› Issue (6): 844-853.doi: 10.11947/j.AGCS.2018.20170627

• 数字摄影测量与深度学习方法 • 上一篇下一篇

卫星影像匹配的深度卷积神经网络方法

范大昭, 董杨, 张永生

信息工程大学地理空间信息学院, 河南郑州 450001

收稿日期:2017-12-13 修回日期:2018-03-23 出版日期:2018-06-20 发布日期:2018-06-21
作者简介:范大昭(1973-),男,博士,教授,研究方向为数字摄影测量的理论与应用。E-mail:fdzcehui@163.com
基金资助:
国家自然科学基金（41401534）；地理信息工程国家重点实验室开放基金（SKLGIE2013-M-3-1）

Satellite Image Matching Method Based on Deep Convolution Neural Network

FAN Dazhao, DONG Yang, ZHANG Yongsheng

Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China

Received:2017-12-13 Revised:2018-03-23 Online:2018-06-20 Published:2018-06-21
Supported by:
The National Natural Science Foundation of China (No.41401534);The Open Fund of State Key Laboratory of Geographic Information Engineering (No.SKLGIE2013-M-3-1)

摘要/Abstract

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%。

关键词: 影像匹配, 深度学习, 面向对象, 卷积神经网络, 卫星影像

Abstract: 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%.

Key words: image matching, deep learning, object-oriented, convolution neural network, satellite image

中图分类号:

P236

范大昭, 董杨, 张永生. 卫星影像匹配的深度卷积神经网络方法[J]. 测绘学报, 2018, 47(6): 844-853.

FAN Dazhao, DONG Yang, ZHANG Yongsheng. Satellite Image Matching Method Based on Deep Convolution Neural Network[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6): 844-853.

参考文献

[1] 李德仁. 展望大数据时代的地球空间信息学[J]. 测绘学报, 2016, 45(4):379-384. DOI:10.11947/j.AGCS.2016.20160057. LI Deren. Towards Geo-spatial Information Science in Big Data Era[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(4):379-384. DOI:10.11947/j.AGCS.2016.20160057.
[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] ŽBONTAR 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] ŽBONTAR 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 High-Speed 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.

卫星影像匹配的深度卷积神经网络方法

Satellite Image Matching Method Based on Deep Convolution Neural Network

PDF

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]	张良培, 何江, 杨倩倩, 肖屹, 袁强强. 数据驱动的多源遥感信息融合研究进展[J]. 测绘学报, 2022, 51(7): 1317-1337.
[2]	程涛, 张洋, James Haworth. 基于网络和图的时空智能——概念、方法和应用[J]. 测绘学报, 2022, 51(7): 1629-1639.
[3]	龚健雅, 宦麟茜, 郑先伟. 影像解译中的深度学习可解释性分析方法[J]. 测绘学报, 2022, 51(6): 873-884.
[4]	张勤, 赵超英, 陈雪蓉. 多源遥感地质灾害早期识别技术进展与发展趋势[J]. 测绘学报, 2022, 51(6): 885-896.
[5]	龚健雅, 张觅, 胡翔云, 张展, 李彦胜, 姜良存. 智能遥感深度学习框架与模型设计[J]. 测绘学报, 2022, 51(4): 475-487.
[6]	毛文婧, 王卫林, 焦利民, 刘安宝. 基于深度学习的中国连续空间覆盖PM_2.5浓度预报[J]. 测绘学报, 2022, 51(3): 361-372.
[7]	金飞, 官恺, 刘智, 韩佳容, 芮杰, 李庆高. 无监督密集匹配特征提取网络性能分析[J]. 测绘学报, 2022, 51(3): 426-436.
[8]	何直蒙, 丁海勇, 安炳琪. 高分辨率遥感影像建筑物提取的空洞卷积E-Unet算法[J]. 测绘学报, 2022, 51(3): 457-467.
[9]	洪亮, 冯亚飞, 彭双云, 楚森森. 面向对象的多尺度加权联合稀疏表示的高空间分辨率遥感影像分类[J]. 测绘学报, 2022, 51(2): 224-237.
[10]	张玉鑫, 颜青松, 邓非. 高分辨率遥感影像建筑物提取多路径RSU网络法[J]. 测绘学报, 2022, 51(1): 135-144.
[11]	张永生, 张振超, 童晓冲, 纪松, 于英, 赖广陵. 地理空间智能研究进展和面临的若干挑战[J]. 测绘学报, 2021, 50(9): 1137-1146.
[12]	艾廷华. 深度学习赋能地图制图的若干思考[J]. 测绘学报, 2021, 50(9): 1170-1182.
[13]	龚健雅, 许越, 胡翔云, 姜良存, 张觅. 遥感影像智能解译样本库现状与研究[J]. 测绘学报, 2021, 50(8): 1013-1022.
[14]	杨必胜, 韩旭, 董震. 适用于城市场景大规模点云语义标识的深度学习网络[J]. 测绘学报, 2021, 50(8): 1059-1067.
[15]	张永军, 万一, 史文中, 张祖勋, 李彦胜, 季顺平, 郭浩宇, 李礼. 多源卫星影像的摄影测量遥感智能处理技术框架与初步实践[J]. 测绘学报, 2021, 50(8): 1068-1083.