Semantic segmentation of clouds in satellite images based on U-Net++ architecture and attention mechanism
•Proposed U-Net++ based network with attention mechanism for cloud segmentation.•Proposed SEUNet++ achieves an IoU score of 91.8 %.•Transfer learning helps to improve the segmentation results.•SEUNet++ performs better than the original U-Net++ architecture.•SEUNet++ improves the state-of-the-art by...
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| Published in: | Expert systems with applications Vol. 209; p. 118380 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
15.12.2022
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| Subjects: | |
| ISSN: | 0957-4174, 1873-6793 |
| Online Access: | Get full text |
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| Abstract | •Proposed U-Net++ based network with attention mechanism for cloud segmentation.•Proposed SEUNet++ achieves an IoU score of 91.8 %.•Transfer learning helps to improve the segmentation results.•SEUNet++ performs better than the original U-Net++ architecture.•SEUNet++ improves the state-of-the-art by lifting the IoU score by 0.23 %.
The presence of clouds in satellite imagery may pose hindrances to the accurate and reliable analysis of the objects present on the land. Therefore, automatic cloud detection is a vital pre-processing step before lending the satellite images to any further analysis. This is a challenging task due to the varying thickness and densities of clouds. It is also very difficult to distinguish clouds from certain terrains such as snow and white sandy beaches. This paper proposes a deep learning based algorithm to solve the problem of cloud segmentation on the Landsat 8 multispectral dataset, 95-Cloud: SEUNet++. Specifically, the proposed model consists of a U-Net++ semantic segmentation model with a lightweight channel attention mechanism. We also experimented with using different encoder backbones in the U-Net++ encoder-decoder architecture such as ResNet (Residual Neural Networks) variants including ResNet-18, ResNet-34, ResNet-50, and ResNet-101, DenseNet-264, CSPNet (Cross Stage Partial Network), and EfficientNet-B8 and compared their performance. The experimental results show that the proposed architecture achieves an IoU (Intersection over Union) score of 91.8 %, improving the state-of-the-art on the task by 0.23 %. It also boosts the accuracy, precision, and recall values creating crisp cloud boundaries and detecting even thin layers of clouds. We also experimented using transfer learning and found that it has a positive impact on the cloud segmentation task. The proposed model also beats the original U-Net++ architecture in terms of various evaluation metrics such as the IoU score, accuracy, precision, and recall. The experimental results thereby demonstrate that our model is computationally efficient and achieves precise segmentation results. |
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| AbstractList | •Proposed U-Net++ based network with attention mechanism for cloud segmentation.•Proposed SEUNet++ achieves an IoU score of 91.8 %.•Transfer learning helps to improve the segmentation results.•SEUNet++ performs better than the original U-Net++ architecture.•SEUNet++ improves the state-of-the-art by lifting the IoU score by 0.23 %.
The presence of clouds in satellite imagery may pose hindrances to the accurate and reliable analysis of the objects present on the land. Therefore, automatic cloud detection is a vital pre-processing step before lending the satellite images to any further analysis. This is a challenging task due to the varying thickness and densities of clouds. It is also very difficult to distinguish clouds from certain terrains such as snow and white sandy beaches. This paper proposes a deep learning based algorithm to solve the problem of cloud segmentation on the Landsat 8 multispectral dataset, 95-Cloud: SEUNet++. Specifically, the proposed model consists of a U-Net++ semantic segmentation model with a lightweight channel attention mechanism. We also experimented with using different encoder backbones in the U-Net++ encoder-decoder architecture such as ResNet (Residual Neural Networks) variants including ResNet-18, ResNet-34, ResNet-50, and ResNet-101, DenseNet-264, CSPNet (Cross Stage Partial Network), and EfficientNet-B8 and compared their performance. The experimental results show that the proposed architecture achieves an IoU (Intersection over Union) score of 91.8 %, improving the state-of-the-art on the task by 0.23 %. It also boosts the accuracy, precision, and recall values creating crisp cloud boundaries and detecting even thin layers of clouds. We also experimented using transfer learning and found that it has a positive impact on the cloud segmentation task. The proposed model also beats the original U-Net++ architecture in terms of various evaluation metrics such as the IoU score, accuracy, precision, and recall. The experimental results thereby demonstrate that our model is computationally efficient and achieves precise segmentation results. |
| ArticleNumber | 118380 |
| Author | Sachan, Manoj Kumar Kaur Buttar, Preetpal |
| Author_xml | – sequence: 1 givenname: Preetpal surname: Kaur Buttar fullname: Kaur Buttar, Preetpal email: preetpal@sliet.ac.in – sequence: 2 givenname: Manoj Kumar surname: Sachan fullname: Sachan, Manoj Kumar |
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| Cites_doi | 10.1016/j.rse.2014.12.014 10.1145/3065386 10.5194/isprs-annals-V-3-2020-505-2020 10.14358/PERS.72.10.1179 10.1109/TGRS.2020.2994349 10.1016/j.ecoinf.2021.101279 10.1109/TGRS.2015.2504369 10.1016/j.rse.2019.05.024 10.3390/rs12122001 10.3390/rs6064907 10.3390/rs11212591 10.3390/rs11040403 10.1109/JSTARS.2021.3070786 10.1016/j.neucom.2021.02.091 10.3390/rs12213530 10.1080/19942060.2021.2009374 10.1109/TPAMI.2016.2644615 10.1109/TGRS.2020.2991398 10.3390/sym12061056 10.3390/rs11192312 10.1109/WACV.2017.58 10.1117/12.2529586 10.1016/j.rse.2019.03.007 10.1109/JSTARS.2017.2686488 10.1016/j.rse.2011.10.028 10.1109/TPAMI.2019.2913372 10.3390/rs10071079 10.1016/j.isprsjprs.2017.08.011 10.1109/TPAMI.2017.2699184 10.1007/BF00344251 10.1109/TGRS.2019.2904868 10.1109/TGRS.2013.2290237 10.1016/j.catena.2021.105189 10.1109/ACCESS.2020.2970836 10.1109/3DV.2016.79 10.3390/s21062153 10.1109/TGRS.2017.2692264 10.1109/ACCESS.2019.2951750 10.1016/j.aquaeng.2020.102053 10.1080/19942060.2021.1974093 10.3390/ijgi8090390 10.3390/rs10060877 10.1109/CVPRW50498.2020.00203 10.1016/j.rse.2019.03.039 10.3390/rs11151774 10.3390/rs10101626 10.1109/CIBCB48159.2020.9277638 |
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| Keywords | Cloud segmentation Semantic segmentation U-Net 95-Cloud ResNet Multispectral satellite data |
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| References | Iglovikov, V., Mushinskiy, S., & Osin, V. (2017). Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition. Retrieved from http://arxiv.org/abs/1908.03809. Izmailov, Podoprikhin, Garipov, Vetrov, Wilson (b0185) 2018 Chai, Newsam, Zhang, Qiu, Huang (b0050) 2019; 225 Buslaev, Iglovikov, Khvedchenya, Parinov, Druzhinin, Kalinin (b0045) 2020; 11 Fan, Xu, Wu, Zheng, Tao (b0085) 2020; 8 – Huang, Wang, Jia, Lu, Li, He (b0155) 2021; 443 Krizhevsky, Sutskever, Hinton (b0225) 2012 Zhao, Shi, Qi, Wang, Jia (b0425) 2017; 2017 Yi, Zhang, Zhang, Zhang, Li, Zhao (b0415) 2019; 11 Simonyan, Zisserman (b0345) 2015 Jiao, Huo, Hu, Tang (b0210) 2020; 12 Woo, Park, Lee, Kweon (b0385) 2018 Retrieved from http://arxiv.org/abs/1911.02685. Milletari, F., Navab, N., & Ahmadi, S. A. (2016). V-Net: Fully convolutional neural networks for volumetric medical image segmentation. Penatti, Nogueira, dos Santos (b0305) 2015; 2015 Zhao, Du, Wang, Emery (b0430) 2017; 132 (April), 464–472. https://doi.org/10.1109/WACV.2017.58. , 565–571. https://doi.org/10.1109/3DV.2016.79. - . Retrieved from https://arxiv.org/abs/1412.6980. 505–511. https://doi.org/10.5194/isprs-annals-V-3-2020-505-2020. Retrieved from http://arxiv.org/abs/1708.07120. Mateo-García, Gómez-Chova, Amorós-López, Muñoz-Marí, Camps-Valls (b0265) 2018; 10 Ian Goodfellow, Yoshua Bengio, A. C. (2016). (pp. 1462–1471). Lille, France: PMLR. Retrieved from Howe, J., Pula, K., & Reite, A. A. (2019). Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery. Retrieved from http://arxiv.org/abs/1804.05340. Retrieved from https://arxiv.org/abs/1412.7062. Retrieved from http://arxiv.org/abs/1803.09820. Hu, Shen, Albanie, Sun, Wu (b0145) 2020; 42 (Vol. 28). Curran Associates, Inc. Retrieved from Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., … Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. Retrieved from https://arxiv.org/abs/1706.06169. Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., … He, Q. (2019). A Comprehensive Survey on Transfer Learning. MIT Press. Retrieved from Banan, Nasiri, Taheri-Garavand (b0025) 2020; 89 Shotton, Johnson, Cipolla (b0340) 2008; 2008 Xie, Shi, Shi, Yin, Zhao (b0395) 2017; 10 Qiu, Zhu, He (b0315) 2019; 231 Ulmas, P., & Liiv, I. (2020). Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification. Long, Shelhamer, Darrell (b0255) 2015 Retrieved from https://arxiv.org/abs/2003.02899. Irish, Barker, Goward, Arvidson (b0180) 2006; 72 Chen, Papandreou, Kokkinos, Murphy, Yuille (b0065) 2018 Retrieved from http://arxiv.org/abs/1609.04747. Bharath, K. (2021). U-Net Architecture for Image Segmentation. Retrieved March 21, 2022, from Guo, Cao, Liu, Gao (b0120) 2020; 12 Zhang, Guindon, Li (b0420) 2014; 52 Du, S. (2020, February 25). Understanding Dice Loss for Crisp Boundary Detection. Retrieved March 22, 2022, from AI Salon website Zi, Xie, Jiang (b0460) 2018; 10 Retrieved from http://arxiv.org/abs/1809.04985. Shamshirband, Rabczuk, Chau (b0335) 2019; 7 Hou, Liu, Zhang, Li (b0130) 2021; 21 Tan, M., & Le, Q. V. (2021). Peng, Zhang, Yu, Luo, Sun (b0310) 2017; 2017 1571–1580. IEEE Computer Society. https://doi.org/10.1109/CVPRW50498.2020.00203. Li, Tan, Chen, Luo, Gao, Jia, Wang (b0235) 2020; 2020 Noh, Hong, Han (b0295) 2015 Gregor, K., Danihelka, I., Graves, A., Rezende, D., & Wierstra, D. (2015). DRAW: A Recurrent Neural Network For Image Generation. In F. Bach & D. Blei (Eds.) Liu, W., & Zeng, K. (2018). SparseNet: A Sparse DenseNet for Image Classification. Wu, H., Zhang, J., Huang, K., Liang, K., & Yu, Y. (2019). FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation. Lin, Milan, Shen, Reid (b0245) 2017; 2017 Fukushima (b0100) 1980; 36 Jeppesen, Jacobsen, Inceoglu, Skjødeberg (b0200) 2019; 229 Kingma, D. P., & Ba, J. L. (2014). Adam: A Method for Stochastic Optimization. Jadon, S. (2020). Xu, Ba, Kiros, Cho, Courville, Salakhudinov, Bengio (b0400) 2015 Mohajerani, Saeedi (b0280) 2019; 1029–1032 Retrieved from http://arxiv.org/abs/1903.07288. Jiao, Huo, Hu, Tang (b0215) 2020; 12 Bai, Mas, Koshimura (b0020) 2018; 10 Hughes, Kennedy (b0165) 2019; 11 Mohajerani, Asad, Abhishek, Sharma, van Duynhoven, Saeedi (b0275) 2019; 2019 Rakhlin, Davydow, Nikolenko (b0320) 2018; 2018 Retrieved from http://arxiv.org/abs/1903.11816. Zhou, Rahman Siddiquee, Tajbakhsh, Liang (b0440) 2018 Chen, Zhang, Kashani, Jun, Bateni, Band, Chau (b0055) 2022; 16 Krizhevsky, Sutskever, Hinton (b0230) 2017; 60 Mohajerani, Saeedi (b0285) 2021; 14 Zhu, Woodcock (b0450) 2012; 118 Ma, D., Tang, P., & Zhao, L.-J. (2018). SiftingGAN: Generating and Sifting Labeled Samples to Improve the Remote Sensing Image Scene Classification Baseline in vitro. Retrieved from http://arxiv.org/abs/1704.04861. Chen, Chen, Chen, Jia, Cao, Liu (b0070) 2016; 54 Smith, L. N. (2018). A disciplined approach to neural network hyper-parameters: Part 1 - learning rate, batch size, momentum, and weight decay. Öztürk, Özkaya, Akdemir, Seyfi (b0300) 2018; 2018 Hughes, Hayes (b0160) 2014; 6 Li, He, Fang, Zheng, Fu, Yu (b0240) 2019; 11 Guo, Yang, Yue, Tan, Hou, Li (b0115) 2021; 59 Ronneberger, Fischer, Brox (b0325) 2015 Dwarampudi, M., & Reddy, N. V. S. (2019). Effects of padding on LSTMs and CNNs. Zheng, Wei, Sun, Anas, Li (b0435) 2019; 8 Badrinarayanan, Kendall, Cipolla (b0010) 2017; 39 Gonzales, Sakla (b0105) 2019 Afan, Osman, Essam, Ahmed, Huang, Kisi, El-Shafie (b0005) 2021; 15 He, Zhang, Ren, Sun (b0125) 2016 Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. (2015). Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. Fraz, Javed, Basit (b0095) 2008 Ruder, S. (2016). An overview of gradient descent optimization algorithms. Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural Machine Translation by Jointly Learning to Align and Translate. Bragagnolo, Rezende, da Silva, Grzybowski (b0040) 2021; 201 Ji, Dai, Lu, Zhang (b0205) 2021; 59 Bragagnolo, da Silva, Grzybowski (b0035) 2021; 62 arXiv:1409.0473. Retrieved from http://arxiv.org/abs/1409.0473. Neves, A., Körting, T., Fonseca, L., Girolamo Neto, C., Wittich, D., Costa, G., & Heipke, C. (2020). Semantic segmentation of Brazilian Savanna vegetation using high spatial resolution satellite data and U-Net. Zhu, Wang, Woodcock (b0445) 2015; 159 Tan, Le (b0365) 2019 Wang, C. Y., Mark Liao, H. Y., Wu, Y. H., Chen, P. Y., Hsieh, J. W., & Yeh, I. H. (2020). CSPNet: A new backbone that can enhance learning capability of CNN. Smith, L. N., & Topin, N. (2017). Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates. Smith, L. N. (2017). Cyclical learning rates for training neural networks. Xu, Wong, Clausi (b0405) 2017; 55 Francis, Sidiropoulos, Muller (b0090) 2019; 11 Jaderberg, M., Simonyan, K., Zisserman, A., & kavukcuoglu, koray. (2015). Spatial Transformer Networks. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, & R. Garnett (Eds.) Huang, Liu, Van Der Maaten, Weinberger (b0150) 2017; 2017 Yang, Guo, Yue, Liu, Hu, Li (b0410) 2019; 57 Penatti (10.1016/j.eswa.2022.118380_b0305) 2015; 2015 Irish (10.1016/j.eswa.2022.118380_b0180) 2006; 72 Li (10.1016/j.eswa.2022.118380_b0240) 2019; 11 Bragagnolo (10.1016/j.eswa.2022.118380_b0040) 2021; 201 Huang (10.1016/j.eswa.2022.118380_b0150) 2017; 2017 Francis (10.1016/j.eswa.2022.118380_b0090) 2019; 11 Qiu (10.1016/j.eswa.2022.118380_b0315) 2019; 231 Buslaev (10.1016/j.eswa.2022.118380_b0045) 2020; 11 Fukushima (10.1016/j.eswa.2022.118380_b0100) 1980; 36 10.1016/j.eswa.2022.118380_b0270 10.1016/j.eswa.2022.118380_b0390 10.1016/j.eswa.2022.118380_b0190 Xu (10.1016/j.eswa.2022.118380_b0405) 2017; 55 Zhang (10.1016/j.eswa.2022.118380_b0420) 2014; 52 Bragagnolo (10.1016/j.eswa.2022.118380_b0035) 2021; 62 10.1016/j.eswa.2022.118380_b0075 Mohajerani (10.1016/j.eswa.2022.118380_b0275) 2019; 2019 Peng (10.1016/j.eswa.2022.118380_b0310) 2017; 2017 10.1016/j.eswa.2022.118380_b0350 10.1016/j.eswa.2022.118380_b0030 Chen (10.1016/j.eswa.2022.118380_b0070) 2016; 54 Gonzales (10.1016/j.eswa.2022.118380_b0105) 2019 10.1016/j.eswa.2022.118380_b0195 Krizhevsky (10.1016/j.eswa.2022.118380_b0230) 2017; 60 10.1016/j.eswa.2022.118380_b0355 Simonyan (10.1016/j.eswa.2022.118380_b0345) 2015 10.1016/j.eswa.2022.118380_b0110 Rakhlin (10.1016/j.eswa.2022.118380_b0320) 2018; 2018 Afan (10.1016/j.eswa.2022.118380_b0005) 2021; 15 Badrinarayanan (10.1016/j.eswa.2022.118380_b0010) 2017; 39 Fraz (10.1016/j.eswa.2022.118380_b0095) 2008 Tan (10.1016/j.eswa.2022.118380_b0365) 2019 Hou (10.1016/j.eswa.2022.118380_b0130) 2021; 21 Zhu (10.1016/j.eswa.2022.118380_b0445) 2015; 159 Chen (10.1016/j.eswa.2022.118380_b0055) 2022; 16 Yang (10.1016/j.eswa.2022.118380_b0410) 2019; 57 Zhu (10.1016/j.eswa.2022.118380_b0450) 2012; 118 Ji (10.1016/j.eswa.2022.118380_b0205) 2021; 59 10.1016/j.eswa.2022.118380_b0380 10.1016/j.eswa.2022.118380_b0060 Jeppesen (10.1016/j.eswa.2022.118380_b0200) 2019; 229 Shamshirband (10.1016/j.eswa.2022.118380_b0335) 2019; 7 Lin (10.1016/j.eswa.2022.118380_b0245) 2017; 2017 10.1016/j.eswa.2022.118380_b0140 10.1016/j.eswa.2022.118380_b0260 Krizhevsky (10.1016/j.eswa.2022.118380_b0225) 2012 10.1016/j.eswa.2022.118380_b0220 Huang (10.1016/j.eswa.2022.118380_b0155) 2021; 443 Hu (10.1016/j.eswa.2022.118380_b0145) 2020; 42 Öztürk (10.1016/j.eswa.2022.118380_b0300) 2018; 2018 Guo (10.1016/j.eswa.2022.118380_b0120) 2020; 12 Mohajerani (10.1016/j.eswa.2022.118380_b0280) 2019; 1029–1032 Mohajerani (10.1016/j.eswa.2022.118380_b0285) 2021; 14 Yi (10.1016/j.eswa.2022.118380_b0415) 2019; 11 Mateo-García (10.1016/j.eswa.2022.118380_b0265) 2018; 10 Chen (10.1016/j.eswa.2022.118380_b0065) 2018 He (10.1016/j.eswa.2022.118380_b0125) 2016 Hughes (10.1016/j.eswa.2022.118380_b0160) 2014; 6 Jiao (10.1016/j.eswa.2022.118380_b0210) 2020; 12 Chai (10.1016/j.eswa.2022.118380_b0050) 2019; 225 10.1016/j.eswa.2022.118380_b0170 10.1016/j.eswa.2022.118380_b0290 Bai (10.1016/j.eswa.2022.118380_b0020) 2018; 10 10.1016/j.eswa.2022.118380_b0175 Li (10.1016/j.eswa.2022.118380_b0235) 2020; 2020 10.1016/j.eswa.2022.118380_b0250 Ronneberger (10.1016/j.eswa.2022.118380_b0325) 2015 10.1016/j.eswa.2022.118380_b0370 10.1016/j.eswa.2022.118380_b0135 Zhao (10.1016/j.eswa.2022.118380_b0425) 2017; 2017 10.1016/j.eswa.2022.118380_b0375 10.1016/j.eswa.2022.118380_b0330 Hughes (10.1016/j.eswa.2022.118380_b0165) 2019; 11 10.1016/j.eswa.2022.118380_b0015 10.1016/j.eswa.2022.118380_b0455 Banan (10.1016/j.eswa.2022.118380_b0025) 2020; 89 Shotton (10.1016/j.eswa.2022.118380_b0340) 2008; 2008 Guo (10.1016/j.eswa.2022.118380_b0115) 2021; 59 Zhou (10.1016/j.eswa.2022.118380_b0440) 2018 Zheng (10.1016/j.eswa.2022.118380_b0435) 2019; 8 Fan (10.1016/j.eswa.2022.118380_b0085) 2020; 8 Jiao (10.1016/j.eswa.2022.118380_b0215) 2020; 12 10.1016/j.eswa.2022.118380_b0080 10.1016/j.eswa.2022.118380_b0360 Xu (10.1016/j.eswa.2022.118380_b0400) 2015 Izmailov (10.1016/j.eswa.2022.118380_b0185) 2018 Noh (10.1016/j.eswa.2022.118380_b0295) 2015 Long (10.1016/j.eswa.2022.118380_b0255) 2015 Zhao (10.1016/j.eswa.2022.118380_b0430) 2017; 132 Xie (10.1016/j.eswa.2022.118380_b0395) 2017; 10 Zi (10.1016/j.eswa.2022.118380_b0460) 2018; 10 Woo (10.1016/j.eswa.2022.118380_b0385) 2018 |
| References_xml | – reference: . Retrieved from http://arxiv.org/abs/1804.05340. – volume: 89 year: 2020 ident: b0025 article-title: Deep learning-based appearance features extraction for automated carp species identification publication-title: Aquacultural Engineering – volume: 118 start-page: 83 year: 2012 end-page: 94 ident: b0450 article-title: Object-based cloud and cloud shadow detection in Landsat imagery publication-title: Remote Sensing of Environment – volume: 59 start-page: 700 year: 2021 end-page: 713 ident: b0115 article-title: CDnetV2: CNN-based cloud detection for remote sensing imagery with cloud-snow coexistence publication-title: IEEE Transactions on Geoscience and Remote Sensing – volume: 231 year: 2019 ident: b0315 article-title: Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery publication-title: Remote Sensing of Environment – reference: Smith, L. N. (2018). A disciplined approach to neural network hyper-parameters: Part 1 - learning rate, batch size, momentum, and weight decay. – reference: Jadon, S. (2020). – volume: 11 year: 2019 ident: b0415 article-title: Semantic segmentation of urban buildings from VHR remote sensing imagery using a deep convolutional neural network publication-title: Remote Sensing – volume: 72 start-page: 1179 year: 2006 end-page: 1188 ident: b0180 article-title: Characterization of the Landsat-7 ETM Automated Cloud-Cover Assessment (ACCA) algorithm publication-title: Photogrammetric Engineering and Remote Sensing – reference: Ian Goodfellow, Yoshua Bengio, A. C. (2016). – volume: 2017 start-page: 6230 year: 2017 end-page: 6239 ident: b0425 article-title: Pyramid scene parsing network publication-title: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 12 year: 2020 ident: b0210 article-title: Refined UNet: UNet-based refinement network for cloud and shadow precise segmentation publication-title: Remote Sensing – start-page: 234 year: 2015 end-page: 241 ident: b0325 article-title: U-Net: Convolutional Networks for Biomedical Image Segmentation publication-title: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 – volume: 55 start-page: 4913 year: 2017 end-page: 4924 ident: b0405 article-title: A novel Bayesian spatial-temporal random field model applied to cloud detection from remotely sensed imagery publication-title: IEEE Transactions on Geoscience and Remote Sensing – reference: , (April), 464–472. https://doi.org/10.1109/WACV.2017.58. – reference: Wang, C. Y., Mark Liao, H. Y., Wu, Y. H., Chen, P. Y., Hsieh, J. W., & Yeh, I. H. (2020). CSPNet: A new backbone that can enhance learning capability of CNN. – reference: . MIT Press. Retrieved from – volume: 36 start-page: 193 year: 1980 end-page: 202 ident: b0100 article-title: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position publication-title: Biol. Cybern. – volume: 60 start-page: 84 year: 2017 end-page: 90 ident: b0230 article-title: ImageNet classification with deep convolutional neural networks publication-title: Communications of the ACM – reference: Gregor, K., Danihelka, I., Graves, A., Rezende, D., & Wierstra, D. (2015). DRAW: A Recurrent Neural Network For Image Generation. In F. Bach & D. Blei (Eds.), – volume: 10 year: 2018 ident: b0265 article-title: Multitemporal cloud masking in the google earth engine publication-title: Remote Sensing – reference: Ulmas, P., & Liiv, I. (2020). Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification. – reference: (pp. 1462–1471). Lille, France: PMLR. Retrieved from – volume: 2017 start-page: 1743 year: 2017 end-page: 1751 ident: b0310 article-title: Large kernel matters — Improve semantic segmentation by global convolutional network publication-title: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 10 start-page: 3631 year: 2017 end-page: 3640 ident: b0395 article-title: Multilevel cloud detection in remote sensing images based on deep learning publication-title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing – reference: . Retrieved from http://arxiv.org/abs/1609.04747. – volume: 11 year: 2019 ident: b0240 article-title: Semantic segmentation-based building footprint extraction using very high-resolution satellite images and multi-source GIS data publication-title: Remote Sensing – reference: . Retrieved from http://arxiv.org/abs/1911.02685. – reference: Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural Machine Translation by Jointly Learning to Align and Translate. – volume: 2018 start-page: 1 year: 2018 end-page: 5 ident: b0300 article-title: Convolution kernel size effect on convolutional neural network in histopathological image processing applications publication-title: International Symposium on Fundamentals of Electrical Engineering (ISFEE) – volume: 52 start-page: 5540 year: 2014 end-page: 5547 ident: b0420 article-title: A robust approach for object-based detection and radiometric characterization of cloud shadow using haze optimized transformation publication-title: IEEE Transactions on Geoscience and Remote Sensing – volume: 159 year: 2015 ident: b0445 article-title: Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images publication-title: Remote Sensing of Environment – reference: Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. (2015). Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. – reference: Dwarampudi, M., & Reddy, N. V. S. (2019). Effects of padding on LSTMs and CNNs. – reference: , – reference: Kingma, D. P., & Ba, J. L. (2014). Adam: A Method for Stochastic Optimization. – volume: 14 start-page: 4254 year: 2021 end-page: 4266 ident: b0285 article-title: Cloud and cloud shadow segmentation for remote sensing imagery via filtered Jaccard loss function and parametric augmentation publication-title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing – reference: . Retrieved from http://arxiv.org/abs/1704.04861. – reference: . Retrieved from https://arxiv.org/abs/1412.6980. – reference: Smith, L. N. (2017). Cyclical learning rates for training neural networks. – reference: Milletari, F., Navab, N., & Ahmadi, S. A. (2016). V-Net: Fully convolutional neural networks for volumetric medical image segmentation. – reference: . Retrieved from https://arxiv.org/abs/1412.7062. – volume: 57 start-page: 6195 year: 2019 end-page: 6211 ident: b0410 article-title: CDnet: CNN-based cloud detection for remote sensing imagery publication-title: IEEE Transactions on Geoscience and Remote Sensing – reference: . Retrieved from http://arxiv.org/abs/1809.04985. – volume: 132 start-page: 48 year: 2017 end-page: 60 ident: b0430 article-title: Contextually guided very-high-resolution imagery classification with semantic segments publication-title: ISPRS Journal of Photogrammetry and Remote Sensing – year: 2015 ident: b0345 article-title: Very deep convolutional networks for large-scale image recognition publication-title: 3Rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings – volume: 225 start-page: 307 year: 2019 end-page: 316 ident: b0050 article-title: Cloud and cloud shadow detection in Landsat imagery based on deep convolutional neural networks publication-title: Remote Sensing of Environment – reference: - – reference: Neves, A., Körting, T., Fonseca, L., Girolamo Neto, C., Wittich, D., Costa, G., & Heipke, C. (2020). Semantic segmentation of Brazilian Savanna vegetation using high spatial resolution satellite data and U-Net. – reference: Iglovikov, V., Mushinskiy, S., & Osin, V. (2017). Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition. – reference: , 505–511. https://doi.org/10.5194/isprs-annals-V-3-2020-505-2020. – reference: – – reference: . Retrieved from http://arxiv.org/abs/1903.11816. – volume: 1029–1032 year: 2019 ident: b0280 article-title: Cloud-Net: An end-to-end cloud detection algorithm for landsat 8 imagery publication-title: International Geoscience and Remote Sensing Symposium (IGARSS) – volume: 8 start-page: 25111 year: 2020 end-page: 25121 ident: b0085 article-title: Spatiotemporal modeling for nonlinear distributed thermal processes based on KL decomposition, MLP and LSTM network publication-title: IEEE Access – volume: 42 start-page: 2011 year: 2020 end-page: 2023 ident: b0145 article-title: Squeeze-and-excitation networks publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – volume: 12 year: 2020 ident: b0215 article-title: Refined UNet V2: End-to-end patch-wise network for noise-free cloud and shadow segmentation publication-title: Remote Sensing – year: 2015 ident: b0255 article-title: Fully convolutional networks for semantic segmentation publication-title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition – reference: . Retrieved from http://arxiv.org/abs/1908.03809. – reference: (Vol. 28). Curran Associates, Inc. Retrieved from – start-page: 3 year: 2018 end-page: 11 ident: b0440 article-title: UNet++: A Nested U-Net Architecture for Medical Image Segmentation publication-title: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support – reference: Ruder, S. (2016). An overview of gradient descent optimization algorithms. – reference: . Retrieved from https://arxiv.org/abs/2003.02899. – volume: 10 year: 2018 ident: b0020 article-title: Towards operational satellite-based damage-mapping using U-net convolutional network: A case study of 2011 Tohoku Earthquake-Tsunami publication-title: Remote Sensing – volume: 59 start-page: 732 year: 2021 end-page: 748 ident: b0205 article-title: Simultaneous cloud detection and removal from bitemporal remote sensing images using cascade convolutional neural networks publication-title: IEEE Transactions on Geoscience and Remote Sensing – start-page: 2048 year: 2015 end-page: 2057 ident: b0400 publication-title: Show, Attend and Tell: Neural Image Caption Generation with Visual Attention – volume: 54 start-page: 2682 year: 2016 end-page: 2694 ident: b0070 article-title: An iterative haze optimized transformation for automatic cloud/haze detection of landsat imagery publication-title: IEEE Transactions on Geoscience and Remote Sensing – volume: 62 year: 2021 ident: b0035 article-title: Amazon forest cover change mapping based on semantic segmentation by U-Nets publication-title: Ecological Informatics – volume: 39 start-page: 2481 year: 2017 end-page: 2495 ident: b0010 article-title: SegNet: A deep convolutional encoder-decoder architecture for image segmentation publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – volume: 443 start-page: 26 year: 2021 end-page: 34 ident: b0155 article-title: See more than once: Kernel-sharing atrous convolution for semantic segmentation publication-title: Neurocomputing – reference: . Retrieved from http://arxiv.org/abs/1803.09820. – volume: 2017 start-page: 5168 year: 2017 end-page: 5177 ident: b0245 article-title: RefineNet: Multi-path refinement networks for high-resolution semantic segmentation publication-title: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 201 year: 2021 ident: b0040 article-title: Convolutional neural networks applied to semantic segmentation of landslide scars publication-title: CATENA – volume: 12 year: 2020 ident: b0120 article-title: Cloud detection for satellite imagery using attention-based U-net convolutional neural network publication-title: Symmetry – year: 2018 ident: b0385 article-title: CBAM: Convolutional block attention module publication-title: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) – volume: 8 year: 2019 ident: b0435 article-title: Using vehicle synthesis generative adversarial networks to improve vehicle detection in remote sensing images publication-title: ISPRS International Journal of Geo-Information – reference: , arXiv:1409.0473. Retrieved from http://arxiv.org/abs/1409.0473. – reference: . Retrieved from https://arxiv.org/abs/1706.06169. – reference: Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., … He, Q. (2019). A Comprehensive Survey on Transfer Learning. – year: 2018 ident: b0185 article-title: Averaging weights leads to wider optima and better generalization publication-title: ArXiv Preprint – volume: 15 start-page: 1420 year: 2021 end-page: 1439 ident: b0005 article-title: Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques publication-title: Engineering Applications of Computational Fluid Mechanics – volume: 21 start-page: 1 year: 2021 end-page: 21 ident: b0130 article-title: C-UNet: Complement UNet for remote sensing road extraction publication-title: Sensors – reference: Smith, L. N., & Topin, N. (2017). Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates. – reference: Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., … Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. – volume: 2019 start-page: 1965 year: 2019 end-page: 1969 ident: b0275 article-title: Cloudmaskgan: A content-aware unpaired image-to-image translation algorithm for remote sensing imagery publication-title: IEEE International Conference on Image Processing (ICIP) – reference: Liu, W., & Zeng, K. (2018). SparseNet: A Sparse DenseNet for Image Classification. – year: 2015 ident: b0295 article-title: Learning deconvolution network for semantic segmentation publication-title: Proceedings of the IEEE International Conference on Computer Vision – reference: . Retrieved from http://arxiv.org/abs/1708.07120. – start-page: 1097 year: 2012 end-page: 1105 ident: b0225 article-title: ImageNet classification with deep convolutional neural networks publication-title: Advances in Neural Information Processing Systems – reference: , 1571–1580. IEEE Computer Society. https://doi.org/10.1109/CVPRW50498.2020.00203. – year: 2019 ident: b0105 article-title: Semantic segmentation of clouds in satellite imagery using deep pre-trained U-nets publication-title: Proceedings - Applied Imagery Pattern Recognition Workshop – year: 2018 ident: b0065 article-title: DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – volume: 229 start-page: 247 year: 2019 end-page: 259 ident: b0200 article-title: A cloud detection algorithm for satellite imagery based on deep learning Remote Sensing of Environment A cloud detection algorithm for satellite imagery based on deep learning publication-title: Remote Sensing of Environment – reference: Du, S. (2020, February 25). Understanding Dice Loss for Crisp Boundary Detection. Retrieved March 22, 2022, from AI Salon website: – reference: . Retrieved from http://arxiv.org/abs/1903.07288. – volume: 2017 start-page: 2261 year: 2017 end-page: 2269 ident: b0150 article-title: Densely connected convolutional networks publication-title: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – start-page: 6105 year: 2019 end-page: 6114 ident: b0365 article-title: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks publication-title: Proceedings of the 36th International Conference on Machine Learning – volume: 11 year: 2019 ident: b0165 article-title: High-quality cloud masking of Landsat 8 imagery using convolutional neural networks publication-title: Remote Sensing – reference: Ma, D., Tang, P., & Zhao, L.-J. (2018). SiftingGAN: Generating and Sifting Labeled Samples to Improve the Remote Sensing Image Scene Classification Baseline in vitro. – volume: 2008 start-page: 1 year: 2008 end-page: 8 ident: b0340 article-title: Semantic texton forests for image categorization and segmentation publication-title: IEEE Conference on Computer Vision and Pattern Recognition – reference: , 565–571. https://doi.org/10.1109/3DV.2016.79. – reference: Jaderberg, M., Simonyan, K., Zisserman, A., & kavukcuoglu, koray. (2015). Spatial Transformer Networks. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, & R. Garnett (Eds.), – volume: 2020 start-page: 345 year: 2020 end-page: 349 ident: b0235 article-title: Attention Unet++: A nested attention-aware U-Net for Liver CT image segmentation publication-title: IEEE International Conference on Image Processing (ICIP) – volume: 16 start-page: 248 year: 2022 end-page: 261 ident: b0055 article-title: Forecast of rainfall distribution based on fixed sliding window long short-term memory publication-title: Engineering Applications of Computational Fluid Mechanics – reference: Howe, J., Pula, K., & Reite, A. A. (2019). Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery. – reference: Bharath, K. (2021). U-Net Architecture for Image Segmentation. Retrieved March 21, 2022, from – year: 2016 ident: b0125 article-title: Deep residual learning for image recognition publication-title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition – volume: 7 start-page: 164650 year: 2019 end-page: 164666 ident: b0335 article-title: A survey of deep learning techniques: Application in wind and solar energy resources publication-title: IEEE Access – reference: . – volume: 6 start-page: 4907 year: 2014 end-page: 4926 ident: b0160 article-title: Automated Detection of cloud and cloud shadow in single-date landsat imagery using neural networks and spatial post-processing publication-title: Remote Sensing – volume: 11 start-page: 1 year: 2020 end-page: 20 ident: b0045 article-title: Albumentations: Fast and flexible image augmentations publication-title: Information (Switzerland) – start-page: 232 year: 2008 end-page: 236 ident: b0095 article-title: A threshold selection method from gray-level histograms publication-title: 4Th IEEE International Conference on Emerging Technologies – reference: Tan, M., & Le, Q. V. (2021). – volume: 11 year: 2019 ident: b0090 article-title: CloudFCN: Accurate and robust cloud detection for satellite imagery with deep learning publication-title: Remote Sensing – volume: 10 year: 2018 ident: b0460 article-title: A cloud detection method for landsat 8 images based on PCANet publication-title: Remote Sensing – volume: 2018 start-page: 257 year: 2018 end-page: 2574 ident: b0320 article-title: Land cover classification from satellite imagery with U-Net and Lovász-Softmax loss publication-title: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) – reference: Wu, H., Zhang, J., Huang, K., Liang, K., & Yu, Y. (2019). FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation. – volume: 2015 start-page: 44 year: 2015 end-page: 51 ident: b0305 article-title: Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? publication-title: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) – volume: 2015 start-page: 44 year: 2015 ident: 10.1016/j.eswa.2022.118380_b0305 article-title: Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? publication-title: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) – volume: 159 year: 2015 ident: 10.1016/j.eswa.2022.118380_b0445 article-title: Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images publication-title: Remote Sensing of Environment doi: 10.1016/j.rse.2014.12.014 – volume: 2018 start-page: 257 year: 2018 ident: 10.1016/j.eswa.2022.118380_b0320 article-title: Land cover classification from satellite imagery with U-Net and Lovász-Softmax loss publication-title: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) – volume: 60 start-page: 84 issue: 6 year: 2017 ident: 10.1016/j.eswa.2022.118380_b0230 article-title: ImageNet classification with deep convolutional neural networks publication-title: Communications of the ACM doi: 10.1145/3065386 – ident: 10.1016/j.eswa.2022.118380_b0290 doi: 10.5194/isprs-annals-V-3-2020-505-2020 – ident: 10.1016/j.eswa.2022.118380_b0360 – volume: 2018 start-page: 1 year: 2018 ident: 10.1016/j.eswa.2022.118380_b0300 article-title: Convolution kernel size effect on convolutional neural network in histopathological image processing applications publication-title: International Symposium on Fundamentals of Electrical Engineering (ISFEE) – volume: 72 start-page: 1179 year: 2006 ident: 10.1016/j.eswa.2022.118380_b0180 article-title: Characterization of the Landsat-7 ETM Automated Cloud-Cover Assessment (ACCA) algorithm publication-title: Photogrammetric Engineering and Remote Sensing doi: 10.14358/PERS.72.10.1179 – ident: 10.1016/j.eswa.2022.118380_b0190 – volume: 2008 start-page: 1 year: 2008 ident: 10.1016/j.eswa.2022.118380_b0340 article-title: Semantic texton forests for image categorization and segmentation publication-title: IEEE Conference on Computer Vision and Pattern Recognition – ident: 10.1016/j.eswa.2022.118380_b0330 – volume: 59 start-page: 732 issue: 1 year: 2021 ident: 10.1016/j.eswa.2022.118380_b0205 article-title: Simultaneous cloud detection and removal from bitemporal remote sensing images using cascade convolutional neural networks publication-title: IEEE Transactions on Geoscience and Remote Sensing doi: 10.1109/TGRS.2020.2994349 – ident: 10.1016/j.eswa.2022.118380_b0370 – volume: 62 year: 2021 ident: 10.1016/j.eswa.2022.118380_b0035 article-title: Amazon forest cover change mapping based on semantic segmentation by U-Nets publication-title: Ecological Informatics doi: 10.1016/j.ecoinf.2021.101279 – volume: 2017 start-page: 1743 year: 2017 ident: 10.1016/j.eswa.2022.118380_b0310 article-title: Large kernel matters — Improve semantic segmentation by global convolutional network publication-title: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 54 start-page: 2682 issue: 5 year: 2016 ident: 10.1016/j.eswa.2022.118380_b0070 article-title: An iterative haze optimized transformation for automatic cloud/haze detection of landsat imagery publication-title: IEEE Transactions on Geoscience and Remote Sensing doi: 10.1109/TGRS.2015.2504369 – start-page: 6105 year: 2019 ident: 10.1016/j.eswa.2022.118380_b0365 article-title: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks – volume: 231 year: 2019 ident: 10.1016/j.eswa.2022.118380_b0315 article-title: Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery publication-title: Remote Sensing of Environment doi: 10.1016/j.rse.2019.05.024 – volume: 12 issue: 12 year: 2020 ident: 10.1016/j.eswa.2022.118380_b0210 article-title: Refined UNet: UNet-based refinement network for cloud and shadow precise segmentation publication-title: Remote Sensing doi: 10.3390/rs12122001 – ident: 10.1016/j.eswa.2022.118380_b0080 – ident: 10.1016/j.eswa.2022.118380_b0175 – year: 2015 ident: 10.1016/j.eswa.2022.118380_b0345 article-title: Very deep convolutional networks for large-scale image recognition – volume: 6 start-page: 4907 issue: 6 year: 2014 ident: 10.1016/j.eswa.2022.118380_b0160 article-title: Automated Detection of cloud and cloud shadow in single-date landsat imagery using neural networks and spatial post-processing publication-title: Remote Sensing doi: 10.3390/rs6064907 – volume: 11 issue: 21 year: 2019 ident: 10.1016/j.eswa.2022.118380_b0165 article-title: High-quality cloud masking of Landsat 8 imagery using convolutional neural networks publication-title: Remote Sensing doi: 10.3390/rs11212591 – volume: 11 issue: 4 year: 2019 ident: 10.1016/j.eswa.2022.118380_b0240 article-title: Semantic segmentation-based building footprint extraction using very high-resolution satellite images and multi-source GIS data publication-title: Remote Sensing doi: 10.3390/rs11040403 – ident: 10.1016/j.eswa.2022.118380_b0250 – volume: 14 start-page: 4254 year: 2021 ident: 10.1016/j.eswa.2022.118380_b0285 article-title: Cloud and cloud shadow segmentation for remote sensing imagery via filtered Jaccard loss function and parametric augmentation publication-title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing doi: 10.1109/JSTARS.2021.3070786 – volume: 443 start-page: 26 year: 2021 ident: 10.1016/j.eswa.2022.118380_b0155 article-title: See more than once: Kernel-sharing atrous convolution for semantic segmentation publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.02.091 – volume: 12 issue: 21 year: 2020 ident: 10.1016/j.eswa.2022.118380_b0215 article-title: Refined UNet V2: End-to-end patch-wise network for noise-free cloud and shadow segmentation publication-title: Remote Sensing doi: 10.3390/rs12213530 – volume: 2017 start-page: 2261 year: 2017 ident: 10.1016/j.eswa.2022.118380_b0150 article-title: Densely connected convolutional networks publication-title: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – year: 2015 ident: 10.1016/j.eswa.2022.118380_b0255 article-title: Fully convolutional networks for semantic segmentation – volume: 16 start-page: 248 issue: 1 year: 2022 ident: 10.1016/j.eswa.2022.118380_b0055 article-title: Forecast of rainfall distribution based on fixed sliding window long short-term memory publication-title: Engineering Applications of Computational Fluid Mechanics doi: 10.1080/19942060.2021.2009374 – ident: 10.1016/j.eswa.2022.118380_b0390 – volume: 2017 start-page: 6230 year: 2017 ident: 10.1016/j.eswa.2022.118380_b0425 article-title: Pyramid scene parsing network publication-title: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – ident: 10.1016/j.eswa.2022.118380_b0260 – volume: 39 start-page: 2481 issue: 12 year: 2017 ident: 10.1016/j.eswa.2022.118380_b0010 article-title: SegNet: A deep convolutional encoder-decoder architecture for image segmentation publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2016.2644615 – start-page: 234 year: 2015 ident: 10.1016/j.eswa.2022.118380_b0325 article-title: U-Net: Convolutional Networks for Biomedical Image Segmentation – start-page: 3 year: 2018 ident: 10.1016/j.eswa.2022.118380_b0440 article-title: UNet++: A Nested U-Net Architecture for Medical Image Segmentation – ident: 10.1016/j.eswa.2022.118380_b0455 – ident: 10.1016/j.eswa.2022.118380_b0375 – volume: 2020 start-page: 345 year: 2020 ident: 10.1016/j.eswa.2022.118380_b0235 article-title: Attention Unet++: A nested attention-aware U-Net for Liver CT image segmentation publication-title: IEEE International Conference on Image Processing (ICIP) – volume: 59 start-page: 700 issue: 1 year: 2021 ident: 10.1016/j.eswa.2022.118380_b0115 article-title: CDnetV2: CNN-based cloud detection for remote sensing imagery with cloud-snow coexistence publication-title: IEEE Transactions on Geoscience and Remote Sensing doi: 10.1109/TGRS.2020.2991398 – volume: 12 issue: 6 year: 2020 ident: 10.1016/j.eswa.2022.118380_b0120 article-title: Cloud detection for satellite imagery using attention-based U-net convolutional neural network publication-title: Symmetry doi: 10.3390/sym12061056 – volume: 11 start-page: 1 issue: 2 year: 2020 ident: 10.1016/j.eswa.2022.118380_b0045 article-title: Albumentations: Fast and flexible image augmentations publication-title: Information (Switzerland) – volume: 11 issue: 19 year: 2019 ident: 10.1016/j.eswa.2022.118380_b0090 article-title: CloudFCN: Accurate and robust cloud detection for satellite imagery with deep learning publication-title: Remote Sensing doi: 10.3390/rs11192312 – ident: 10.1016/j.eswa.2022.118380_b0350 doi: 10.1109/WACV.2017.58 – ident: 10.1016/j.eswa.2022.118380_b0140 doi: 10.1117/12.2529586 – volume: 225 start-page: 307 year: 2019 ident: 10.1016/j.eswa.2022.118380_b0050 article-title: Cloud and cloud shadow detection in Landsat imagery based on deep convolutional neural networks publication-title: Remote Sensing of Environment doi: 10.1016/j.rse.2019.03.007 – volume: 2019 start-page: 1965 year: 2019 ident: 10.1016/j.eswa.2022.118380_b0275 article-title: Cloudmaskgan: A content-aware unpaired image-to-image translation algorithm for remote sensing imagery publication-title: IEEE International Conference on Image Processing (ICIP) – volume: 10 start-page: 3631 issue: 8 year: 2017 ident: 10.1016/j.eswa.2022.118380_b0395 article-title: Multilevel cloud detection in remote sensing images based on deep learning publication-title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing doi: 10.1109/JSTARS.2017.2686488 – ident: 10.1016/j.eswa.2022.118380_b0060 – volume: 1029–1032 year: 2019 ident: 10.1016/j.eswa.2022.118380_b0280 article-title: Cloud-Net: An end-to-end cloud detection algorithm for landsat 8 imagery publication-title: International Geoscience and Remote Sensing Symposium (IGARSS) – ident: 10.1016/j.eswa.2022.118380_b0015 – volume: 118 start-page: 83 year: 2012 ident: 10.1016/j.eswa.2022.118380_b0450 article-title: Object-based cloud and cloud shadow detection in Landsat imagery publication-title: Remote Sensing of Environment doi: 10.1016/j.rse.2011.10.028 – volume: 42 start-page: 2011 issue: 8 year: 2020 ident: 10.1016/j.eswa.2022.118380_b0145 article-title: Squeeze-and-excitation networks publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2019.2913372 – volume: 10 issue: 7 year: 2018 ident: 10.1016/j.eswa.2022.118380_b0265 article-title: Multitemporal cloud masking in the google earth engine publication-title: Remote Sensing doi: 10.3390/rs10071079 – volume: 132 start-page: 48 year: 2017 ident: 10.1016/j.eswa.2022.118380_b0430 article-title: Contextually guided very-high-resolution imagery classification with semantic segments publication-title: ISPRS Journal of Photogrammetry and Remote Sensing doi: 10.1016/j.isprsjprs.2017.08.011 – year: 2018 ident: 10.1016/j.eswa.2022.118380_b0065 article-title: DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2017.2699184 – volume: 36 start-page: 193 year: 1980 ident: 10.1016/j.eswa.2022.118380_b0100 article-title: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position publication-title: Biol. Cybern. doi: 10.1007/BF00344251 – volume: 2017 start-page: 5168 year: 2017 ident: 10.1016/j.eswa.2022.118380_b0245 article-title: RefineNet: Multi-path refinement networks for high-resolution semantic segmentation publication-title: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 57 start-page: 6195 issue: 8 year: 2019 ident: 10.1016/j.eswa.2022.118380_b0410 article-title: CDnet: CNN-based cloud detection for remote sensing imagery publication-title: IEEE Transactions on Geoscience and Remote Sensing doi: 10.1109/TGRS.2019.2904868 – volume: 52 start-page: 5540 issue: 9 year: 2014 ident: 10.1016/j.eswa.2022.118380_b0420 article-title: A robust approach for object-based detection and radiometric characterization of cloud shadow using haze optimized transformation publication-title: IEEE Transactions on Geoscience and Remote Sensing doi: 10.1109/TGRS.2013.2290237 – ident: 10.1016/j.eswa.2022.118380_b0355 – year: 2018 ident: 10.1016/j.eswa.2022.118380_b0185 article-title: Averaging weights leads to wider optima and better generalization publication-title: ArXiv Preprint – volume: 201 year: 2021 ident: 10.1016/j.eswa.2022.118380_b0040 article-title: Convolutional neural networks applied to semantic segmentation of landslide scars publication-title: CATENA doi: 10.1016/j.catena.2021.105189 – start-page: 2048 year: 2015 ident: 10.1016/j.eswa.2022.118380_b0400 – ident: 10.1016/j.eswa.2022.118380_b0110 – volume: 8 start-page: 25111 year: 2020 ident: 10.1016/j.eswa.2022.118380_b0085 article-title: Spatiotemporal modeling for nonlinear distributed thermal processes based on KL decomposition, MLP and LSTM network publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2970836 – ident: 10.1016/j.eswa.2022.118380_b0270 doi: 10.1109/3DV.2016.79 – ident: 10.1016/j.eswa.2022.118380_b0030 – ident: 10.1016/j.eswa.2022.118380_b0135 – volume: 21 start-page: 1 issue: 6 year: 2021 ident: 10.1016/j.eswa.2022.118380_b0130 article-title: C-UNet: Complement UNet for remote sensing road extraction publication-title: Sensors doi: 10.3390/s21062153 – year: 2016 ident: 10.1016/j.eswa.2022.118380_b0125 article-title: Deep residual learning for image recognition – year: 2019 ident: 10.1016/j.eswa.2022.118380_b0105 article-title: Semantic segmentation of clouds in satellite imagery using deep pre-trained U-nets publication-title: Proceedings - Applied Imagery Pattern Recognition Workshop – volume: 55 start-page: 4913 issue: 9 year: 2017 ident: 10.1016/j.eswa.2022.118380_b0405 article-title: A novel Bayesian spatial-temporal random field model applied to cloud detection from remotely sensed imagery publication-title: IEEE Transactions on Geoscience and Remote Sensing doi: 10.1109/TGRS.2017.2692264 – volume: 7 start-page: 164650 year: 2019 ident: 10.1016/j.eswa.2022.118380_b0335 article-title: A survey of deep learning techniques: Application in wind and solar energy resources publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2951750 – ident: 10.1016/j.eswa.2022.118380_b0170 – volume: 89 year: 2020 ident: 10.1016/j.eswa.2022.118380_b0025 article-title: Deep learning-based appearance features extraction for automated carp species identification publication-title: Aquacultural Engineering doi: 10.1016/j.aquaeng.2020.102053 – year: 2015 ident: 10.1016/j.eswa.2022.118380_b0295 article-title: Learning deconvolution network for semantic segmentation – volume: 15 start-page: 1420 issue: 1 year: 2021 ident: 10.1016/j.eswa.2022.118380_b0005 article-title: Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques publication-title: Engineering Applications of Computational Fluid Mechanics doi: 10.1080/19942060.2021.1974093 – volume: 8 issue: 9 year: 2019 ident: 10.1016/j.eswa.2022.118380_b0435 article-title: Using vehicle synthesis generative adversarial networks to improve vehicle detection in remote sensing images publication-title: ISPRS International Journal of Geo-Information doi: 10.3390/ijgi8090390 – volume: 10 issue: 6 year: 2018 ident: 10.1016/j.eswa.2022.118380_b0460 article-title: A cloud detection method for landsat 8 images based on PCANet publication-title: Remote Sensing doi: 10.3390/rs10060877 – start-page: 1097 year: 2012 ident: 10.1016/j.eswa.2022.118380_b0225 article-title: ImageNet classification with deep convolutional neural networks publication-title: Advances in Neural Information Processing Systems – ident: 10.1016/j.eswa.2022.118380_b0380 doi: 10.1109/CVPRW50498.2020.00203 – start-page: 232 year: 2008 ident: 10.1016/j.eswa.2022.118380_b0095 article-title: A threshold selection method from gray-level histograms – volume: 229 start-page: 247 issue: August year: 2019 ident: 10.1016/j.eswa.2022.118380_b0200 article-title: A cloud detection algorithm for satellite imagery based on deep learning Remote Sensing of Environment A cloud detection algorithm for satellite imagery based on deep learning publication-title: Remote Sensing of Environment doi: 10.1016/j.rse.2019.03.039 – year: 2018 ident: 10.1016/j.eswa.2022.118380_b0385 article-title: CBAM: Convolutional block attention module – volume: 11 issue: 15 year: 2019 ident: 10.1016/j.eswa.2022.118380_b0415 article-title: Semantic segmentation of urban buildings from VHR remote sensing imagery using a deep convolutional neural network publication-title: Remote Sensing doi: 10.3390/rs11151774 – volume: 10 issue: 10 year: 2018 ident: 10.1016/j.eswa.2022.118380_b0020 article-title: Towards operational satellite-based damage-mapping using U-net convolutional network: A case study of 2011 Tohoku Earthquake-Tsunami publication-title: Remote Sensing doi: 10.3390/rs10101626 – ident: 10.1016/j.eswa.2022.118380_b0195 doi: 10.1109/CIBCB48159.2020.9277638 – ident: 10.1016/j.eswa.2022.118380_b0075 – ident: 10.1016/j.eswa.2022.118380_b0220 |
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