- 1.
Belkin M, Hsu D, Ma S Y and Mandal S. 2019. Reconciling modern machine-learning practice and the classical bias-variance trade-off. Proceedings of the National Academy of Sciences of the United States of America, 116(32): 15849-15854
- 2.
Duan G Y, Gong H L, Li X J and Chen B B. 2014. Shadow extraction based on characteristic components and object-oriented method for high-resolution images. Journal of Remote Sensing (in Chinese), 18(4): 760-770
- 3.
Han B, Yao Q M, Yu X R, Niu G, Xu M, Hu W H, Tsang I W and Sugiyama M. 2018. Co-teaching: robust training of deep neural networks with extremely noisy labels//Proceedings of the 32nd International Conference on Neural Information Processing Systems. Montréal: Curran Associates Inc.: 8536-8546
- 4.
He K M, Fan H Q, Wu Y X, Xie S N and Girshick R. 2020. Momentum contrast for unsupervised visual representation learning//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE: 9726-9735
- 5.
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: IEEE: 770-778
- 6.
Hou L, Vicente T F Y, Hoai M and Samaras D. 2021. Large scale shadow annotation and detection using lazy annotation and stacked CNNs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(4): 1337-1351
- 7.
Ji S P, Wei S Q and Lu M. 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
- 8.
Jing L L and Tian Y L. 2021. Self-supervised visual feature learning with deep neural networks: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(11): 4037-4058
- 9.
Jung H, Oh Y, Jeong S, Lee C and Jeon T. 2022. Contrastive self-supervised learning with smoothed representation for remote sensing. IEEE Geoscience and Remote Sensing Letters, 19: 8010105
- 10.
Khan S H, Bennamoun M, Sohel F and Togneri R. 2014. Automatic feature learning for robust shadow detection//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE: 1939-1946
- 11.
Li D R, Tong Q X, Li R X, Gong J Y and Zhang L P. 2012. Current issues in high-resolution Earth observation technology. Science China Earth Science, 55(7): 1043-1051
- 12.
Li J Y, Hu Q W and Ai M Y. 2016. Joint model and observation cues for single-image shadow detection. Remote Sensing, 8(6): 484
- 13.
Li Y, Gong P. 2005. Integrating photogrammetry and image analysis for shadow detection. Journal of Remote Sensing (in Chinese), 9(4): 357-362
- 14.
Liao C J, Zhao J, Xing J, Wen J G and Liu Q H. 2023. Research on classification of GF satellite data application products and construction of common product system. National Remote Sensing Bulletin, 27(3): 563-572
- 15.
Lin Z Q, Sun J, Davis A and Snavely N. 2020. Visual chirality//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE: 12292-12300
- 16.
Liu D Y, Zhang J P, Wu Y H and Zhang Y. 2021a. A shadow detection algorithm based on multiscale spatial attention mechanism for aerial remote sensing images. IEEE Geoscience and Remote Sensing Letters, 19: 6003905
- 17.
Liu X, Zhang F J, Hou Z Y, Mian L, Wang Z Y, Zhang J and Tang J. 2021b. Self-supervised learning: generative or contrastive. IEEE Transactions on Knowledge and Data Engineering, 35(1): 857-876
- 18.
Luo S, Li H F and Shen H F. 2020. Deeply supervised convolutional neural network for shadow detection based on a novel aerial shadow imagery dataset. ISPRS Journal of Photogrammetry and Remote Sensing, 167: 443-457
- 19.
Luo S, Li H F, Zhu R Z, Gong Y T and Shen H F. 2021. ESPFNet: an edge-aware spatial pyramid fusion network for salient shadow detection in aerial remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 4633-4646
- 20.
Ma C, Feng D J and Zhang L. 2012. Detecting the building shadow with the two algorithms of threshold segmentation and mathematical morphology. Surveying and Mapping, 35(4): 151-154
- 21.
Makarau A, Richter R, Muller R and Reinartz P. 2011. Adaptive shadow detection using a blackbody radiator model. IEEE Transactions on Geoscience and Remote Sensing, 49(6): 2049-2059
- 22.
Mu X D, Bai K, You X, Zhu Y Q and Chen X B. 2021. Remote sensing image feature extraction and classification based on contrastive learning method. Optics and Precision Engineering, 29(9): 2222-2234
- 23.
Peng X Y, Wang K, Zhu Z, Wang M and You Y. 2022. Crafting better contrastive views for Siamese representation learning//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE: 16010-16019
- 24.
Selvaraju R R, Cogswell M, Das A, Vedantam R, Parikh D and Batra D. 2017. Grad-CAM: visual explanations from deep networks via gradient-based localization//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice: IEEE: 618-626
- 25.
Shahtahmassebi A, Yang N, Wang K, Moore N and Shen Z Q. 2013. Review of shadow detection and de-shadowing methods in remote sensing. Chinese Geographical Science, 23(4): 403-420
- 26.
Shen L, Wee Chua T and Leman K. 2015. Shadow optimization from structured deep edge detection//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE: 2067-2074
- 27.
Shi L, Fang J and Zhao Y F. 2023. Automatic shadow detection in high-resolution multispectral remote sensing images. Computers and Electrical Engineering, 105: 108557
- 28.
Stojnic V and Risojevic V. 2021. Self-supervised learning of remote sensing scene representations using contrastive multiview coding//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Nashville: IEEE: 1182-1191
- 29.
Su N, Zhang Y, Tian S, Yan Y M and Miao X Y. 2016. Shadow detection and removal for occluded object information recovery in urban high-resolution panchromatic satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6): 2568-2582
- 30.
Tao C, Qi J, Lu W P, Wang H and Li H F. 2022. Remote sensing image scene classification with self-supervised paradigm under limited labeled samples. IEEE Geoscience and Remote Sensing Letters, 19: 8004005
- 31.
Tao C, Yin Z W, Zhu Q and Li H F. 2021. Remote sensing image intelligent interpretation: from supervised learning to self-supervised learning. Acta Geodaetica et Cartographica Sinica, 50(8): 1122-1134
- 32.
Tong X D. 2016. Development of China high-resolution earth observation system. Journal of Remote Sensing (in Chinese), 20(5): 775-780
- 33.
Wang Q J, Yan L, Yuan Q Q and Ma Z L. 2017. An automatic shadow detection method for VHR remote sensing orthoimagery. Remote Sensing, 9(5): 469
- 34.
Xie Y K, Feng D J, Chen H Y, Liao Z Y, Zhu J, Li C N and Baik S W. 2022. An omni-scale global-local aware network for shadow extraction in remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 193: 29-44
- 35.
Yang Y, Guo M Q and Zhu Q Q. 2021. CADNet: top-down contextual saliency detection network for high spatial resolution remote sensing image shadow detection//Proceedings of 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. Brussels: IEEE: 4075-4078
- 36.
Yu D F, Yin J P and Zhang G M. 2008. An automatic shadow detection method for remote sensing images based on gray histogram. Computer Engineering and Science, 30(12): 43-44, 93
- 37.
Zhou P C, Cheng G, Yao X W and Han J W. 2021. Machine learning paradigms in high-resolution remote sensing image interpretation. National Remote Sensing Bulletin, 25(1): 182-197
- 38.
Zhu Q Q, Yang Y, Sun X L and Guo M Q. 2022. CDANet: contextual detail-aware network for high-spatial-resolution remote-sensing imagery shadow detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 5617415