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Published： 07 February 2024 ，
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刘宣广，李蒙蒙，汪小钦，张振超.2024.基于面向对象孪生神经网络的高分辨率遥感影像建筑物变化检测.遥感学报，28（2）： 437-454
Liu X G，Li M M，Wang X Q and Zhang Z C. 2024. Use of object-based Siamese neural network to build change detection from very high resolution remote-sensing images. National Remote Sensing Bulletin， 28（2）：437-454
建筑物变化检测在城市环境监测、土地规划管理和违章违规建筑识别等应用中具有重要作用。针对传统孪生神经网络在影像变化检测中存在的检测边界与实际边界吻合度低的问题，本文结合面向对象图像分析技术，提出一种基于面向对象孪生神经网络（Obj-SiamNet）的高分辨率遥感影像变化检测方法，利用模糊集理论自动融合多尺度变化检测结果，并通过生成对抗网络实现训练样本迁移。该方法应用在高分二号和高分七号高分辨率卫星影像中，并与基于时空自注意力的变化检测模型（STANet）、视觉变化检测网络（ChangeNet）和孪生UNet神经网络模型（Siam-NestedUNet）进行比较。结果表明：（1）融合面向对象多尺度分割的检测结果较单一尺度分割的检测结果，召回率最高提升32%，F1指数最高提升25%，全局总体误差（GTC）最高降低7%；（2）在样本数量有限的情况下，通过生成对抗网络进行样本迁移，与未使用样本迁移前的检测结果相比，召回率最高提升16%，F1指数最高提升14%，GTC降低了9%；（3）Obj-SiamNet方法较其他变化检测方法，整体检测精度得到提升，F1指数最高提升23%，GTC最高降低9%。该方法有效提高了建筑物变化检测在几何和属性方面的精度，并能有效利用开放地理数据集，降低了模型训练样本制作成本，提升了检测效率和适用性。
Building change detection is essential to many applications
such as monitoring of urban areas
land use management
and illegal building detection. It has been seen as an effective means to detect building changes from remote-sensing images.
This paper proposes an object-based Siamese neural network
labeled as Obj-SiamNet
to detect building changes from high-resolution remote-sensing images. We combine the advantages of object-based image analysis methods and Siamese neural networks to improve the geometric accuracies of detected boundaries. Moreover
we implement the Obj-SiamNet at multiple segmentation levels and automatically construct a set of fuzzy measures to fuse the obtained results at multi-levels. Furthermore
we use generative adversarial methods to generate target-like training samples from publicly available datasets and construct a relatively sufficient training dataset for the Obj-SiamNet model. Finally
we apply the proposed method into three high-resolution remote-sensing datasets
i.e.
a GF-2 image-pair in Fuzhou City
and a GF2 image pair in Pucheng County
and a GF-2—GF-7 image pair in Quanzhou City. We also compare the proposed method with three other existing ones
namely
STANet
ChangeNet
and Siam-NestedUNet.
Experimental results show that the proposed method performs better than the other three in terms of detection accuracy. (1) Compared with the detection results from single-scale segmentation
the detection results from multi-scale increases the recall rate by up to 32%
the F1-Score increases by up to 25%
and the Global Total Classification error (GTC) decreases by up to 7%. (2) When the number of available samples is limited
the adopted Generative Adversarial Network (GAN) is able to generate effective target-like samples for diverting samples. Compared with the detection without using GAN-generated samples
the proposed detection increases the recall rate by up to 16%
increases the F1-Score by up to 14%
and decreases GTC by 9%. (3) Compared with other change-detection methods
the proposed method improves the detection accuracies significantly
i.e.
the F1-Score increases by up to 23%
and GTC decreases by up to 9%. Moreover
the boundaries of the detected changes by the proposed method have a high consistency with that of ground truth.
We conclude that the proposed Obj-SiamNet method has a high potential for building change detection from high-resolution remote-sensing images.
遥感变化检测孪生神经网络面向对象多尺度分析模糊集融合生成对抗网络高分辨率遥感影像
change detection of remote sensingSiamese Neural Networkobject-based multi-scale analysisfuzzy sets fusiongenerative adversarial networkvery high resolution remote sensing images
Benz U C, Hofmann P, Willhauck G, Lingenfelder I and Heynen M. 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3/4): 239-258 [DOI: 10.1016/j.isprsjprs.2003.10.002http://dx.doi.org/10.1016/j.isprsjprs.2003.10.002]
Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1): 2-16 [DOI: 10.1016/j.isprsjprs.2009.06.004http://dx.doi.org/10.1016/j.isprsjprs.2009.06.004]
Chen H and Shi Z W. 2020. A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sensing, 12(10): 1662-1684 [DOI: 10.3390/rs12101662http://dx.doi.org/10.3390/rs12101662]
Chen J, He C Y, Shi P J, Chen Y H and Ma N. 2001. Land use/cover change detection with change vector analysis (CVA): change magnitude threshold determination. Journal of Remote Sensing, 5(4): 259-266
陈晋, 何春阳, 史培军, 陈云浩, 马楠. 2001. 基于变化向量分析的土地利用/覆盖变化动态监测(Ⅰ)——变化阈值的确定方法. 遥感学报, 5(4): 259-266 [DOI: 10.11834/jrs.20010404http://dx.doi.org/10.11834/jrs.20010404]
Chopra S, Hadsell R and LeCun Y. 2005. Learning a similarity metric discriminatively, with application to face verification//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE: 539-546 [DOI: 10.1109/CVPR.2005.202http://dx.doi.org/10.1109/CVPR.2005.202]
Coulter L L, Stow D A, Tsai Y H, Ibanez N, Shih H C, Kerr A, Benza M, Weeks J R and Mensah F. 2016. Classification and assessment of land cover and land use change in southern Ghana using dense stacks of Landsat 7 ETM+imagery. Remote Sensing of Environment, 184: 396-409 [DOI: 10.1016/j.rse.2016.07.016http://dx.doi.org/10.1016/j.rse.2016.07.016]
Desclée B, Bogaert P and Defourny P. 2006. Forest change detection by statistical object-based method. Remote Sensing of Environment, 102(1/2): 1-11 [DOI: 10.1016/j.rse.2006.01.013http://dx.doi.org/10.1016/j.rse.2006.01.013]
Drǎguţ L, Tiede D and Levick S R. 2010. ESP: a tool to estimate scale parameter for multi-resolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6): 859-871 [DOI: 10.1080/13658810903174803http://dx.doi.org/10.1080/13658810903174803]
Feng W Q and Zhang Y J. 2015. Object-oriented change detection for remote sensing images based on multi-scale fusion. Acta Geodaetica et Cartographica Sinica, 44(10): 1142-1151
冯文卿, 张永军. 2015. 利用多尺度融合进行面向对象的遥感影像变化检测. 测绘学报, 44(10): 1142-1151 [DOI: 10.11947/j.AGCS2.0152.0140260http://dx.doi.org/10.11947/j.AGCS2.0152.0140260]
Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A and Bengio Y. 2014. Generative adversarial nets//Proceedings in Neural Information Processing Systems. Montreal: MIT Press: 2672-2680 [DOI: 10.48550/arXiv.1406.2661http://dx.doi.org/10.48550/arXiv.1406.2661]
Hadsell R, Chopra S and LeCun Y. 2006. Dimensionality reduction by learning an invariant mapping//Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, NY, USA: IEEE: 1735-1742 [DOI: 10.1109/CVPR.2006.100http://dx.doi.org/10.1109/CVPR.2006.100]
He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition. Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE: 770-778 [DOI: 10.1109/CVPR.2016.90http://dx.doi.org/10.1109/CVPR.2016.90]
Huang B, Zhao B and Song Y M. 2018a. Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sensing of Environment, 214: 73-86 [DOI: 10.1016/j.rse.2018.04.050http://dx.doi.org/10.1016/j.rse.2018.04.050]
Huang F M, Chen L X, Yin K L, Huang J S and Gui L. 2018b. Object-oriented change detection and damage assessment using high-resolution remote sensing images, Tangjiao Landslide, Three Gorges Reservoir, China. Environmental Earth Sciences, 77(183) [DOI: 10.1007/s12665-018-7334-5]
Huang P, Zheng Q and Liang C. 2020. Overview of image segmentation methods. Journal of Wuhan University (Natural Science Edition), 66(6): 519-531
黄鹏, 郑淇, 梁超. 2020. 图像分割方法综述. 武汉大学学报(理学版), 66(6): 519-531 [DOI: 10.14188/j.1671-8836.2019.0002http://dx.doi.org/10.14188/j.1671-8836.2019.0002]
Isola P, Zhu J Y, Zhou T H and Efros A A. 2017. Image-to-image translation with conditional adversarial networks//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE: 5967-5976 [DOI: 10.1109/CVPR.2017.632http://dx.doi.org/10.1109/CVPR.2017.632]
Karras T, Aila T, Laine S and Lehtinen J. 2018. Progressive growing of GANs for improved quality, stability, and variation. Proceedings of 2018 International Conference on Learning Representations. [DOI: 10.48550/arXiv.1710.10196http://dx.doi.org/10.48550/arXiv.1710.10196]
Khelifi L and Mignotte M. 2020. Deep learning for change detection in remote sensing images: comprehensive review and meta-analysis. IEEE Access, 8: 126385-126400 [DOI: 10.1109/ACCESS.2020.3008036http://dx.doi.org/10.1109/ACCESS.2020.3008036]
Kingma P D and Ba L J. 2015. Adam: a method for stochastic optimization\\Proceedings of 2015 International Conference on Learning Representations. San Diego. [DOI: 10.48550/arXiv.1412.6980http://dx.doi.org/10.48550/arXiv.1412.6980]
Li K Y, Li Z, and Fang S. 2021. Siamese NestedUNet Networks for Change Detection of High Resolution Satellite Image\\Proceedings of 2020 1st International Conference on Control, Robotics and Intelligent System. Association for Computing Machinery, New York, NY, USA: IEEE: 42-48 [DOI: 10.1145/3437802.3437810http://dx.doi.org/10.1145/3437802.3437810]
Li M M, Bijker W and Stein A. 2015. Use of Binary Partition Tree and energy minimization for object-based classification of urban land cover. ISPRS Journal of Photogrammetry and Remote Sensing, 102: 48-61 [DOI: 10.1016/j.isprsjprs.2014.12.023http://dx.doi.org/10.1016/j.isprsjprs.2014.12.023]
Li M M, Stein A, Bijker W and Zhan Q M. 2016. Region-based urban road extraction from VHR satellite images using Binary Partition Tree. International Journal of Applied Earth Observation and Geoinformation, 44: 217-225 [DOI: 10.1016/j.jag.2015.09.005http://dx.doi.org/10.1016/j.jag.2015.09.005]
Liu S C, Du P J and Chen S J. 2011. A novel change detection method of multi-resolution remotely sensed images based on the decision level fusion. Journal of Remote Sensing, 15(4): 846-862
柳思聪, 杜培军, 陈绍杰. 2011. 决策级融合的多分辨率遥感影像变化检测. 遥感学报, 15(4): 846-862 [DOI: 10.11834/jrs.20110098http://dx.doi.org/10.11834/jrs.20110098]
Liu T, Yang L X and Lunga D. 2021. Change detection using deep learning approach with object-based image analysis. Remote Sensing of Environment, 256: 112308-112323 [DOI: 10.1016/j.rse.2021.112308http://dx.doi.org/10.1016/j.rse.2021.112308]
Nair V and Hinton G E. 2010. Rectified linear units improve restricted Boltzmann machines//Proceedings of the 27th International Conference on International Conference on Machine Learning. Haifa: Omnipress: 807-814 [DOI: 10.1.1.165.6419/arXiv: 1111.6189v1http://dx.doi.org/10.1.1.165.6419/arXiv:1111.6189v1]
Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66 [DOI: 10.1109/TSMC.1979.4310076http://dx.doi.org/10.1109/TSMC.1979.4310076]
Paul S, Saxena K G, Nagendra H and Lele N. 2021. Tracing land use and land cover change in peri-urban Delhi, India, over 1973-2017 period. Environmental Monitoring and Assessment, 193(52) [DOI: 10.1007/s10661-020-08841-x]
Peng D F, Bruzzone L, Zhang Y J, Guan H Y, Ding H Y and Huang X. 2021. SemiCDNet: a semisupervised convolutional neural network for change detection in high resolution remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing, 59(7): 5891-5906 [DOI: 10.1109/TGRS.2020.3011913http://dx.doi.org/10.1109/TGRS.2020.3011913]
Radford A, Metz L and Chintala S. 2016. Unsupervised representation learning with deep convolutional generative adversarial networks. [DOI: 10.48550/arXiv.1511.06434http://dx.doi.org/10.48550/arXiv.1511.06434]
Shen Y, Wang H, Dai Y X. 2018. Deep siamese network-based classifier and its application. Computer Engineering and Applications, 54(10): 19-25
沈雁,王环,戴瑜兴.基于改进深度孪生网络的分类器及其应用[J].计算机工程与应用,2018,54(10):19-25 [DOI: CNKI:SUN:JSGG.0.2018-10-003http://dx.doi.org/CNKI:SUN:JSGG.0.2018-10-003]
Sui H G, Feng W Q, Li W Z, Sun K M and Xu C. 2018. Review of change detection methods for multi-temporal remote sensing imagery. Geomatics and Information Science of Wuhan University, 43(12): 1885-1898
眭海刚, 冯文卿, 李文卓, 孙开敏, 徐川. 2018. 多时相遥感影像变化检测方法综述. 武汉大学学报(信息科学版), 43(12): 1885-1898 [DOI: 10.13203/j.whugis20180251http://dx.doi.org/10.13203/j.whugis20180251]
Sun X X, Zhang J X, Yan Q and Gao J X. 2011. A summary on current techniques and prospects of remote sensing change detection. Remote Sensing Information, (1): 119-123
孙晓霞, 张继贤, 燕琴, 高井祥. 2011. 遥感影像变化检测方法综述及展望. 遥感信息, (1): 119-123 [DOI: 10.3969/j.issn.1000-3177.2011.01.023http://dx.doi.org/10.3969/j.issn.1000-3177.2011.01.023]
Tang Z X, Li M M and Wang X Q. 2020. Mapping tea plantations from VHR images using OBIA and convolutional neural networks. Remote Sensing, 12(18): 2935-2953 [DOI: 10.3390/rs12182935http://dx.doi.org/10.3390/rs12182935]
Tobias O J and Seara R. 2002. Image segmentation by histogram thresholding using fuzzy sets. IEEE Transactions on Image Processing, 11(12): 1457-1465 [DOI: 10.1109/TIP.2002.806231http://dx.doi.org/10.1109/TIP.2002.806231]
Tong G F, Li Y, Ding W L and Yue X Y. 2015. Review of remote sensing image change detection. Journal of Image and Graphics, 20(12): 1561-1571
佟国峰, 李勇, 丁伟利, 岳晓阳. 2015. 遥感影像变化检测算法综述. 中国图象图形学报, 20(12): 1561-1571 [DOI: 10.11834/Jig.20151201http://dx.doi.org/10.11834/Jig.20151201]
Varghese A, Gubbi J, Ramaswamy A and Balamuralidhar P. 2019. ChangeNet: a deep learning architecture for visual change detection//Proceedings of 2018 European Conference on Computer Vision Workshops. Munich, Germany: Springer: 129-145 [DOI: 10.1007/978-3-030-11012-3_10http://dx.doi.org/10.1007/978-3-030-11012-3_10]
Wang J, Xiao X M, Liu L, Wu X C, Qin Y W, Steiner J L and Dong J W. 2020. Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images. Remote Sensing of Environment, 247: 111951-111966 [DOI: 10.1016/j.rse.2020.111951http://dx.doi.org/10.1016/j.rse.2020.111951]
Wang Y L, Pu J, Zhao J H and Li J H. 2019. Detection of new ground buildings based on generative adversarial network. Journal of Computer Applications, 39(5): 1518-1522
王玉龙, 蒲军, 赵江华, 黎建辉. 2019. 基于生成对抗网络的地面新增建筑检测. 计算机应用, 39(5): 1518-1522 [DOI: 10.11772/j.issn.1001-9081.2018102083http://dx.doi.org/10.11772/j.issn.1001-9081.2018102083]
Wen D W, Huang X, Bovolo F, Li J Y, Ke X L, Zhang A L and Benediktsson J A. 2021. Change detection from very-high-spatial-resolution optical remote sensing images: methods, applications, and future directions. IEEE Geoscience and Remote Sensing Magazine, 9(4): 68-101 [DOI: 10.1109/MGRS.2021.3063465http://dx.doi.org/10.1109/MGRS.2021.3063465]
Wen D W, Huang X, Zhang A L and Ke X L. 2019. Monitoring 3D building change and urban redevelopment patterns in Inner City Areas of Chinese megacities using multi-view satellite imagery. Remote Sensing, 11(7): 763-785 [DOI: 10.3390/rs11070763http://dx.doi.org/10.3390/rs11070763]
Woo S, Park J, Lee J Y and Kweon I S. 2018. CBAM: convolutional block attention module//Proceedings of 2018 European Conference on Computer Vision. Munich, Germany: Springer: 3-19 [DOI: 10.1007/978-3-030-01234-2_1http://dx.doi.org/10.1007/978-3-030-01234-2_1]
Xu Y D, Yu L, Zhao F R, Cai X L, Zhao J Y, Lu H and Gong P. 2018. Tracking annual cropland changes from 1984 to 2016 using time-series Landsat images with a change-detection and post-classification approach: experiments from three sites in Africa. Remote Sensing of Environment, 218: 13-31 [DOI: 10.1016/j.rse.2018.09.008http://dx.doi.org/10.1016/j.rse.2018.09.008]
Yang K P, Xia G S, Liu Z C, Du B, Yang W, Pelillo M and Zhang L P. 2022. Asymmetric Siamese networks for semantic change detection in aerial images. IEEE Transactions on Geoscience and Remote Sensing, 60: 5609818-5609835 [DOI: 10.1109/TGRS.2021.3113912http://dx.doi.org/10.1109/TGRS.2021.3113912]
Zabalza J, Ren J C, Zheng J B, Zhao H M, Qing C M, Yang Z J, Du P J and Marshall S. 2016. Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing, 185: 1-10 [DOI: 10.1016/j.neucom.2015.11.044http://dx.doi.org/10.1016/j.neucom.2015.11.044]
Zhang C, Sargent I, Pan X, Li H P, Gardiner A, Hare J and Atkinson P M. 2018. An object-based convolutional neural network (OCNN) for urban land use classification. Remote Sensing of Environment, 216: 57-70 [DOI: 10.1016/j.rse.2018.06.034http://dx.doi.org/10.1016/j.rse.2018.06.034]
Zhang Z C, Vosselman G, Gerke M, Persello C, Tuia D and Yang M Y. 2019. Detecting building changes between airborne laser scanning and photogrammetric data. Remote Sensing, 11(20): 2417-2433 [DOI: 10.3390/rs11202417http://dx.doi.org/10.3390/rs11202417]
Zhou Q M. 2011. Review on change detection using multi-temporal remotely sensed imagery. Geomatics World, 9(2): 28-33
周启鸣. 2011. 多时相遥感影像变化检测综述. 地理信息世界, 9(2): 28-33 [DOI: 10.3969/j.issn.1672-1586.2011.02.007http://dx.doi.org/10.3969/j.issn.1672-1586.2011.02.007]
Zhu J Y, Park T, Isola P and Efros A A. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 2242-2251 [DOI: 10.1109/ICCV.2017.244http://dx.doi.org/10.1109/ICCV.2017.244]
Zhu Q Q, Guo X, Deng W H, Shi S N, Guan Q F, Zhong Y F, Zhang L P and Li D R. 2022. Land-Use/Land-Cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 184: 63-78 [DOI: 10.1016/j.isprsjprs.2021.12.005http://dx.doi.org/10.1016/j.isprsjprs.2021.12.005]
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