-
摘要: 针对目前人为探察土地资源利用情况的任务繁重、办事效率低下等问题, 提出了一种基于深度卷积神经网络的建筑物变化检测方法, 利用高分辨率遥感图像实时检测每个区域新建与扩建的建筑物, 以方便对土地资源进行有效管理.本文受超列(Hypercolumn)和FlowNet中的细化(Refinement)结构启发, 将细化和其他改进应用到U-Net, 提出FlowS-Unet网络.首先对遥感图像裁剪、去噪、标注语义制作数据集, 将该数据集划分为训练集和测试集, 对训练集进行数据增强, 并根据训练集图像的均值和方差对所有图像进行归一化; 然后将训练集输入集成了多尺度交叉训练、多重损失计算、Adam优化的全卷积神经网络FlowS-Unet中进行训练; 最后对网络模型的预测结果进行膨胀、腐蚀以及孔洞填充等后处理得到最终的分割结果.本文以人工分割结果为参考标准进行对比测试, 用FlowS-Unet检测得到的F1分数高达0.943, 明显优于FCN和U-Net的预测结果.实验结果表明, FlowS-Unet能够实时准确地将新建与扩建的建筑物变化检测出来, 并且该模型也可扩展到其他类似的图像检测问题中.
-
关键词:
- FlowS-Unet /
- 建筑物变化检测 /
- 全卷积神经网络 /
- 多尺度交叉训练 /
- 多重损失
Abstract: Since manually detecting the situation of land resource utilization is arduous and inefficient, a smart building change detection method based on deep convolutional network is proposed, which can detect newly emerged or expanded buildings in each region of the high-resolution remote sensing images at real-time, thus can be used to manage the land resources efficiently. This article proposes a model named FlowS-Unet by applying refinement and other improvements to U-Net, which was inspired by hypercolumns and the refinement structure in FlowNet. First, the remote sensing images were cropped, denoised, and semantically annotated to form the dataset which is further divided into the training set and testing set, the training set is augmented to get enough training samples, and the mean value and variance of all training images are calculated and used to normalize the dataset; Second, the training set is fed into the fully convolutional network FlowS-Unet for training, which integrates multi-scale cross training, multiple losses and Adam algorithm for its optimization. Finally, the predicted result of FlowS-Unet is further post-processed with dilating, eroding and hole-filling to get the final segmentation result. By using manually segmented results as the ground truth, a comparison with several different algorithms shows that the F1 score of FlowS-Unet is as high as 0.943, which is apparently better than the predicted results of fully convolutional networks (FCN) and U-Net. Experimental results indicate that the newly emerged or expanded buildings can be accurately detected at real time with FlowS-Unet. This model can also be applied to other similar image detection problems.-
Key words:
- FlowS-Unet /
- change detection for buildings /
- fully convolutional networks (FCN) /
- multi-scale cross training /
- multiple losses
1) 本文责任编委 刘青山 -
图 1 一种端到端的建筑物变化检测概览图
Fig. 1 An overview of end-to-end architecture of building change detection
图 4 减少拼接区域色差效果图
Fig. 4 The result after the color difference of stitching blocks was sup-pressed
图 10 FlowS-Unet与现有方法的定性比较
Fig. 10 A quality comparison of FlowS-Unet and previous methods
表 1 FlowS-Unet与现有方法的性能比较
Table 1 The performance comparison of FlowS-Unet
序号 方法 F1分数(后处理前/后) 时间(s) 1 FlowS-Unet 0.933/0.943 62 2 FCN 0.858/0.873 50 3 U-Net 0.898/0.913 59 4 人工标注 1.000 18 000 
下载: 导出CSV
表 2 多尺度与单尺度训练及预测的F1分数比较
Table 2 The F1 score comparison between multi-scale cross and single-scale training and testing
训练尺度(像素) 预测尺度(像素) F1分数(后处理前/后) 224 224 0.903/0.923 256 256 0.909/0.928 288 288 0.913/0.931 320 320 0.911/0.932 224 0.933/0.939 256 0.938/0.943 多尺度 288 0.939/0.945 320 0.939/0.944 多尺度平均 0.942/0.946
下载: 导出CSV
表 3 FlowS-Unet与其他队伍的F1分数比较
Table 3 The F1 score comparison of FlowS-Unet and other teams
名次 初赛 复赛 决赛 第1名 0.890 0.914 0.861 第2名(FlowS-Unet) 0.903 0.877 0.840 第3名 0.867 0.898 0.800 第4名 0.706 0.870 0.842 第5名 0.879 0.936 0.823
下载: 导出CSV
-
[1] Beumier C, Idrissa M. Building change detection from uniform regions. In: Proceedings of the 17th Iberoamerican Congress Pattern Recognition, Image Analysis, Computer Vision, and Applications. Buenos Aires, Argentina: Springer, 2012. 648-655 [2] Turker M, Sumer E. Building-based damage detection due to earthquake using the watershed segmentation of the post-event aerial images. International Journal of Remote Sensing, 2008, 29(11): 3073-3089 doi: 10.1080/01431160701442096 [3] Huang X, Zhang L P, Zhu T T. Building change detection from multitemporal high-resolution remotely sensed images based on a morphological building index. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(1): 105-115 doi: 10.1109/JSTARS.2013.2252423 [4] 周则明, 孟勇, 黄思训, 胡宝鹏.基于能量最小化的星载SAR图像建筑物分割方法.自动化学报, 2016, 42(2): 279-289 doi: 10.16383/j.aas.2016.c150460Zhou Ze-Ming, Meng Yong, Huang Si-Xun, Hu Bao-Peng. Building segmentation of spaceborne SAR images based on energy minimization. Acta Automatica Sinica, 2016, 42(2): 279-289 doi: 10.16383/j.aas.2016.c150460 [5] 李炜明, 吴毅红, 胡占义.视角和光照显著变化时的变化检测方法研究.自动化学报, 2009, 35(5): 449-461 doi: 10.3724/SP.J.1004.2009.00449Li Wei-Ming, Wu Yi-Hong, Hu Zhan-Yi. Urban change detection under large view and illumination variations. Acta Automatica Sinica, 2009, 35(5): 449-461 doi: 10.3724/SP.J.1004.2009.00449 [6] 田昊, 杨剑, 汪彦明, 李国辉.基于先验形状约束水平集模型的建筑物提取方法.自动化学报, 2010, 36(11): 1502-1511 doi: 10.3724/SP.J.1004.2010.01502Tian Hao, Yang Jian, Wang Yan-Ming, Li Guo-Hui. Towards automatic building extraction: Variational level set model using prior shape knowledge. Acta Automatica Sinica, 2010, 36(11): 1502-1511 doi: 10.3724/SP.J.1004.2010.01502 [7] Liu B, Tang K, Liang J. A bottom-up/top-down hybrid algorithm for model-based building detection in single very high resolution SAR image. IEEE Geoscience and Remote Sensing Letters, 2017, 14(6): 926-930 doi: 10.1109/LGRS.2017.2687946 [8] Lukashevich P, Zalessky B, Belotserkovsky A. Building detection on aerial and space images. In: Proceedings of the 2017 International Conference on Information and Digital Technologies (IDT). Zilina, Slovakia: IEEE, 2017. 246-251 [9] 施文灶, 毛政元.基于图分割的高分辨率遥感影像建筑物变化检测研究.地球信息科学学报, 2016, 18(3): 423-432 http://d.old.wanfangdata.com.cn/Periodical/dqxxkx201603017Shi Wen-Zao, Mao Zheng-Yuan. The research on building change detection from high resolution remotely sensed imagery based on graph-cut segmentation. Journal of Geo-Information Science, 2016, 18(3): 423-432 http://d.old.wanfangdata.com.cn/Periodical/dqxxkx201603017 [10] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems. Nevada, USA: Curran Associates Inc., 2012. 1097-1105 [11] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3rd International Conference on Learning Representations. San Diego, California, USA, 2015. 1-14 %arXiv: 1409.1556, 2014. [12] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA: IEEE, 2015. 3431 -3440 [13] Yuan J Y. Automatic building extraction in aerial scenes using convolutional networks. arXiv: 1602.06564, 2016. [14] Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. In: Proceedings of the 18th Medical Image Computing and Computer-Assisted Intervention. Munich, Germany: Springer, 2015. 234-241 [15] Hariharan B, Arbeláez B, Girshick R, Malik J. Hypercolumns for object segmentation and fine-grained localization. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA: IEEE, 2015. 447-456 [16] Dosovitskiy A, Fischer P, Ilg E, Häusser P, Hazirbas C, Golkov V, et al. FlowNet: Learning optical flow with convolutional networks. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile: IEEE, 2015. 2758-2766 [17] 陈文康.基于深度学习的农村建筑物遥感影像检测.测绘, 2016, 39(5): 227-230 doi: 10.3969/j.issn.1674-5019.2016.05.010Chen Wen-Kang. Remote sensing image detection of rural buildings based on deep learning algorithm. Surveying and Mapping, 2016, 39(5): 227-230 doi: 10.3969/j.issn.1674-5019.2016.05.010 [18] Silberman N, Sontag D, Fergus R. Instance segmentation of indoor scenes using a coverage loss. In: Proceedings of the 13th European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014. 616-631 [19] Farabet C, Couprie C, Najman L, LeCun Y. Learning hierarchical features for scene labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1915- 1929 doi: 10.1109/TPAMI.2012.231 [20] Zhang A, Liu X M, Gros A, Tiecke T. Building detection from satellite images on a global scale. arXiv: 1707.08952, 2017. [21] Ghaffarian S, Ghaffarian S. Automatic building detection based on supervised classification using high resolution Google earth images. In: Proceedings of the 2014 ISPRS Technical Commission Ⅲ Symposium. Zurich, Switzerland: ISPRS, 2014. 101-106 [22] Shu Z, Hu X Y, Sun J. Center-point-guided proposal generation for detection of small and dense buildings in aerial imagery. IEEE Geoscience and Remote Sensing Letters, 2018, 15(7): 1100-1104 doi: 10.1109/LGRS.2018.2822760 [23] Yang H L, Lunga D, Yuan J Y. Toward country scale building detection with convolutional neural network using aerial images. In: Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Fort Worth, TX, USA: IEEE, 2017. 870-873 [24] Sun L, Tang Y Q, Zhang L P. Rural building detection in high-resolution imagery based on a two-stage CNN model. IEEE Geoscience and Remote Sensing Letters, 2017, 14(11): 1998-2002 doi: 10.1109/LGRS.2017.2745900 [25] Vakalopoulou M, Bus N, Karantzalos K, Paragios N. Integrating edge/boundary priors with classification scores for building detection in very high resolution data. In: Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Fort Worth, TX, USA: IEEE, 2017. 3309-3312 [26] Canny J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, PAMI-8(6): 679-698 doi: 10.1109/TPAMI.1986.4767851 [27] 卫亚星, 王莉雯.遥感图像增强方法分析.测绘与空间地理信息, 2006, 29(2): 4-7 doi: 10.3969/j.issn.1672-5867.2006.02.002Wei Ya-Xing, Wang Li-Wen. Analysis of enhancement methods about satellite images. Geomatics and Spatial Information Technology, 2006, 29(2): 4-7 doi: 10.3969/j.issn.1672-5867.2006.02.002 [28] Cai B L, Xu X M, Jia K, Qing C M, Tao D C. DehazeNet: An end-to-end system for single image haze removal. IEEE Transactions on Image Processing, 2016, 25(11): 5187-5198 doi: 10.1109/TIP.2016.2598681 [29] Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. Journal of Machine Learning Research, 2011, 15: 315-323%In: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. Fort Lauderdale, USA: JMLR W & CP, 2011. 315-323 http://d.old.wanfangdata.com.cn/Periodical/zdhxb201606009 [30] Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning. Lille, France: JMLR, 2015. 448-456 [31] Zeiler M D, Krishnan D, Taylor G W, Fergus R. Deconvolutional networks. In: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA: IEEE, 2010. 2528- 2535 [32] Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 2014, 15(1): 1929-1958 http://d.old.wanfangdata.com.cn/Periodical/kzyjc200606005 [33] Lin T Y, Dollár P, Girshick R, He K M, Hariharan B, Belongie S. Feature pyramid networks for object detection. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, USA: IEEE, 2017. 936-944 [34] Kingma D P, Ba J. Adam: A method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations. San Diego, CA, USA: 2015. 1-15 -
图(10) / 表(3)
计量
- 文章访问数: 2377
- HTML全文浏览量: 361
- PDF下载量: 312
- 被引次数: 0
