基于分类后验概率空间的孪生Nested-UNet（SNU-PS）变化检测网络

朱传海; 陈学泓; 陈晋; 袁宇恒; 唐凯

doi:10.11834/jrs.20233070

变化检测与深度学习 | 浏览量 : 0 下载量: 21 CSCD: 0

R-PDF
PDF
导出
分享
收藏
专辑

基于分类后验概率空间的孪生Nested-UNet（SNU-PS）变化检测网络
A siamese Nested-UNet for change detection in posterior probability space （SNU-PS）
2023年27卷第9期页码：2006-2023
DOI： 10.11834/jrs.20233070

扫描看全文

朱传海, 陈学泓, 陈晋, 等. 基于分类后验概率空间的孪生Nested-UNet（SNU-PS）变化检测网络[J]. 遥感学报, 2023,27(9):2006-2023.

ZHU Chuanhai, CHEN Xuehong, CHEN Jin, et al. A siamese Nested-UNet for change detection in posterior probability space （SNU-PS）[J]. National Remote Sensing Bulletin, 2023,27(9):2006-2023.
朱传海, 陈学泓, 陈晋, 等. 基于分类后验概率空间的孪生Nested-UNet（SNU-PS）变化检测网络[J]. 遥感学报, 2023,27(9):2006-2023. DOI： 10.11834/jrs.20233070.

ZHU Chuanhai, CHEN Xuehong, CHEN Jin, et al. A siamese Nested-UNet for change detection in posterior probability space （SNU-PS）[J]. National Remote Sensing Bulletin, 2023,27(9):2006-2023. DOI： 10.11834/jrs.20233070.

摘要

近年来，深度学习在多时相遥感影像变化检测任务中表现出巨大的潜力。充分的训练样本是深度学习技术能够有效挖掘遥感影像变化特征的重要前提，然而当前有限的公开标注数据集还不能满足实际应用中各种变化类型检测的需求。由于地表覆盖变化通常只占据少部分区域，能够获取的变化样本常常数量很少，且与不变化样本相比存在严重的不平衡问题。因此，如何在小样本与样本不平衡的情况下有效训练变化检测网络是急需突破的难题。相比变化检测样本，单时相地表覆盖分类样本的获取难度要低得多；在分类样本的支持下，充分训练的地表覆盖分类网络可为变化检测提供重要的先验特征。基于此，本文提出了一种基于分类后验概率空间的孪生Nested-UNet变化检测网络SNU-PS（Siamese Nested-UNet for change detection in Posterior Probability Space），通过结合两期地表覆盖分类后验概率信息，降低对变化检测样本的依赖。该方法首先利用地表覆盖分类样本训练高分辨率网络HRNet（High-Resolution Network），得到双时相影像的地物分类后验概率；然后将后验概率图像输入到孪生Nested-UNet变化检测网络SNU（Siamese Nested-UNet for change detection）中以获取变化检测结果。在SpaceNet7 和HRSCD数据集上测试的结果表明，SNU-PS能够充分利用地表覆盖的语义信息，在不同变化检测训练样本数量水平下，保持稳定的变化检测精度；相比分类后比较PCC（Post Classification Comparison）、基于后验概率空间的变化向量分析CVAPS（Change-vector analysis in posterior probability space）、以及各种类型变化检测网络（SNU、FC-EF、BIT、PCFN），具备更高与更稳定的变化检测精度，特别在样本数量不足时，优势更为明显。因此，本文提出的SNU-PS在小样本情形下的变化检测任务上具备更好的应用前景。

Abstract

Deep learning has shown great potential in the change detection of multi-temporal remote sensing images in recent years. However, the annotated datasets, which are critically required in training change detection networks, is often limited in various change detection tasks in practical applications. As land cover change usually occupies only a small portion of an image, the number of changed samples is often very small, leading to a serious imbalance between changed and unchanged samples. Therefore, it is an urgent challenge to effectively training change detection networks with small and imbalanced change detection samples. Compared to the collection of change detection samples, it is much easier to obtain land cover classification samples at a single time. Based on the adequate land cover classification samples, a well-trained land cover segmentation network can provide important prior features for change detection.,Therefore, this paper proposes a method named as siamese Nested-UNet for change detection in posterior probability space (SNU-PS), which aims to reduce the dependence on change detection samples by utilizing the posterior probability information of segmentation network. The method first trains a High-Resolution Network (HRNet) based on land cover classification samples to obtain the posterior probability of the bi-temporal image. Then, the posterior probability images are input into a siamese Nested-UNet for change detection(SNU) to obtain the change detection results. In order to simplify the network complexity and reduce the training difficulty, the training of semantic segmentation network and change detection network are carried out step by step without interactions in their training stages. As the posterior probability image already contains semantic information of land cover, the requirement of the change detection samples is reduced because the change detection network does not need to extract the features in the multi-spectral images.,The change detection experiments based on the SpaceNet7 and HRSCD datasets show that SNU-PS can well utilize the semantic information provided by the land cover segmentation network and maintain stable change detection accuracy when it was trained with different change detection sample sizes. Compared with Post Classification Comparison (PCC), CVAPS (Change-vector analysis in posterior probability space), and different change detection networks (FC-EF, BIT, PCFN, and SNU), SNU-PS achieved higher accuracy and better stability, especially when the sample size is small. Unfortunately, all of the compared methods failed to identify the change type due to the extreme imbalanced samples of different change types.,SNU-PS method makes full use of the low-cost classification samples to train the semantic segmentation network, which helps to reduce the reliance on the change detection samples because the change detection network in SNU-PS does not undertake the feature exploration of multi-spectral images. Moreover, the semantic segmentation network and the change detection network are integrated with independent training process in SNU-PS, thus the integration of two networks does not increase the training difficulty and semantic segmentation network can be flexibly replaced with better network if available. In conclusion, the proposed SNU-PS maintains good performance under small sample size, thus has a good applicability in various change detection tasks.

关键词

地表覆盖变化检测深度学习小样本样本不平衡语义分割网络孪生网络后验概率

Keywords

land coverchange detectiondeep learningsmall samplesample imbalancesemantic segmentation networkSiamese networkposterior probability

references

Amankwah S O Y, Wang G J, Gnyawali K, Hagan D F T, Sarfo I, Zhen D, Nooni I K, Ullah W and Duan Z. 2022. Landslide detection from bitemporal satellite imagery using attention-based deep neural networks. Landslides, 19(10): 2459-2471 [DOI: 10.1007/s10346-022-01915-6http://dx.doi.org/10.1007/s10346-022-01915-6]

Asokan A and Anitha J. 2019. Change detection techniques for remote sensing applications: a survey. Earth Science Informatics, 12(2): 143-160 [DOI: 10.1007/s12145-019-00380-5http://dx.doi.org/10.1007/s12145-019-00380-5]

Baker C, Lawrence R L, Montagne C and Patten D. 2007. Change detection of wetland ecosystems using Landsat imagery and change vector analysis. Wetlands, 27(3): 610-619 [DOI: 10.1672/0277-5212(2007)27http://dx.doi.org/10.1672/0277-5212(2007)27[610:CDOWEU]2.0.CO;2]

Bandara W G C and Patel V M. 2022. A transformer-based Siamese network for change detection//IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium. Kuala Lumpur: IEEE: 207-210 [DOI: 10.1109/IGARSS46834.2022.9883686http://dx.doi.org/10.1109/IGARSS46834.2022.9883686]

Bouziani M, Goïta K and He D C. 2010. Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1): 143-153 [DOI: 10.1016/j.isprsjprs.2009.10.002http://dx.doi.org/10.1016/j.isprsjprs.2009.10.002]

Celik T. 2009. Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geoscience and Remote Sensing Letters, 6(4): 772-776 [DOI: 10.1109/LGRS.2009.2025059http://dx.doi.org/10.1109/LGRS.2009.2025059]

Chan T H, Jia K, Gao S H, Lu J W, Zeng Z N and Ma Y. 2015. PCANet: a simple deep learning baseline for image classification?. IEEE Transactions on Image Processing, 24(12): 5017-5032 [DOI: 10.1109/TIP.2015.2475625http://dx.doi.org/10.1109/TIP.2015.2475625]

Chen H, Qi Z P and Shi Z W. 2022a. Remote sensing image change detection with transformers. IEEE Transactions on Geoscience and Remote Sensing, 60: 5607514 [DOI: 10.1109/TGRS.2021.3095166http://dx.doi.org/10.1109/TGRS.2021.3095166]

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 [DOI: 10.3390/rs12101662http://dx.doi.org/10.3390/rs12101662]

Chen J, Chen X H, Cui X H and Chen J. 2011. Change vector analysis in posterior probability space: a new method for land cover change detection. IEEE Geoscience and Remote Sensing Letters, 8(2): 317-321 [DOI: 10.1109/LGRS.2010.2068537http://dx.doi.org/10.1109/LGRS.2010.2068537]

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]

Chen J, Yuan Z Y, Peng J, Chen L, Huang H Z, Zhu J W, Liu Y and Li H F. 2021a. DASNet: dual attentive fully convolutional Siamese networks for change detection in high-resolution satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 1194-1206 [DOI: 10.1109/JSTARS.2020.3037893http://dx.doi.org/10.1109/JSTARS.2020.3037893]

Chen J N, Lu Y Y, Yu Q H, Luo X D, Adeli E, Wang Y, Lu L, Yuille A L and Zhou Y Y. 2021b. TransuNET: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv: 2102.04306 [DOI: 10.48550/arXiv.2102.04306http://dx.doi.org/10.48550/arXiv.2102.04306]

Chen L C, Zhu Y K, Papandreou G, Schroff F and Adam H. 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation//Proceedings of the 15th European Conference on Computer Vision. Munich: Springer: 833-851 [DOI: 10.1007/978-3-030-01234-2_49http://dx.doi.org/10.1007/978-3-030-01234-2_49]

Chen Y H, Li X B, Chen J and Shi P J. 2002. The change of NDVI time series based on change vector analysis in China, 1983-1992. Journal of Remote Sensing, 6(1): 12-18

陈云浩, 李晓兵, 陈晋, 史培军. 2002. 1983-1992年中国陆地植被NDVI演变特征的变化矢量分析. 遥感学报, 6(1): 12-18 [DOI: 10.11834/jrs.20020103http://dx.doi.org/10.11834/jrs.20020103]

Chen Z L, Zhou Y, Wang B, Xu X W, He N, Jin S and Jin S R. 2022b. EGDE-Net: a building change detection method for high-resolution remote sensing imagery based on edge guidance and differential enhancement. ISPRS Journal of Photogrammetry and Remote Sensing, 191: 203-222 [DOI: 10.1016/j.isprsjprs.2022.07.016http://dx.doi.org/10.1016/j.isprsjprs.2022.07.016]

Daudt R C, Le Saux B and Boulch A. 2018. Fully convolutional Siamese networks for change detection//2018 25th IEEE International Conference on Image Processing (ICIP). Athens: IEEE: 4063-4067 [DOI: 10.1109/ICIP.2018.8451652http://dx.doi.org/10.1109/ICIP.2018.8451652]

Daudt R C, Le Saux B, Boulch A and Gousseau Y. 2019. Multitask learning for large-scale semantic change detection. Computer Vision and Image Understanding, 187: 102783 [DOI: 10.1016/j.cviu.2019.07.003http://dx.doi.org/10.1016/j.cviu.2019.07.003]

Diakogiannis F I, Waldner F, Caccetta P and Wu C. 2020. ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing, 162: 94-114 [DOI: 10.1016/j.isprsjprs.2020.01.013http://dx.doi.org/10.1016/j.isprsjprs.2020.01.013]

Du P J and Liu S C. 2012. Change detection from multi-temporal remote sensing images by integrating multiple features. Journal of Remote Sensing, 16(4): 663-677

杜培军, 柳思聪. 2012. 融合多特征的遥感影像变化检测. 遥感学报, 16(4): 663-677 [DOI: 10.11834/jrs.20121168http://dx.doi.org/10.11834/jrs.20121168]

El-Hattab M M. 2016. Applying post classification change detection technique to monitor an Egyptian coastal zone (Abu Qir Bay). The Egyptian Journal of Remote Sensing and Space Science, 19(1): 23-36 [DOI: 10.1016/j.ejrs.2016.02.002http://dx.doi.org/10.1016/j.ejrs.2016.02.002]

Fang H, Guo S C, Wang X, Liu S C, Lin C and Du P J. 2023. Automatic urban scene-level binary change detection based on a novel sample selection approach and advanced triplet neural network. IEEE Transactions on Geoscience and Remote Sensing, 61: 5601518 [DOI: 10.1109/TGRS.2023.3235917http://dx.doi.org/10.1109/TGRS.2023.3235917]

Fang S, Li K Y, Shao J Y and Li Z. 2022. SNUNet-CD: a densely connected Siamese network for change detection of VHR images. IEEE Geoscience and Remote Sensing Letters, 19: 8007805 [DOI: 10.1109/LGRS.2021.3056416http://dx.doi.org/10.1109/LGRS.2021.3056416]

Foley J A, DeFries R, Asner G P, Barford C, Bonan G, Carpenter S R, Chapin F S, Coe M T, Daily G C, Gibbs H K, Helkowski J H, Holloway T, Howard E A, Kucharik C J, Monfreda C, Patz J A, Prentice I C, Ramankutty N and Snyder P K. 2005. Global consequences of land use. Science, 309(5734): 570-574 [DOI: 10.1126/science.1111772http://dx.doi.org/10.1126/science.1111772]

Gong M G, Niu X D, Zhang P Z and Li Z T. 2017. Generative adversarial networks for change detection in multispectral imagery. IEEE Geoscience and Remote Sensing Letters, 14(12): 2310-2314 [DOI: 10.1109/LGRS.2017.2762694http://dx.doi.org/10.1109/LGRS.2017.2762694]

Hecheltjen A, Thonfeld F and Menz G. 2014. Recent advances in remote sensing change detection-A review//Manakos I and Braun M, eds. Land Use and Land Cover Mapping in Europe. Dordrecht: Springer: 145-178 [DOI: 10.1007/978-94-007-7969-3_10http://dx.doi.org/10.1007/978-94-007-7969-3_10]

Hermosilla T, Wulder M A, White J C, Coops N C and Hobart G W. 2015. Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sensing of Environment, 170: 121-132 [DOI: 10.1016/j.rse.2015.09.004http://dx.doi.org/10.1016/j.rse.2015.09.004]

Jiang J W, Xing Y J, Wei W, Yan E P, Xiang J and Mo D K. 2022. DSNUNet: an improved forest change detection network by combining Sentinel-1 and Sentinel-2 images. Remote Sensing, 14(19): 5046 [DOI: 10.3390/rs14195046http://dx.doi.org/10.3390/rs14195046]

Jin S M, Yang L M, Danielson P, Homer C, Fry J and Xian G. 2013. A comprehensive change detection method for updating the National Land Cover Database to circa 2011. Remote Sensing of Environment, 132: 159-175 [DOI: 10.1016/j.rse.2013.01.012http://dx.doi.org/10.1016/j.rse.2013.01.012]

Kaviani Baghbaderani R, Qu Y, Qi H R and Stutts C. 2020. Representative-discriminative learning for open-set land cover classification of satellite imagery//16th European Conference on Computer Vision-ECCV 2020. Glasgow: Springer: 1-17 [DOI: 10.1007/978-3-030-58577-8_1http://dx.doi.org/10.1007/978-3-030-58577-8_1]

Ke L, Lin Y K, Zeng Z, Zhang L F and Meng L K. 2018. Adaptive change detection with significance test. IEEE Access, 6: 27442-27450 [DOI: 10.1109/ACCESS.2018.2807380http://dx.doi.org/10.1109/ACCESS.2018.2807380]

Kussul N, Lavreniuk M, Skakun S and Shelestov A. 2017. Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5): 778-782 [DOI: 10.1109/LGRS.2017.2681128http://dx.doi.org/10.1109/LGRS.2017.2681128]

Li N, Zhu X F, Pan Y Z and Zhan P. 2018. Optimized SVM based on artificial bee colony algorithm for remote sensing image classification. Journal of Remote Sensing, 22(4): 559-569

李楠, 朱秀芳, 潘耀忠, 詹培. 2018. 人工蜂群算法优化的SVM遥感影像分类. 遥感学报, 22(4): 559-569 [DOI: 10.11834/jrs.20187176http://dx.doi.org/10.11834/jrs.20187176]

Li Z M, Yan C X, Sun Y and Xin Q C. 2022. A densely attentive refinement network for change detection based on very-high-resolution bitemporal remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60: 4409818 [DOI: 10.1109/TGRS.2022.3159544http://dx.doi.org/10.1109/TGRS.2022.3159544]

Lin T Y, Goyal P, Girshick R, He K M and Dollár P. 2017. Focal loss for dense object detection//Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice: IEEE: 2999-3007 [DOI: 10.1109/ICCV.2017.324http://dx.doi.org/10.1109/ICCV.2017.324]

Liu H Y, Wang Z S, Shang F H, Zhang M Y, Gong M G, Ge F H and Jiao L C. 2019. A novel deep framework for change detection of multi-source heterogeneous images//2019 International Conference on Data Mining Workshops (ICDMW). Beijing: IEEE: 165-171 [DOI: 10.1109/ICDMW.2019.00034http://dx.doi.org/10.1109/ICDMW.2019.00034]

Liu J F, Chen K M, Xu G L, Sun X, Yan M L, Diao W H and Han H Z. 2020b. Convolutional neural network-based transfer learning for optical aerial images change detection. IEEE Geoscience and Remote Sensing Letters, 17(1): 127-131 [DOI: 10.1109/LGRS.2019.2916601http://dx.doi.org/10.1109/LGRS.2019.2916601]

Liu R C, Jiang D W, Zhang L L and Zhang Z T. 2020a. Deep depthwise separable convolutional network for change detection in optical aerial images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 1109-1118 [DOI: 10.1109/JSTARS.2020.2974276http://dx.doi.org/10.1109/JSTARS.2020.2974276]

Liu S C, Bruzzone L, Bovolo F, Zanetti M and Du P J. 2015. Sequential spectral change vector analysis for iteratively discovering and detecting multiple changes in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 53(8): 4363-4378 [DOI: 10.1109/TGRS.2015.2396686http://dx.doi.org/10.1109/TGRS.2015.2396686]

Lu D S, Li G Y and Moran E. 2014. Current situation and needs of change detection techniques. International Journal of Image and Data Fusion, 5(1): 13-38 [DOI: 10.1080/19479832.2013.868372http://dx.doi.org/10.1080/19479832.2013.868372]

Lv N, Chen C, Qiu T and Sangaiah A K. 2018. Deep learning and superpixel feature extraction based on contractive autoencoder for change detection in SAR images. IEEE Transactions on Industrial Informatics, 14(12): 5530-5538 [DOI: 10.1109/TII.2018.2873492http://dx.doi.org/10.1109/TII.2018.2873492]

Milletari F, Navab N and Ahmadi S A. 2016. V-Net: fully convolutional neural networks for volumetric medical image segmentation//2016 Fourth International Conference on 3D Vision (3DV). Stanford: IEEE: 565-571 [DOI: 10.1109/3DV.2016.79http://dx.doi.org/10.1109/3DV.2016.79]

Moustakidis S, Mallinis G, Koutsias N, Theocharis J B and Petridis V. 2012. SVM-based fuzzy decision trees for classification of high spatial resolution remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 50(1): 149-169 [DOI: 10.1109/TGRS.2011.2159726http://dx.doi.org/10.1109/TGRS.2011.2159726]

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]

Peng L K, Chen X H, Chen J, Zhao W Z and Cao X. 2022. Understanding the role of receptive field of convolutional neural network for cloud detection in Landsat 8 OLI imagery. IEEE Transactions on Geoscience and Remote Sensing, 60: 5407317 [DOI: 10.1109/TGRS.2022.3150083http://dx.doi.org/10.1109/TGRS.2022.3150083]

Seong S and Choi J. 2021. Semantic segmentation of urban buildings using a high-resolution network (HRNet) with channel and spatial attention gates. Remote Sensing, 13(16): 3087 [DOI: 10.3390/rs13163087http://dx.doi.org/10.3390/rs13163087]

Shafique A, Cao G, Khan Z, Asad M and Aslam M. 2022. Deep learning-based change detection in remote sensing images: a review. Remote Sensing, 14(4): 871 [DOI: 10.3390/rs14040871http://dx.doi.org/10.3390/rs14040871]

Shi W Z, Zhang M, Zhang R, Chen S X and Zhan Z. 2020. Change detection based on artificial intelligence: state-of-the-art and challenges. Remote Sensing, 12(10): 1688 [DOI: 10.3390/rs12101688http://dx.doi.org/10.3390/rs12101688]

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]

Tan M X and Le Q V. 2019. EfficientNet: rethinking model scaling for convolutional neural networks//Proceedings of the 36th International Conference on Machine Learning. Long Beach: [s.n.]: 6105-6114

Tian S Q, Zhong Y F, Zheng Z, Ma A L, Tan X C and Zhang L P. 2022. Large-scale deep learning based binary and semantic change detection in ultra high resolution remote sensing imagery: from benchmark datasets to urban application. ISPRS Journal of Photogrammetry and Remote Sensing, 193: 164-186 [DOI: 10.1016/j.isprsjprs.2022.08.012http://dx.doi.org/10.1016/j.isprsjprs.2022.08.012]

Van Etten A, Hogan D, Manso J M, Shermeyer J, Weir N and Lewis R. 2021. The multi-temporal urban development spacenet dataset//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE: 6394-6403 [DOI: 10.1109/CVPR46437. 2021.00633http://dx.doi.org/10.1109/CVPR46437.2021.00633]

Varghese A, Gubbi J, Ramaswamy A and Balamuralidhar P. 2019. ChangeNet: a deep learning architecture for visual change detection//European Conference on Computer Vision (ECCV). Munich: Springer: 129-145 [DOI: 10.1007/978-3-030-11012-3_10http://dx.doi.org/10.1007/978-3-030-11012-3_10]

Wan L, Tian Ye, Kang W C and Ma L. 2022. D-TNet: category-awareness based difference-threshold alternative learning network for remote sensing image change detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 5633316 [DOI: 10.1109/TGRS.2022.3213925http://dx.doi.org/10.1109/TGRS.2022.3213925]

Wan L, Xiang Y M and You H J. 2019. A post-classification comparison method for SAR and optical images change detection. IEEE Geoscience and Remote Sensing Letters, 16(7): 1026-1030 [DOI: 10.1109/LGRS.2019.2892432http://dx.doi.org/10.1109/LGRS.2019.2892432]

Wang J D, Sun K, Cheng T H, Jiang B R, Deng C R, Zhao Y, Liu D, Mu Y D, Tan M K and Wang X G. 2021. Deep high-resolution representation learning for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(10): 3349-3364 [DOI: 10.1109/TPAMI.2020.2983686http://dx.doi.org/10.1109/TPAMI.2020.2983686]

Wiratama W, Lee J, Park S E and Sim D. 2018. Dual-dense convolution network for change detection of high-resolution panchromatic imagery. Applied Sciences, 8(10): 1785 [DOI: 10.3390/app8101785http://dx.doi.org/10.3390/app8101785]

Wu C, Du B and Zhang L P. 2023. Fully convolutional change detection framework with generative adversarial network for unsupervised, weakly supervised and regional supervised change detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8): 9774-9788 [DOI: 10.1109/TPAMI.2023.3237896http://dx.doi.org/10.1109/TPAMI.2023.3237896]

Wu C, Zhang L P and Du B. 2017. Kernel slow feature analysis for scene change detection. IEEE Transactions on Geoscience and Remote Sensing, 55(4): 2367-2384 [DOI: 10.1109/TGRS.2016.2642125http://dx.doi.org/10.1109/TGRS.2016.2642125]

Xia H, Tian Y G, Zhang L H and Li S L. 2022. A deep Siamese postclassification fusion network for semantic change detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 5622716 [DOI: 10.1109/TGRS.2022.3171067http://dx.doi.org/10.1109/TGRS.2022.3171067]

Xian G, Homer C and Fry J. 2009. Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods. Remote Sensing of Environment, 113(6): 1133-1147 [DOI: 10.1016/j.rse.2009.02.004http://dx.doi.org/10.1016/j.rse.2009.02.004]

Xie G Y and Niculescu S. 2021. Mapping and monitoring of land cover/land use (LCLU) changes in the crozon peninsula (Brittany, France) from 2007 to 2018 by machine learning algorithms (support vector machine, random forest, and convolutional neural network) and by post-classification comparison (PCC). Remote Sensing, 13(19): 3899 [DOI: 10.3390/rs13193899http://dx.doi.org/10.3390/rs13193899]

Xu Q F, Chen K M, Sun X, Zhang Y, Li H and Xu G L. 2022. Pseudo-Siamese capsule network for aerial remote sensing images change detection. IEEE Geoscience and Remote Sensing Letters, 19: 6000405 [DOI: 10.1109/LGRS.2020.3022512http://dx.doi.org/10.1109/LGRS.2020.3022512]

Xu Q F, Chen K M, Zhou G Y and Sun X. 2021. Change capsule network for optical remote sensing image change detection. Remote Sensing, 13(14): 2646 [DOI: 10.3390/rs13142646http://dx.doi.org/10.3390/rs13142646]

Yang B, Mao Y, Chen J, Liu J Q, Chen J and Yan K. 2022. Review of remote sensing change detection in deep learning: bibliometric and analysis. Journal of Remote Sensing: 1-18

杨彬, 毛银, 陈晋, 刘建强, 陈杰, 闫凯. 2022. 深度学习的遥感变化检测综述: 文献计量与分析. 遥感学报: 1-18 [DOI: 10.11834/jrs.20222156http://dx.doi.org/10.11834/jrs.20222156]

Yang K P, Xia G S, Liu Z C, Du B, Yang W, Pelillo M and Zhang L P. 2021. Semantic change detection with asymmetric Siamese networks. arXiv preprint arXiv: 2010.05687

Yang M J, Jiao L C, Liu F, Hou B and Yang S Y. 2019. Transferred deep learning-based change detection in remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 57(9): 6960-6973 [DOI: 10.1109/TGRS.2019.2909781http://dx.doi.org/10.1109/TGRS.2019.2909781]

Zhan Y, Fu K, Yan M L, Sun X, Wang H Q and Qiu X S. 2017. Change detection based on deep Siamese convolutional network for optical aerial images. IEEE Geoscience and Remote Sensing Letters, 14(10): 1845-1849 [DOI: 10.1109/LGRS.2017.2738149http://dx.doi.org/10.1109/LGRS.2017.2738149]

Zhang L P and Wu C. 2017. Advance and future development of change detection for multi-temporal remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 46(10): 1447-1459

张良培, 武辰. 2017. 多时相遥感影像变化检测的现状与展望. 测绘学报, 46(10): 1447-1459 [DOI: 10.11947/j.AGCS.2017.20170340http://dx.doi.org/10.11947/j.AGCS.2017.20170340]

Zhang X Z, Su H, Zhang C, Gu X W, Tan X H and Atkinson P M. 2021. Robust unsupervised small area change detection from SAR imagery using deep learning. ISPRS Journal of Photogrammetry and Remote Sensing, 173: 79-94 [DOI: 10.1016/j.isprsjprs.2021.01.004http://dx.doi.org/10.1016/j.isprsjprs.2021.01.004]

Zhao J J, Gong M G, Liu J and Jiao L C. 2014. Deep learning to classify difference image for image change detection//2014 International Joint Conference on Neural Networks (IJCNN). Beijing: IEEE: 411-417 [DOI: 10.1109/IJCNN.2014.6889510http://dx.doi.org/10.1109/IJCNN.2014.6889510]

Zheng Z, Zhong Y F, Tian S Q, Ma A L and Zhang L P. 2022. ChangeMask: deep multi-task encoder-transformer-decoder architecture for semantic change detection. ISPRS Journal of Photogrammetry and Remote Sensing, 183: 228-239 [DOI: 10.1016/j.isprsjprs.2021.10.015http://dx.doi.org/10.1016/j.isprsjprs.2021.10.015]

Zheng Z, Zhong Y F, Wang J J, Ma A L and Zhang L P. 2021. Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: from natural disasters to man-made disasters. Remote Sensing of Environment, 265: 112636 [DOI: 10.1016/j.rse.2021.112636http://dx.doi.org/10.1016/j.rse.2021.112636]

Zhou Z W, Siddiquee M M R, Tajbakhsh N and Liang J M. 2020. Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging, 39(6): 1856-1867 [DOI: 10.1109/TMI.2019.2959609http://dx.doi.org/10.1109/TMI.2019.2959609]

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]

Zhu X X, Tuia D, Mou L C, Xia G S, Zhang L P, Xu F and Fraundorfer F. 2017. Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4): 8-36 [DOI: 10.1109/MGRS.2017.2762307http://dx.doi.org/10.1109/MGRS.2017.2762307]

文章被引用时，请邮件提醒。

提交

深度学习的遥感变化检测综述：文献计量与分析

人工智能时代的遥感变化检测技术：继承、发展与挑战