Consistency-guided lightweight network for semi-supervised binary change detection of buildings in remote sensing images

References

• Abdi, G., and S. Jabari. 2021. “A Multi-Feature Fusion Using Deep Transfer Learning for Earthquake Building Damage Detection.” Canadian Journal of Remote Sensing 47 (2): 337–26. https://doi.org/10.1080/07038992.2021.1925530.
   Web of Science ®Google Scholar
• Badrinarayanan, V., A. Kendall, and R. Cipolla. 2017. “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (12): 2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615.
   PubMed Web of Science ®Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Bruzzone, L., and D. Prieto. 2000. “Automatic Analysis of the Difference Image for Unsupervised Change Detection.” IEEE Transactions on Geoscience and Remote Sensing 38 (3): 1171–1182. https://doi.org/10.1109/36.843009.
   Web of Science ®Google Scholar
• Buslaev, A., V. Iglovikov, E. Khvedchenya, A. Parinov, M. Druzhinin, and A. Kalinin. 2020. “Albumentations: Fast and Flexible Image Augmentations.” Information 11 (2): 125. https://doi.org/10.3390/info11020125.
   Web of Science ®Google Scholar
• Cao, Y., and X. Huang. 2023. “A Full-Level Fused Cross-Task Transfer Learning Method for Building Change Detection Using Noise-Robust Pretrained Networks on Crowdsourced Labels.” Remote Sensing of Environment 284:113371. https://doi.org/10.1016/j.rse.2022.113371.
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1109/LGRS.2009.2025059.
   Web of Science ®Google Scholar
• Chen, J., B. Sun, L. Wang, B. Fang, Y. Chang, Y. Li, J. Zhang, X. Lyu, and G. Chen. 2022. “Semi-Supervised Semantic Segmentation Framework with Pseudo Supervisions for Land-Use/land-Cover Mapping in Coastal Areas.” International Journal of Applied Earth Observation and Geoinformation 112:102881. https://doi.org/10.1016/j.jag.2022.102881.
   Web of Science ®Google Scholar
• Chen, H., C. Wu, B. Du, L. Zhang, and L. Wang. 2020. “Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network.” IEEE Transactions on Geoscience and Remote Sensing 58 (4): 2848–2864. https://doi.org/10.1109/TGRS.2019.2956756.
   Web of Science ®Google Scholar
• Chen, Z., Y. Zhou, B. Wang, X. Xu, N. He, S. Jin, and S. Jin. 2022. “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. https://doi.org/10.1016/j.isprsjprs.2022.07.016.
   Web of Science ®Google Scholar
• Chollet, F. 2017. “Xception: Deep Learning with Depthwise Separable Convolutions.” 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, HI, United states, 1800–1807.
   Google Scholar
• Daudt, R. C., B. L. Saux, and A. Boulch, 2018. “Fully Convolutional Siamese Networks for Change Detection.“ 25th IEEE International Conference on Image Processing (ICIP 2018), Athens, Greece, 4063–4067.
   Google Scholar
• Deng, Y., Y. Meng, J. Chen, A. Yue, D. Liu, and J. Chen. 2023. “TChange: A Hybrid Transformer-CNN Change Detection Network.” Remote Sensing 15 (5): 1219. https://doi.org/10.3390/rs15051219.
   Web of Science ®Google Scholar
• Ding, Q., Z. Shao, X. Huang, and O. Altan. 2021. “DSA-Net: A Novel Deeply Supervised Attention-Guided Network for Building Change Detection in High-Resolution Remote Sensing Images.” International Journal of Applied Earth Observation and Geoinformation 105:102591. https://doi.org/10.1016/j.jag.2021.102591.
   Web of Science ®Google Scholar
• Ding, Q., Z. Shao, X. Huang, O. Altan, and B. Hu. 2022. “Time-Series Land Cover Mapping and Urban Expansion Analysis Using OpenStreetmap Data and Remote Sensing Big Data: A Case Study of Guangdong-Hong Kong-Macao Greater Bay Area, China.” International Journal of Applied Earth Observation and Geoinformation 113:113001. https://doi.org/10.1016/j.jag.2022.103001.
   Web of Science ®Google Scholar
• Fang, S., K. Li, J. Shao, and Z. Li. 2022. “SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images.” IEEE Geoscience and Remote Sensing Letters 19:1–5. https://doi.org/10.1109/LGRS.2021.3056416.
   Web of Science ®Google Scholar
• Fan, Y., A. Kukleva, D. Dai, and B. Schiele. 2023. “Revisiting Consistency Regularization for Semi-Supervised Learning.” International Journal of Computer Vision 131 (3): 626–643. https://doi.org/10.1007/s11263-022-01723-4.
   Web of Science ®Google Scholar
• Foody, G. 2002. “Status of Land Cover Classification Accuracy Assessment.” Remote Sensing of Environment 80 (1): 185–201. https://doi.org/10.1016/S0034-4257(01)00295-4.
   Web of Science ®Google Scholar
• Foody, G. 2010. “Assessing the Accuracy of Land Cover Change with Imperfect Ground Reference Data.” Remote Sensing of Environment 114 (10): 2271–2285. https://doi.org/10.1016/j.rse.2010.05.003.
   Web of Science ®Google Scholar
• Gong, M., T. Zhan, P. Zhang, and Q. Miao. 2017. “Superpixel-Based Difference Representation Learning for Change Detection in Multispectral Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 55 (5): 2658–2673. https://doi.org/10.1109/TGRS.2017.2650198.
   Web of Science ®Google Scholar
• Guo, Z., and S. Du. 2017. “Mining Parameter Information for Building Extraction and Change Detection with Very High-Resolution Imagery and GIS Data.” GIScience and Remote Sensing 54 (1): 38–63. https://doi.org/10.1080/15481603.2016.1250328.
   Web of Science ®Google Scholar
• Habib, T., J. Inglada, G. Mercier, and J. Chanussot. 2009. “Support Vector Reduction in SVM Algorithm for Abrupt Change Detection in Remote Sensing.” IEEE Geoscience and Remote Sensing Letters 6 (3): 606–610. https://doi.org/10.1109/LGRS.2009.2020306.
   Web of Science ®Google Scholar
• Han, T., M. Wulder, J. White, N. Coops, M. Alvarez, and C. Butson. 2007. “An Efficient Protocol to Process Landsat Images for Change Detection with Tasselled Cap Transformation.” IEEE Geoscience and Remote Sensing Letters 4 (1): 147–151. https://doi.org/10.1109/LGRS.2006.887066.
   Web of Science ®Google Scholar
• Hou, B., Q. Liu, H. Wang, and Y. Wang. 2020. “From W-Net to CDGAN: Bitemporal Change Detection via Deep Learning Techniques.” IEEE Transactions on Geoscience and Remote Sensing 58 (3): 1790–1802. https://doi.org/10.1109/TGRS.2019.2948659.
   Web of Science ®Google Scholar
• Hou, B., Y. Wang, and Q. Liu. 2017. “Change Detection Based on Deep Features and Low Rank.” IEEE Geoscience and Remote Sensing Letters 14 (12): 2418–2422. https://doi.org/10.1109/LGRS.2017.2766840.
   Web of Science ®Google Scholar
• Huang, D., Y. Tang, and R. Qin. 2022. “An Evaluation of PlanetScope Images for 3D Reconstruction and Change Detection - Experimental Validations with Case Studies.” GIScience and Remote Sensing 59 (1): 744–761. https://doi.org/10.1080/15481603.2022.2060595.
   Web of Science ®Google Scholar
• Hung, W., Y. Tsai, Y. Liou, Y. Lin, and M. Yang, 2019. “Adversarial Learning for Semi-Supervised Semantic Segmentation.” 29th British Machine Vision Conference (BMVC 2018), Newcastle, United kingdom.
   Google Scholar
• Ji, S., S. Wei, and M. Lu. 2019. “Fully Convolutional Networks for Multisource Building Extraction from an Open Aerial and Satellite Imagery Data Set.” IEEE Transactions on Geoscience and Remote Sensing 57 (1): 574–586. https://doi.org/10.1109/TGRS.2018.2858817.
   Web of Science ®Google Scholar
• Kalantar, B., N. Ueda, H. Al-Najjar, and A. Halin. 2020. “Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection Based on Pre-And Post-Event Orthophoto Images.” Remote Sensing 12 (21): 1–20. https://doi.org/10.3390/rs12213529.
   Web of Science ®Google Scholar
• Lei, Y., X. Liu, J. Shi, C. Lei, and J. Wang. 2019. “Multiscale Superpixel Segmentation with Deep Features for Change Detection.” IEEE Access 7:36600–36616. https://doi.org/10.1109/ACCESS.2019.2902613.
   Web of Science ®Google Scholar
• Li, J., X. Huang, L. Tu, T. Zhang, and L. Wang. 2022. “A Review of Building Detection from Very High Resolution Optical Remote Sensing Images.” GIScience and Remote Sensing 59 (1): 1199–1225. https://doi.org/10.1080/15481603.2022.2101727.
   Web of Science ®Google Scholar
• Li, M., J. Im, and C. Beier. 2013. “Machine Learning Approaches for Forest Classification and Change Analysis Using Multi-Temporal Landsat TM Images Over Huntington Wildlife Forest.” GIScience and Remote Sensing 50 (4): 361–384. https://doi.org/10.1080/15481603.2013.819161.
   Web of Science ®Google Scholar
• Li, Q., Y. Shi, and X. Zhu. 2022. “Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training.” IEEE Transactions on Geoscience and Remote Sensing 60:1–17. https://doi.org/10.1109/TGRS.2022.3174636.
   Web of Science ®Google Scholar
• Liu, Y., W. Xin, and Z. Chen. 2014. “Remote Sensing Change Detection Study Based on Adaptive Threshold in Pixel Ratio Method.“ Land Surface Remote Sensing II, Beijing, China, 9260.
   Google Scholar
• Long, J., E. Shelhamer, and T. Darrell. 2015. “Fully Convolutional Networks for Semantic Segmentation.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), Boston, MA, United states, 431–440.
   Google Scholar
• Mahmoodzadeh, H. 2007. “Digital Change Detection Using Remotely Sensed Data for Monitoring Green Space Destruction in Tabriz.” International Journal of Environmental Research 1 (1): 35–41.
   Web of Science ®Google Scholar
• Mittal, S., M. Tatarchenko, and T. Brox. 2021. “Semi-Supervised Semantic Segmentation with High- and Low-Level Consistency.” IEEE Transactions on Pattern Analysis and Machine Intelligence 43 (4): 1369–1379. https://doi.org/10.1109/TPAMI.2019.2960224.
   PubMed Web of Science ®Google Scholar
• Nielsen, A., K. Conradsen, and J. Simpson. 1998. “Multivariate Alteration Detection (MAD) and MAF Postprocessing in Multispectral, Bitemporal Image Data: New Approaches to Change Detection Studies.” Remote Sensing of Environment 64 (1): 1–19. https://doi.org/10.1016/S0034-4257(97)00162-4.
   Web of Science ®Google Scholar
• Ouali, Y., C. Hudelot, and M. Tami. 2020. “Semi-Supervised Semantic Segmentation with Cross-Consistency Training.” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020), Virtual, Online, United states, 12671–12681.
   Google Scholar
• Park, S., and A. Song. 2023. “Hybrid Approach Using Deep Learning and Graph Comparison for Building Change Detection.” GIScience and Remote Sensing 60 (1). https://doi.org/10.1080/15481603.2023.2220525.
   Web of Science ®Google Scholar
• Peng, D., L. Bruzzone, Y. Zhang, H. Guan, H. Ding, and X. Huang. 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. https://doi.org/10.1109/TGRS.2020.3011913.
   Web of Science ®Google Scholar
• Peng, D., Y. Zhang, and H. Guan. 2019. “End-To-End Change Detection for High Resolution Satellite Images Using Improved Unet++.” Remote Sensing 11 (11): 1382. https://doi.org/10.3390/rs11111382.
   Web of Science ®Google Scholar
• Rokni, K., A. Ahmad, K. Solaimani, and S. Hazini. 2015. “A New Approach for Surface Water Change Detection: Integration of Pixel Level Image Fusion and Image Classification Techniques.” International Journal of Applied Earth Observation and Geoinformation 34 (1): 226–234. https://doi.org/10.1016/j.jag.2014.08.014.
   Web of Science ®Google Scholar
• Ronneberger, O., P. Fischer, and T. Brox. 2015. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015), Munich, Germany, 234–241.
   Google Scholar
• Saha, S., F. Bovolo, and L. Bruzzone. 2019. “Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images.” IEEE Transactions on Geoscience and Remote Sensing 57 (6): 3677–3693. https://doi.org/10.1109/TGRS.2018.2886643.
   Web of Science ®Google Scholar
• Sandler, M., A. Howard, M. Zhu, A. Zhmoginov, and L. Chen. 2018. “MobileNetV2: Inverted Residuals and Linear Bottlenecks.” 31th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, United states, 4510–4520.
   Google Scholar
• Shafique, A., G. Cao, Z. Khan, M. Asad, and M. Aslam. 2022. “Deep Learning-Based Change Detection in Remote Sensing Images: A Review.” Remote Sensing 14 (4): 871. https://doi.org/10.3390/rs14040871.
   Web of Science ®Google Scholar
• Shu, Q., J. Pan, Z. Zhang, and M. Wang. 2022. “MTCNet: Multitask Consistency Network with Single Temporal Supervision for Semi-Supervised Building Change Detection.” International Journal of Applied Earth Observation and Geoinformation 115:103110. https://doi.org/10.1016/j.jag.2022.103110.
   Web of Science ®Google Scholar
• Sokolova, M., and G. Lapalme. 2009. “A Systematic Analysis of Performance Measures for Classification Tasks.” Information Processing & Management 45 (4): 427–437. https://doi.org/10.1016/j.ipm.2009.03.002.
   Web of Science ®Google Scholar
• Sun, C., J. Wu, H. Chen, and C. Du. 2022. “SemiSanet: A Semi-Supervised High-Resolution Remote Sensing Image Change Detection Model Using Siamese Networks with Graph Attention.” Remote Sensing 14 (12): 2801. https://doi.org/10.3390/rs14122801.
   Web of Science ®Google Scholar
• Tan, M., and Q. Le. 2019. “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.” 36th International Conference on Machine Learning (ICML 2019), Long Beach, CA, United states, 10691–10700.
   Google Scholar
• Van Engelen, J., and H. Hoos. 2020. “A Survey on Semi-Supervised Learning.” Machine Learning 109 (2): 373–440. https://doi.org/10.1007/s10994-019-05855-6.
   Web of Science ®Google Scholar
• Wang, B., P. Jiang, J. Gao, W. Huo, Z. Yang, and Y. Liao. 2023. “A Lightweight Few-Shot Marine Object Detection Network for Unmanned Surface Vehicles.” Ocean Engineering 277:114329. https://doi.org/10.1016/j.oceaneng.2023.114329.
   Web of Science ®Google Scholar
• Wang, P., and E. Sertel. 2023. “Multi-Frame Super-Resolution of Remote Sensing Images Using Attention-Based GAN Models.” Knowledge-Based Systems 266:110387. https://doi.org/10.1016/j.knosys.2023.110387.
   Web of Science ®Google Scholar
• Wang, X., Q. Zhao, P. Jiang, Y. Zheng, L. Yuan, and P. Yuan. 2022. “LDS-YOLO: A Lightweight Small Object Detection Method for Dead Trees from Shelter Forest.” Computers and Electronics in Agriculture 198:107035. https://doi.org/10.1016/j.compag.2022.107035.
   Web of Science ®Google Scholar
• Wele, G., and V. Patel. 2022. “Revisiting Consistency Regularization for Semi-Supervised Change Detection in Remote Sensing Images.” arXiv: 2204.08454.
   Google Scholar
• Wieland, M., S. Martinis, R. Kiefl, and V. Gstaiger. 2023. “Semantic Segmentation of Water Bodies in Very High-Resolution Satellite and Aerial Images.” Remote Sensing of Environment 287:113452. https://doi.org/10.1016/j.rse.2023.113452.
   Web of Science ®Google Scholar
• Wu, C., L. Zhang, and B. Du. 2017. “Kernel Slow Feature Analysis for Scene Change Detection.” IEEE Transactions on Geoscience and Remote Sensing 55 (4): 2367–2384. https://doi.org/10.1109/TGRS.2016.2642125.
   Web of Science ®Google Scholar
• Xiao, P., G. Sheng, X. Zhang, H. Liu, and R. Guo. 2021. “Direction-Dominated Change Vector Analysis for Forest Change Detection.” International Journal of Applied Earth Observation and Geoinformation 103:102492. https://doi.org/10.1016/j.jag.2021.102492.
   Web of Science ®Google Scholar
• Yang, H., Y. Chen, W. Wu, S. Pu, X. Wu, Q. Wan, and W. Dong. 2023. “A Lightweight Siamese Neural Network for Building Change Detection Using Remote Sensing Images.” Remote Sensing 15 (4): 928. https://doi.org/10.3390/rs15040928.
   Web of Science ®Google Scholar
• Yang, R., S. Wang, X. Wu, T. Liu, and X. Liu. 2022. “Using Lightweight Convolutional Neural Network to Track Vibration Displacement in Rotating Body Video.” Mechanical Systems and Signal Processing 177:109137. https://doi.org/10.1016/j.ymssp.2022.109137.
   Google Scholar
• Ye, W., J. Lao, Y. Liu, C. Chang, Z. Zhang, H. Li, and H. Zhou. 2022. “Pine Pest Detection Using Remote Sensing Satellite Images Combined with a Multi-Scale Attention-UNet Model.” Ecological Informatics 72:101906. https://doi.org/10.1016/j.ecoinf.2022.101906.
   Web of Science ®Google Scholar
• Yin, M., S. He, T. Soomro, and H. Yuan. 2023. “Efficient Skeleton-Based Action Recognition via Multi-Stream Depthwise Separable Convolutional Neural Network.” Expert Systems with Applications 226:120080. https://doi.org/10.1016/j.eswa.2023.120080.
   Web of Science ®Google Scholar
• Zhang, P., M. Gong, L. Su, J. Liu, and Z. Li. 2016. “Change Detection Based on Deep Feature Representation and Mapping Transformation for Multi-Spatial-Resolution Remote Sensing Images.” ISPRS Journal of Photogrammetry and Remote Sensing 116:24–41. https://doi.org/10.1016/j.isprsjprs.2016.02.013.
   Web of Science ®Google Scholar
• Zhang, H., G. Ma, and Y. Zhang. 2022. “Intelligent-BCD: A Novel Knowledge-Transfer Building Change Detection Framework for High-Resolution Remote Sensing Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15:5065–5075. https://doi.org/10.1109/JSTARS.2022.3184298.
   Web of Science ®Google Scholar
• Zhang, H., G. Ma, Y. Zhang, B. Wang, H. Li, and L. Fan. 2023. “MCHA-Net: A Multi-End Composite Higher-Order Attention Network Guided with Hierarchical Supervised Signal for High-Resolution Remote Sensing Image Change Detection.” ISPRS Journal of Photogrammetry and Remote Sensing 202:40–68. https://doi.org/10.1016/j.isprsjprs.2023.05.033.
   Web of Science ®Google Scholar
• Zhang, J., Z. Shao, Q. Ding, X. Huang, Y. Wang, X. Zhou, and D. Li. 2023. “AERNet: An Attention-Guided Edge Refinement Network and a Dataset for Remote Sensing Building Change Detection.” IEEE Transactions on Geoscience and Remote Sensing 61:1–16. https://doi.org/10.1109/TGRS.2023.3308936.
   Web of Science ®Google Scholar
• Zhang, C., L. Wang, S. Cheng, and Y. Li. 2022. “SwinSunet: Pure Transformer Network for Remote Sensing Image Change Detection.” IEEE Transactions on Geoscience and Remote Sensing 60:1–13. https://doi.org/10.1109/TGRS.2022.3160007.
   Web of Science ®Google Scholar
• Zhang, Y., F. Xie, X. Song, H. Zhou, Y. Yang, H. Zhang, and J. Liu. 2022. “A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval.” Computers in Biology and Medicine 151 (Pt A): 106272. https://doi.org/10.1016/j.compbiomed.2022.106272.
   PubMedGoogle Scholar
• Zhang, C., P. Yue, D. Tapete, L. Jiang, B. Shangguan, L. Huang, and G. Liu. 2020. “A Deeply Supervised Image Fusion Network for Change Detection in High Resolution Bi-Temporal Remote Sensing Images.” ISPRS Journal of Photogrammetry and Remote Sensing 166:183–200. https://doi.org/10.1016/j.isprsjprs.2020.06.003.
   Web of Science ®Google Scholar
• Zhang, R., H. Zhang, X. Ning, X. Huang, J. Wang, and W. Cui. 2023. “Global-Aware Siamese Network for Change Detection on Remote Sensing Images.” ISPRS Journal of Photogrammetry and Remote Sensing 199:61–72. https://doi.org/10.1016/j.isprsjprs.2023.04.001.
   Web of Science ®Google Scholar
• Zhang, X., X. Zhou, M. Lin, and J. Sun. 2018. “ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices.” 31th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, United states, 6848–6856.
   Google Scholar
• Zhao, H., J. Shi, X. Qi, X. Wang, and J. Jia. 2017. “Pyramid Scene Parsing Network.” 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, HI, United states, 6230–6239.
   Google Scholar
• Zheng, Z., Y. Wan, Y. Zhang, S. Xiang, D. Peng, and B. Zhang. 2021. “CLNet: Cross-Layer Convolutional Neural Network for Change Detection in Optical Remote Sensing Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 175:247–267. https://doi.org/10.1016/j.isprsjprs.2021.03.005.
   Web of Science ®Google Scholar
• Zhou, Z., M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang. 2018. “UNet++: A Nested U-Net Architecture for Medical Image Segmentation.“ Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Granada, Spain, 11045:3–11.
   Google Scholar
• Zhu, Z., S. Qiu, and S. Ye. 2022. “Remote Sensing of Land Change: A Multifaceted Perspective.” Remote Sensing of Environment 282:113266. https://doi.org/10.1016/j.rse.2022.113266.
   Web of Science ®Google Scholar
• Zhu, F., C. Wang, B. Zhu, C. Sun, and C. Qi. 2023. “An Improved Generative Adversarial Networks for Remote Sensing Image Super-Resolution Reconstruction via Multi-Scale Residual Block.” The Egyptian Journal of Remote Sensing and Space Sciences 26 (1): 151–160. https://doi.org/10.1016/j.ejrs.2022.12.008.
   Web of Science ®Google Scholar
• Zou, Y., Z. Yu, B. Kumar, and J. Wang. 2018. “Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training.” 15th European Conference on Computer Vision (ECCV 2018), Munich, Germany, 297–313.
   Google Scholar