[1] Xu Y J, Shu H. Image change extraction method based on ISODATA and change vector analysis[J]. Geospatial Information, 18, 73-76, 9(2020).

[2] Zhou T, Zou J, Cui Y H et al. Water extraction from Remote sensing image based on principal component analysis and support vector machine and its application[J/OL]. Water Resource Conservation, 1-15. http://kns.cnki.net/kcms/detail/32.1356.TV.20220407.1836.006.html

[3] Wang M C, Zhang H M, Sun W W et al. A coarse-to-fine deep learning based land use change detection method for high-resolution remote sensing images[J]. Remote Sensing, 12, 1933(2020).

[4] Sun X X, Zhang J X, Yan Q et al. A summary on current techniques and prospects of remote sensing change detection[J]. Remote Sensing Information, 26, 119-123(2011).

[5] Iandola F N, Han S, Moskewicz M W et al. SqueezeNet: AlexNet-level accuracy with[EB/OL], 5. https://arxiv.org/abs/1602.07360

[6] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C], 3431-3440(2015).

[7] Luo S Q, Li H, Chen R X. Building extraction of remote sensing images using ResUNet+ with enhanced multiscale features[J]. Laser & Optoelectronics Progress, 59, 0828007(2022).

[8] Rajaram R N, Ohn-Bar E, Trivedi M M. RefineNet: refining object detectors for autonomous driving[J]. IEEE Transactions on Intelligent Vehicles, 1, 358-368(2016).

[9] Tang F P, Wu Z H, Cai D Y et al. Experiment and analysis of difference image change detection based on UNet[J]. Geospatial Information, 20, 25-28(2022).

[10] Wang M C, Zhu C Y, Chen X Y et al. Building change detectionin high resolution remote sensing images based on FPN Res-Unet[J]. Journal of Jilin University (Earth Science Edition), 51, 296-306(2021).

[11] Hao M, Tian Y, Zhang H et al. Research on building change detection of UAV images based on DeepLabV3+ deep learning[J]. Modern Surveying and Mapping, 44, 1-4(2021).

[12] Chen L X, Yu H Y, Li Y C et al. A model for detecting building changes incorporating attention mechanisms[J]. Science of Surveying and Mapping, 47, 153-159(2022).

[13] Zang X M, Li Z P, Hu J X. A change detection method of remote sensing image based on Siamese network[J]. China Computer & Communication, 33, 56-58(2021).

[14] Fan Z Z, Wang S, Zhang H et al. W-Net-based segmentation for remote sensing satellite image of high resolution[J]. Journal of South China University of Technology (Natural Science Edition), 48, 114-124(2020).

[15] Zhang H M, Wang M C, Wang F Y et al. A novel squeeze-and-excitation W-net for 2D and 3D building change detection with multi-source and multi-feature remote sensing data[J]. Remote Sensing, 13, 440(2021).

[16] Wang S, Fan Z Z, Li Z M et al. An effective lunar crater recognition algorithm based on convolutional neural network[J]. Remote Sensing, 12, 2694(2020).

[17] Zhan G L, Liu H, Yang L. Industrial smoke image segmentation based on improved attention W-Net[J]. Computer Integrated Manufacturing Systems, 29, 628-637(2023).

[18] Xia X D, Kulis B. W-Net: a deep model for fully unsupervised image segmentation[EB/OL]. https://arxiv.org/abs/1711.08506

[19] Woo S, Park J, Lee J Y, Ferrari V, Hebert M, Sminchisescu C et al. CBAM: convolutional block attention module[M]. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).

[20] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).

[21] Ji S P, Wei S Q, Lu M. Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 574-586(2019).

[22] Chen H, Shi Z W. A spatial-temporal attention-based method and a new dataset for remote sensing image change detection[J]. Remote Sensing, 12, 1662(2020).