{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:39:21Z","timestamp":1779295161705,"version":"3.51.4"},"reference-count":65,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Projects of High-resolution Earth Observation System","award":["30-Y20A010-9007-17\/18"],"award-info":[{"award-number":["30-Y20A010-9007-17\/18"]}]},{"name":"Major Projects of High-resolution Earth Observation System","award":["4-Y30B01-9001-18\/20"],"award-info":[{"award-number":["4-Y30B01-9001-18\/20"]}]},{"name":"13th Five-Year Advance Research Project on 498 Civil Space Technology of the National Defense Science and Technology Administration","award":["\/"],"award-info":[{"award-number":["\/"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recent developments in hyperspectral satellites have dramatically promoted the wide application of large-scale quantitative remote sensing. As an essential part of preprocessing, cloud detection is of great significance for subsequent quantitative analysis. For Gaofen-5 (GF-5) data producers, the daily cloud detection of hundreds of scenes is a challenging task. Traditional cloud detection methods cannot meet the strict demands of large-scale data production, especially for GF-5 satellites, which have massive data volumes. Deep learning technology, however, is able to perform cloud detection efficiently for massive repositories of satellite data and can even dramatically speed up processing by utilizing thumbnails. Inspired by the outstanding learning capability of convolutional neural networks (CNNs) for feature extraction, we propose a new dual-branch CNN architecture for cloud segmentation for GF-5 preview RGB images, termed a multiscale fusion gated network (MFGNet), which introduces pyramid pooling attention and spatial attention to extract both shallow and deep information. In addition, a new gated multilevel feature fusion module is also employed to fuse features at different depths and scales to generate pixelwise cloud segmentation results. The proposed model is extensively trained on hundreds of globally distributed GF-5 satellite images and compared with current mainstream CNN-based detection networks. The experimental results indicate that our proposed method has a higher F1 score (0.94) and fewer parameters (7.83 M) than the compared methods.<\/jats:p>","DOI":"10.3390\/rs12132106","type":"journal-article","created":{"date-parts":[[2020,7,2]],"date-time":"2020-07-02T02:44:25Z","timestamp":1593657865000},"page":"2106","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["An Effective Cloud Detection Method for Gaofen-5 Images via Deep Learning"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2987-0504","authenticated-orcid":false,"given":"Junchuan","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Research and Development, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yichuan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Research and Development, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangxiang","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Research and Development, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufeng","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Earth Science and Surveying Engineering, University of Mining &amp; Technology, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"He","sequence":"additional","affiliation":[{"name":"Department of Research and Development, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, Y., Li, H., Du, Y., Cao, B., Liu, Q., Sun, L., Zhu, J., and Mo, F. (2018, January 22\u201327). A temperature and emissivity separation algortihm for chinese gaofen-5 satelltie data. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium(IGARSS 2018), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517701"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.5194\/isprs-archives-XLII-3-1157-2018","article-title":"Mineral information extraction based on gaofen-5\u2032s thermal infrared data","volume":"XLII-3","author":"Liu","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yu, J.C., and Yan, B.K. (2017, January 18\u201321). Efficient solution of large-scale domestic hyperspectral data processing and geological application. Proceedings of the IEEE 2017 International Workshop on Remote Sensing with Intelligent Processing, Shanghai, China.","DOI":"10.1109\/RSIP.2017.7970774"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3826","DOI":"10.1109\/TGRS.2012.2227333","article-title":"Spatial and temporal distribution of clouds observed by modis onboard the terra and aqua satellites","volume":"51","author":"King","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.14358\/PERS.72.10.1179","article-title":"Characterization of the landsat-7 etm+ automated cloud-cover assessment (acca) algorithm","volume":"72","author":"Irish","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the fmask algorithm: Cloud, cloud shadow, and snow detection for landsats 4\u20137, 8, and sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1175\/1520-0442(1993)006<2341:CDUSMO>2.0.CO;2","article-title":"Cloud detection using satellite measurements of infrared and visible radiances for isccp","volume":"6","author":"Rossow","year":"1993","journal-title":"J. Clim."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1080\/01431168908903929","article-title":"An algorithm for snow and ice detection using avhrr data an extension to the apollo software package","volume":"10","author":"Gesell","year":"1989","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/0273-1177(91)90402-6","article-title":"Global distribution of cloud cover derived from noaa\/avhrr operational satellite data","volume":"11","author":"Stowe","year":"1991","journal-title":"Adv. Space Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.rse.2017.07.002","article-title":"Improving fmask cloud and cloud shadow detection in mountainous area for landsats 4\u20138 images","volume":"199","author":"Qiu","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2019.05.024","article-title":"Fmask 4.0: Improved cloud and cloud shadow detection in landsats 4\u20138 and sentinel-2 imagery","volume":"231","author":"Qiu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1016\/j.rse.2010.03.002","article-title":"A multi-temporal method for cloud detection, applied to formosat-2, ven\u00b5s, landsat and sentinel-2 images","volume":"114","author":"Hagolle","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2014.06.012","article-title":"Automated cloud, cloud shadow, and snow detection in multitemporal landsat data: An algorithm designed specifically for monitoring land cover change","volume":"152","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.isprsjprs.2015.05.003","article-title":"Radiometric normalization and cloud detection of optical satellite images using invariant pixels","volume":"106","author":"Lin","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1682","DOI":"10.1109\/TGRS.2002.802455","article-title":"An automated, dynamic threshold cloud-masking algorithm for daytime avhrr images over land","volume":"40","author":"Emery","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7172","DOI":"10.1002\/2015JD024722","article-title":"A universal dynamic threshold cloud detection algorithm (udtcda) supported by a prior surface reflectance database","volume":"121","author":"Sun","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4167","DOI":"10.1016\/j.rse.2008.06.010","article-title":"Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven modis land bands over canada and north america","volume":"112","author":"Luo","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.rse.2018.04.046","article-title":"Improvement of the fmask algorithm for sentinel-2 images: Separating clouds from bright surfaces based on parallax effects","volume":"215","author":"Frantz","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bian, J., Li, A., Liu, Q., and Huang, C. (2016). Cloud and snow discrimination for ccd images of HJ-1A\/B constellation based on spectral signature and spatio-temporal context. Remote Sens., 8.","DOI":"10.3390\/rs8010031"},{"key":"ref_21","first-page":"204","article-title":"Cloud detection algorithm for images of visual and infrared multispectral imager","volume":"36","author":"Ge","year":"2019","journal-title":"Aerosp. Shanghai"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1109\/LGRS.2017.2735801","article-title":"Distinguishing cloud and snow in satellite images via deep convolutional network","volume":"14","author":"Zhan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, L., Chen, Y., Tang, L., Fan, R., and Yao, Y. (2018). Object-based convolutional neural networks for cloud and snow detection in high-resolution multispectral imagers. Water, 10.","DOI":"10.3390\/w10111666"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"9113","DOI":"10.1080\/01431161.2018.1506183","article-title":"A new landsat 8 cloud discrimination algorithm using thresholding tests","volume":"39","author":"Oishi","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4062","DOI":"10.1109\/TGRS.2018.2889677","article-title":"Cloud detection in remote sensing images based on multiscale features-convolutional neural network","volume":"57","author":"Shao","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1175\/JAM2173.1","article-title":"Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system","volume":"43","author":"Hong","year":"2004","journal-title":"J. Appl. Meteorol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(95)00137-P","article-title":"Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data","volume":"54","author":"Hall","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.asr.2018.04.030","article-title":"Introducing two random forest based methods for cloud detection in remote sensing images","volume":"62","author":"Ghasemian","year":"2018","journal-title":"Adv. Space Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Egli, S., Thies, B., and Bendix, J. (2018). A hybrid approach for fog retrieval based on a combination of satellite and ground truth data. Remote Sens., 10.","DOI":"10.3390\/rs10040628"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1175\/1520-0426(2004)021<0159:CCOSRD>2.0.CO;2","article-title":"Cloud classification of satellite radiance data by multicategory support vector machines","volume":"21","author":"Lee","year":"2004","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.rse.2017.11.003","article-title":"Development of a support vector machine based cloud detection method for modis with the adjustability to various conditions","volume":"205","author":"Ishida","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.isprsjprs.2019.02.017","article-title":"Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors","volume":"150","author":"Li","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tsagkatakis, G., Aidini, A., Fotiadou, K., Giannopoulos, M., Pentari, A., and Tsakalides, P. (2019). Survey of deep-learning approaches for remote sensing observation enhancement. Sensors, 19.","DOI":"10.3390\/s19183929"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"15297","DOI":"10.1109\/ACCESS.2018.2814568","article-title":"Improving Semantic Image Segmentation With a Probabilistic Superpixel-Based Dense Conditional Random Field","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.isprsjprs.2008.12.007","article-title":"Use of markov random fields for automatic cloud\/shadow detection on high resolution optical images","volume":"64","author":"Andre","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.rse.2019.03.007","article-title":"Cloud and cloud shadow detection in landsat imagery based on deep convolutional neural networks","volume":"225","author":"Chai","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Mohajerani, S., and Saeedi, P. (2020). Cloud-net+: A cloud segmentation cnn for landsat 8 remote sensing imagery optimized with filtered jaccard loss function. arXiv.","DOI":"10.1109\/JSTARS.2021.3070786"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6195","DOI":"10.1109\/TGRS.2019.2904868","article-title":"Cdnet: Cnn-based cloud detection for remote sensing imagery","volume":"57","author":"Yang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.rse.2019.03.039","article-title":"A cloud detection algorithm for satellite imagery based on deep learning","volume":"229","author":"Jeppesen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Dr\u00f6nner, J., Korfhage, N., Egli, S., M\u00fchling, M., Thies, B., Bendix, J., Freisleben, B., and Seeger, B. (2018). Fast cloud segmentation using convolutional neural networks. Remote Sens., 10.","DOI":"10.3390\/rs10111782"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liu, H., Zeng, D., and Tian, Q. (2018, January 13\u201316). In Super-pixel cloud detection using hierarchical fusion cnn. Proceedings of the 2018 IEEE Fourth International Conference on Multimedia Big Data, Xi\u2019an, China.","DOI":"10.1109\/BigMM.2018.8499091"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Morales, G., Huam\u00e1n, S.G., and Telles, J. (2018, January 21\u201323). In Cloud detection in high-resolution multispectral satellite imagery using deep learning. Proceedings of the International Conference on Artificial Neural Networks, Kuala Lumpur, Malaysia.","DOI":"10.1007\/978-3-030-01424-7_28"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Guo, Z.S., Li, C.H., Wang, Z.M., Kwok, E., and Wei, X. (2018, January 5\u20137). A cloud boundary detection scheme combined with aslic and cnn using zy-3, gf-1\/2 satellite imagery. Proceedings of the ISPRS Technical Commission III Midterm Symposium on \u201cDevelopments, Technologies and Applications in Remote Sensing\u201d, Beijing, China.","DOI":"10.5194\/isprs-archives-XLII-3-455-2018"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zi, Y., Xie, F., and Jiang, Z. (2018). A cloud detection method for landsat 8 images based on pcanet. Remote Sens., 10.","DOI":"10.3390\/rs10060877"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chen, Y., Fan, R., Bilal, M., Yang, X., Wang, J., and Li, W. (2018). Multilevel cloud detection for high-resolution remote sensing imagery using multiple convolutional neural networks. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7050181"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3631","DOI":"10.1109\/JSTARS.2017.2686488","article-title":"Multilevel cloud detection in remote sensing images based on deep learning","volume":"10","author":"Xie","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). In U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_50","unstructured":"Badrinarayanan, V., Kendall, A., and SegNet, R.C. (2015). A deep convolutional encoder-decoder architecture for image segmentation. arXiv."},{"key":"ref_51","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Terzopoulos, D., and Myronenko, A. (2020). Edge-gated cnns for volumetric semantic segmentation of medical images. arXiv.","DOI":"10.1101\/2020.03.14.992115"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Takikawa, T., Acuna, D., Jampani, V., and Fidler, S. (2019, January 20\u201326). In Gated-scnn: Gated shape cnns for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00533"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., and Sang, N. (2018, January 8\u201314). In Bisenet: Bilateral segmentation network for real-time semantic segmentation. Proceedings of the European Conference on Computer Vision (ECCV 2018), Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). In Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). In Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). In Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhang, X., Peng, C., Xue, X., and Sun, J. (2018, January 8\u201314). In Exfuse: Enhancing feature fusion for semantic segmentation. Proceedings of the European Conference on Computer Vision (ECCV 2018), Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_17"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201322). In Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (2019, January 20\u201326). In Searching for mobilenetv3. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). In Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). In Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV 2018), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_63","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_64","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). In Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Sardinia, Italy."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/13\/2106\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:45:45Z","timestamp":1760175945000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/13\/2106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,1]]},"references-count":65,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["rs12132106"],"URL":"https:\/\/doi.org\/10.3390\/rs12132106","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,1]]}}}