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王斌，陈占龙，吴亮，谢鹏，范冬林，付波霖.2020.兼顾连通性的U-Net网络高分辨率遥感影像道路提取.遥感学报，24（12）： 1488-1499
Wang B，Chen Z L，Wu L，Xie P，Fan D L and Fu B L. 2020. Road extraction of high-resolution satellite remote sensing images in U-Net network with consideration of connectivity. Journal of Remote Sensing（Chinese）， 24（12）：1488-1499
遥感影像道路提取结果中的断线一方面降低了提取精度，另一方面影响了道路形态完整性，使得提取结果不能直接应用于空间决策与分析。本文基于U-Net网络在高分辨率遥感影像道路提取时全局特征表达的优势，提出一种兼顾连通性的道路断线修复方法完善U-Net网络局部特征表达的劣势。首先，利用数据增强和扩充数据量后的样本数据作为U-Net网络的输入以此训练模型并进行最优模型的道路提取；然后，对提取结果中出现的道路断线以三次多项式曲线拟合的形式进行优化处理。实验表明，与相近网络比较，本文道路提取的精度和形态完整性有了明显的提高，查准率为86.25%，查全率为85.50%，F1-score达到了85.87%。其成果数据能直接地应用于地理决策分析，特别有利于灾后的路径规划，本文提出的方法对道路、电网、轨道、河流等线性地物分类结果中出现类似断线问题具有一定的参考意义。
Existing remote sensing image interpretation methods can obtain high-precision classification results but also have some defects. On the one hand, the problem of disconnection in the road extraction result of remote sensing image reduces the extraction precision. On the other hand, this problem affects the integrity of the road morphology. Thus, the extraction result cannot be directly applied to spatial decision making and analysis. Therefore, this study solves the problem of disconnection in the road extraction results of remote sensing images. A road extraction method for high-resolution remote sensing image of U-Net network with consideration of connectivity is also proposed.,On the basis of the advantages of global feature representation of U-Net network in road extraction of high-resolution remote sensing images, this study proposes a broken road repair method that considers connectivity to improve the local features of U-Net network. First, the sample data after data enhancement and data volume expansion are used as an input of the U-Net network to train the model and perform road extraction of the optimal model. Then, the road breakpoint detection, the road breakpoint clustering, and the cubic polynomial curve fitting are organically combined to optimize the result because of the broken road appearing in the extraction result.,The proposed method is practical and feasible according to the verification of experimental results. The method is also universal. The experiment shows that the accuracy and shape integrity of the road extraction in this method are improved significantly compared with similar networks. The precision is 86.25%, the recall rate is 85.50%, and F1-score reaches 85.87%.,The roads extracted by the proposed method have good connectivity and morphological integrity. The resulting image can be directly applied to geographic decision analysis, especially for post-disaster path planning. The proposed method has certain reference significance for the occurrence of similar disconnection problems in the classification results of linear objects, such as roads, power grids, orbits, and rivers.
遥感形态完整性U-Net网络高分辨率遥感影像连通性道路断线
remote sensingthe integrity of the road morphologyU-Net networkhigh-resolution remote sensing imagesconnectivitybroken road
Biswas S, Ghoshal D and Hazra R. 2016. A new algorithm of image segmentation using curve fitting based higher order polynomial smoothing. Optik, 127(20): 8916-8925 [DOI: 10.1016/j.ijleo.2016.06.110http://dx.doi.org/10.1016/j.ijleo.2016.06.110]
Cardim G P, Da Silva E A, Dias M A, Bravo I and Gardel A. 2018. Statistical evaluation and analysis of road extraction methodologies using a unique dataset from remote sensing. Remote Sensing, 10(4): 620 [DOI: 10.3390/rs10040620http://dx.doi.org/10.3390/rs10040620]
Chen Y S, Lin Z H, Zhao X, Wang G and Gu Y F. 2014. Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2094-2107 [DOI: 10.1109/JSTARS.2014.2329330http://dx.doi.org/10.1109/JSTARS.2014.2329330]
Chowdhury K, Chaudhuri D, Pal A K and Samal A. 2019. Seed selection algorithm through K-means on optimal number of clusters. Multimedia Tools and Applications, 78(13): 18617-18651 [DOI: 10.1007/s11042-018-7100-4http://dx.doi.org/10.1007/s11042-018-7100-4]
He H, Wang S C, Yang D F, Wang S Y and Liu X. 2019. An road extraction method for remote sensing image based on Encoder-Decoder network. Acta Geodaetica et Cartographica Sinica, 48(3): 330-338
贺浩, 王仕成, 杨东方, 王舒洋, 刘星. 2019. 基于Encoder-Decoder网络的遥感影像道路提取方法. 测绘学报, 48(3): 330-338 [DOI: 10.11947/j.AGCS.2019.20180005http://dx.doi.org/10.11947/j.AGCS.2019.20180005]
Hoberg T and Rottensteiner F. 2010. Classification of settlementareas in remote sensing imagery using conditional random fields. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(7): 53-58 [DOI: 10.15488/1114http://dx.doi.org/10.15488/1114]
Hu J S, Kang J R, Zhang Q, Liu P C and Zhu M D. 2018. An improving image boundary tracking algorithm based on eight neighborhood. Bulletin of Surveying and Mapping, (12): 21-25
胡晋山, 康建荣, 张琪, 刘鹏程, 朱铭达. 2018. 一种八邻域图像边界追踪改进算法. 测绘通报, (12): 21-25 [DOI: 10.13474/j.cnki.11-2246.2018.0451]
Kim J, Kim J, Jang G J and Lee M. 2017. Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection. Neural Networks, 87: 109-121 [DOI: 10.1016/j.neunet.2016.12.002http://dx.doi.org/10.1016/j.neunet.2016.12.002]
Kim J H, Lee H, Hong S J, Kim S, Park J, Hwang J Y and Choi J P. 2019. Objects segmentation from high-resolution aerial images using U-Net with pyramid pooling layers. IEEE Geoscience and Remote Sensing Letters, 16(1): 115-116 [DOI: 10.1109/LGRS.2018.2868880http://dx.doi.org/10.1109/LGRS.2018.2868880]
Lam L, Lee S W and Suen C Y. 1992. Thinning methodologies-a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(9): 869-885 [DOI: 10.1109/34.161346http://dx.doi.org/10.1109/34.161346]
Li H C, Chu H, Huo Y H. 2018. Multi-feature multiple kernels SVM-based urban road extraction. Bulletin of Surveying and Mapping, (2): 72-77
李洪川, 楚恒, 霍英海. 2018. 城市道路的多特征多核SVM提取方法. 测绘通报, (2): 72-77 [DOI: 10.13474/j.cnki.11-2246.2018.0047]
Li L W, Liu J P and Yin Z W. 2005. Road extraction from high resolution remote sensing image based on mathematic morphology. Remote Sensing Information, (5): 9-11
李利伟, 刘吉平, 尹作为. 2005. 基于数学形态学的高分辨率遥感影像道路提取. 遥感信息, (5): 9-11 [DOI: 10.3969/j.issn.1000-3177.2005.05.003]
Long Y, Gong Y P, Xiao Z F and Liu Q. 2017. Accurate object localization in remote sensing images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 55(5): 2486-2498 [DOI: 10.1109/TGRS.2016.2645610http://dx.doi.org/10.1109/TGRS.2016.2645610]
Martin D R, Fowlkes C C and Malik J. 2004. Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5): 530-549 [DOI: 10.1109/TPAMI.2004.1273918http://dx.doi.org/10.1109/TPAMI.2004.1273918]
Meng X L, Currit N, Wang L and Yang X J. 2012. Detect residential buildings from Lidar and aerial photographs through object-oriented land-use classification. Photogrammetric Engineering and Remote Sensing, 78(1): 35-44 [DOI: 10.14358/PERS.78.1.35http://dx.doi.org/10.14358/PERS.78.1.35]
Mittal K, Aggarwal G and Mahajan P. 2019. Performance study of K-nearest neighbor classifier and K-means clustering for predicting the diagnostic accuracy. International Journal of Information Technology, 11(3): 535-540 [DOI: 10.1007/s41870-018-0233-xhttp://dx.doi.org/10.1007/s41870-018-0233-x]
Mnih V. 2013. Machine Learning for Aerial Image Labeling. Toronto: University of Toronto
Oon A, Shafri H Z M, Lechner A M and Azhar B. 2019. Discriminating between large-scale oil palm plantations and smallholdings on tropical peatlands using vegetation indices and supervised classification of LANDSAT-8. International Journal of Remote Sensing, 40(19): 7312-7328 [DOI: 10.1080/01431161.2019.1579944http://dx.doi.org/10.1080/01431161.2019.1579944]
Pan J P and Li Z. 2009. Road extraction based on remote sensing classification and mathematic morphology. Computer Engineering and Applications, 46(34): 213-214, 227.
潘建平, 李治. 2009. 基于遥感分类与数学形态学的道路信息提取. 计算机工程与应用, 46(34): 213-214, 227 [DOI: 10.3778/j.issn.1002-8331.2010.34.064http://dx.doi.org/10.3778/j.issn.1002-8331.2010.34.064]
Panboonyuen T, Jitkajornwanich K, Lawawirojwong S, Srestasathiern P and Vateekul P. 2017. Road segmentation of remotely-sensed images using deep convolutional neural networks with landscape metrics and conditional random fields. Remote Sensing, 9(7): 680 [DOI: 10.3390/rs9070680http://dx.doi.org/10.3390/rs9070680]
Peng D F, Zhang Y J and Guan H Y. 2019. End-to-End change detection for high resolution satellite images using improved UNet++. Remote Sensing, 11(11): 1382 [DOI: 10.3390/rs11111382http://dx.doi.org/10.3390/rs11111382]
Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer: 234-241 [DOI: 10.1007/978-3-319-24574-4_28http://dx.doi.org/10.1007/978-3-319-24574-4_28]
Saeed K, Tabędzki M, Rybnik M and Adamski M. 2010. K3M: a universal algorithm for image skeletonization and a review of thinning techniques. International Journal of Applied Mathematics and Computer Science, 20(2): 317-335 [DOI: 10.2478/v10006-010-0024-4http://dx.doi.org/10.2478/v10006-010-0024-4]
Salari E and Siy P. 1984. The ridge-seeking method for obtaining the skeleton of digital images. IEEE Transactions on Systems, Man, and Cybernetics, SMC-14(3): 524-528 [DOI: 10.1109/TSMC.1984.6313252http://dx.doi.org/10.1109/TSMC.1984.6313252]
Salehnia N, Salehnia N, Ansari H, Kolsoumi S and Bannayan M. 2019. Climate data clustering effects on arid and semi-arid rainfed wheat yield: a comparison of artificial intelligence and K-means approaches. International Journal of Biometeorology, 63(7): 861-872 [DOI: 10.1007/s00484-019-01699-whttp://dx.doi.org/10.1007/s00484-019-01699-w]
Sobel L. 1978. Neighborhood coding of binary images for fast contour following and general binary array processing. Computer Graphics and Image Processing, 8(1): 127-135 [DOI: 10.1016/S0146-664X(78)80020-3http://dx.doi.org/10.1016/S0146-664X(78)80020-3]
Tang Z M, Luo L K, Peng H and Li S H. 2018. A joint residual network with paired ReLUs activation for image super-resolution. Neurocomputing, 273: 37-46 [DOI: 10.1016/j.neucom.2017.07.061http://dx.doi.org/10.1016/j.neucom.2017.07.061]
Wiedemann C. 2003. External evaluation of road networks[J]. ISPRS Archives, 34(3/W8): 93-98
Xu R and Wunsch D. 2005. Survey of clustering algorithms. IEEE Transactions on Neural Networks, 16(3): 645-678 [DOI: 10.1109/TNN.2005.845141http://dx.doi.org/10.1109/TNN.2005.845141]
Xu Y Y, Xie Z, Feng Y X and Chen Z L. 2018. Road extraction from high-resolution remote sensing imagery using deep learning. Remote Sensing, 10(9): 1461 [DOI: 10.3390/rs10091461http://dx.doi.org/10.3390/rs10091461]
Yuan M, Liu Z and Wang F. 2019. Using the wide-range attention U-Net for road segmentation. Remote Sensing Letters, 10(5): 506-515 [DOI: 10.1080/2150704X.2019.1574990http://dx.doi.org/10.1080/2150704X.2019.1574990]
Zhang M M, Li W and Du Q. 2018. Diverse region-based CNN for hyperspectral image classification. IEEE Transactions on Image Processing, 27(6): 2623-2634 [DOI: 10.1109/TIP.2018.2809606http://dx.doi.org/10.1109/TIP.2018.2809606]
Zhang Y D, Pan C C, Sun J D and Tang C S. 2018. Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU. Journal of Computational Science, 28: 1-10 [DOI: 10.1016/j.jocs.2018.07.003http://dx.doi.org/10.1016/j.jocs.2018.07.003]
Zhang Y H, He J, Kan X, Xia G H, Zhu L L and Ge T T. 2018. Summary of road extraction methods for remote sensing images. Computer Engineering and Applications, 54(13): 1-10, 51
张永宏, 何静, 阚希, 夏广浩, 朱灵龙, 葛涛涛. 2018. 遥感图像道路提取方法综述. 计算机工程与应用, 54(13): 1-10, 51 [DOI: 10.3778/j.issn.1002-8331.1804-0271http://dx.doi.org/10.3778/j.issn.1002-8331.1804-0271]
Zhang Z X, Liu Q J and Wang Y H. 2018. Road extraction by deep residual U-Net. IEEE Geoscience and Remote Sensing Letters, 15(5): 749-753 [DOI: 10.1109/LGRS.2018.2802944http://dx.doi.org/10.1109/LGRS.2018.2802944]
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