{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:49:21Z","timestamp":1775472561181,"version":"3.50.1"},"reference-count":82,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T00:00:00Z","timestamp":1676419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42075130"],"award-info":[{"award-number":["42075130"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Because clouds and snow block the underlying surface and interfere with the information extracted from an image, the accurate segmentation of cloud\/snow regions is essential for imagery preprocessing for remote sensing. Nearly all remote sensing images have a high resolution and contain complex and diverse content, which makes the task of cloud\/snow segmentation more difficult. A multi-branch convolutional attention network (MCANet) is suggested in this study. A double-branch structure is adopted, and the spatial information and semantic information in the image are extracted. In this way, the model\u2019s feature extraction ability is improved. Then, a fusion module is suggested to correctly fuse the feature information gathered from several branches. Finally, to address the issue of information loss in the upsampling process, a new decoder module is constructed by combining convolution with a transformer to enhance the recovery ability of image information; meanwhile, the segmentation boundary is repaired to refine the edge information. This paper conducts experiments on the high-resolution remote sensing image cloud\/snow detection dataset (CSWV), and conducts generalization experiments on two publicly available datasets (HRC_WHU and L8 SPARCS), and the self-built cloud and cloud shadow dataset. The MIOU scores on the four datasets are 92.736%, 91.649%, 80.253%, and 94.894%, respectively. The experimental findings demonstrate that whether it is for cloud\/snow detection or more complex multi-category detection tasks, the network proposed in this paper can completely restore the target details, and it provides a stronger degree of robustness and superior segmentation capabilities.<\/jats:p>","DOI":"10.3390\/rs15041055","type":"journal-article","created":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T03:37:24Z","timestamp":1676432244000},"page":"1055","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["MCANet: A Multi-Branch Network for Cloud\/Snow Segmentation in High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7181-9935","authenticated-orcid":false,"given":"Kai","family":"Hu","sequence":"first","affiliation":[{"name":"Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Enwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4681-9129","authenticated-orcid":false,"given":"Min","family":"Xia","sequence":"additional","affiliation":[{"name":"Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Liguo","family":"Weng","sequence":"additional","affiliation":[{"name":"Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3835-6075","authenticated-orcid":false,"given":"Haifeng","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Marghany, M. (2021). Nonlinear Ocean Fynamics: Synthetic Aperture Radar, Elsevier.","DOI":"10.1016\/B978-0-12-818111-9.00008-2"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Marghany, M. (2021). Advanced Algorithms for Mineral and Hydrocarbon Exploration Using Synthetic Aperture Radar, Elsevier.","DOI":"10.1016\/B978-0-12-821796-2.00005-7"},{"key":"ref_3","first-page":"79","article-title":"Hyperspectral image processing for automatic target detection applications","volume":"14","author":"Manolakis","year":"2003","journal-title":"Linc. Lab. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.2134\/agronj2010.0395","article-title":"Remote sensing leaf chlorophyll content using a visible band index","volume":"103","author":"Hunt","year":"2011","journal-title":"Agron. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"016513","DOI":"10.1117\/1.JRS.16.016513","article-title":"MLNet: Multichannel feature fusion lozenge network for land segmentation","volume":"16","author":"Gao","year":"2022","journal-title":"J. Appl. Remote Sens."},{"key":"ref_7","first-page":"102597","article-title":"SUACDNet: Attentional change detection network based on siamese U-shaped structure","volume":"105","author":"Song","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","first-page":"103206","article-title":"Attention-guided siamese networks for change detection in high resolution remote sensing images","volume":"117","author":"Yin","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1109\/LGRS.2013.2245857","article-title":"Single remote sensing image dehazing","volume":"11","author":"Long","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","unstructured":"Paltridge, G.W., and CMR, P. (1976). Radiative Processes in Meteorology and Climatology, Elsevier."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/0034-4257(89)90101-6","article-title":"Spectral signature of alpine snow cover from the Landsat Thematic Mapper","volume":"28","author":"Dozier","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"7048","DOI":"10.1080\/01431161.2019.1597310","article-title":"Deep feature learning versus shallow feature learning systems for joint use of airborne thermal hyperspectral and visible remote sensing data","volume":"40","author":"Bigdeli","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1109\/36.628794","article-title":"Spectral band selection for visible-near infrared remote sensing: Spectral-spatial resolution tradeoffs","volume":"35","author":"Price","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"685","DOI":"10.5721\/EuJRS20144739","article-title":"Coastline extraction using high resolution WorldView-2 satellite imagery","volume":"47","author":"Maglione","year":"2014","journal-title":"Eur. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"537","DOI":"10.5194\/isprsarchives-XXXIX-B1-537-2012","article-title":"Pleiades system architecture and main performances","volume":"39","author":"Gleyzes","year":"2012","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.isprsjprs.2016.12.005","article-title":"A cloud detection algorithm-generating method for remote sensing data at visible to short-wave infrared wavelengths","volume":"124","author":"Sun","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1029\/RG020i001p00067","article-title":"Optical properties of snow","volume":"20","author":"Warren","year":"1982","journal-title":"Rev. Geophys."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1175\/1520-0450(1990)029<0994:SDWMSM>2.0.CO;2","article-title":"Snow\/cloud discrimination with multispectral satellite measurements","volume":"29","author":"Allen","year":"1990","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1560\/IJPS.60.1-2.253","article-title":"Evaluation of atmospheric correction using bi-temporal hyperspectral images","volume":"60","author":"Moses","year":"2012","journal-title":"Isr. J. Plant Sci."},{"key":"ref_21","first-page":"134","article-title":"A bi-channel dynamic thershold algorithm used in automatically identifying clouds on gms-5 imagery","volume":"16","author":"Liu","year":"2005","journal-title":"J. Appl. Meteorlog. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.solener.2012.11.015","article-title":"Equipment and methodologies for cloud detection and classification: A review","volume":"95","author":"Tapakis","year":"2013","journal-title":"Sol. Energy"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.isprsjprs.2018.07.006","article-title":"Cloud\/shadow detection based on spectral indices for multi\/hyperspectral optical remote sensing imagery","volume":"144","author":"Zhai","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.05.024","article-title":"An automatic method for screening clouds and cloud shadows in optical satellite image time series in cloudy regions","volume":"214","author":"Zhu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.rse.2017.01.026","article-title":"Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery","volume":"191","author":"Li","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111884","DOI":"10.1016\/j.rse.2020.111884","article-title":"Cirrus clouds that adversely affect Landsat 8 images: What are they and how to detect them?","volume":"246","author":"Qiu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/S0034-4257(02)00034-2","article-title":"An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images","volume":"82","author":"Zhang","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4206","DOI":"10.1109\/JSTARS.2015.2438015","article-title":"Scene Learning for Cloud Detection on Remote-Sensing Images","volume":"8","author":"An","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4591","DOI":"10.1109\/TGRS.2013.2265413","article-title":"Information content of very high resolution SAR images: Study of feature extraction and imaging parameters","volume":"51","author":"Dumitru","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1109\/LGRS.2013.2287295","article-title":"Dempster\u2013Shafer fusion of multiple sparse representation and statistical property for SAR target configuration recognition","volume":"11","author":"Liu","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hu, K., Weng, C., Zhang, Y., Jin, J., and Xia, Q. (2022). An overview of underwater vision enhancement: From traditional methods to recent deep learning. J. Mar. Sci. Eng., 10.","DOI":"10.3390\/jmse10020241"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hu, K., Ding, Y., Jin, J., Weng, L., and Xia, M. (2022). Skeleton motion recognition based on multi-scale deep spatio-temporal features. Appl. Sci., 12.","DOI":"10.3390\/app12031028"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"046503","DOI":"10.1117\/1.JRS.16.046503","article-title":"Multilevel feature context semantic fusion network for cloud and cloud shadow segmentation","volume":"16","author":"Zhang","year":"2022","journal-title":"J. Appl. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Shen, X., Weng, L., Xia, M., and Lin, H. (2022). Multi-Scale Feature Aggregation Network for Semantic Segmentation of Land Cover. Remote Sens., 14.","DOI":"10.3390\/rs14236156"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hu, K., Li, M., Xia, M., and Lin, H. (2022). Multi-Scale Feature Aggregation Network for Water Area Segmentation. Remote Sens., 14.","DOI":"10.3390\/rs14010206"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5874","DOI":"10.1080\/01431161.2022.2073795","article-title":"MANet: A multi-level aggregation network for semantic segmentation of high-resolution remote sensing images","volume":"43","author":"Chen","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3155","DOI":"10.1109\/TPWRD.2021.3124528","article-title":"Parameter Identification in Power Transmission Systems Based on Graph Convolution Network","volume":"37","author":"Wang","year":"2022","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ma, Z., Xia, M., Weng, L., and Lin, H. (2023). Local Feature Search Network for Building and Water Segmentation of Remote Sensing Image. Sustainability, 15.","DOI":"10.3390\/su15043034"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5940","DOI":"10.1080\/01431161.2021.2014077","article-title":"Cloud\/shadow segmentation based on multi-level feature enhanced network for remote sensing imagery","volume":"43","author":"Miao","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). 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_42","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). 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_44","unstructured":"Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (2016). Enet: A deep neural network architecture for real-time semantic segmentation. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Yang, M., Yu, K., Zhang, C., Li, Z., and Yang, K. (2018, January 18\u201323). Denseaspp for semantic segmentation in street scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00388"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Chen, X., Chen, X., and Wang, J. (2019). Segmentation transformer: Object-contextual representations for semantic segmentation. arXiv.","DOI":"10.1007\/978-3-030-58539-6_11"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"112045","DOI":"10.1016\/j.rse.2020.112045","article-title":"Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning","volume":"250","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1109\/TGRS.2020.2991398","article-title":"CDnetV2: CNN-Based Cloud Detection for Remote Sensing Imagery With Cloud-Snow Coexistence","volume":"59","author":"Guo","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","first-page":"260","article-title":"Cloud and snow detection from remote sensing imagery based on convolutional neural network","volume":"11187","author":"Hongcai","year":"2019","journal-title":"Optoelectron. Imaging Multimed. Technol. VI"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"046512","DOI":"10.1117\/1.JRS.15.046512","article-title":"PANDA: Parallel asymmetric network with double attention for cloud and its shadow detection","volume":"15","author":"Xia","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.1080\/01431161.2020.1849852","article-title":"Cloud\/shadow segmentation based on global attention feature fusion residual network for remote sensing imagery","volume":"42","author":"Xia","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, G., Gao, X., Yang, Y., Wang, M., and Ran, S. (2021). Controllably Deep Supervision and Multi-Scale Feature Fusion Network for Cloud and Snow Detection Based on Medium-and High-Resolution Imagery Dataset. Remote Sens., 13.","DOI":"10.3390\/rs13234805"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Liao, D., Shi, C., and Wang, L. (2023). A complementary integrated Transformer network for hyperspectral image classification. CAAI Trans. Intell. Technol.","DOI":"10.1049\/cit2.12150"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Shi, C., Zhao, X., and Wang, L. (2021). A multi-branch feature fusion strategy based on an attention mechanism for remote sensing image scene classification. Remote Sens., 13.","DOI":"10.3390\/rs13101950"},{"key":"ref_55","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., and Shao, L. (2021, January 11\u201317). Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canadam.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., and Zhang, L. (2021, January 11\u201317). Cvt: Introducing convolutions to vision transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Pan, J., Bulat, A., Tan, F., Zhu, X., Dudziak, L., Li, H., Tzimiropoulos, G., and Martinez, B. (2022, January 23\u201327). Edgevits: Competing light-weight cnns on mobile devices with vision transformers. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-20083-0_18"},{"key":"ref_59","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"032609","DOI":"10.1117\/1.JRS.14.032609","article-title":"Cloud\/snow recognition of satellite cloud images based on multiscale fusion attention network","volume":"14","author":"Xia","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/JSTARS.2022.3224081","article-title":"Axial Cross Attention Meets CNN: Bibranch Fusion Network for Change Detection","volume":"16","author":"Song","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1080\/01431161.2018.1508917","article-title":"Cloud\/snow recognition for multispectral satellite imagery based on a multidimensional deep residual network","volume":"40","author":"Xia","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2417","DOI":"10.1109\/TIFS.2020.2969552","article-title":"Multi-stage feature constraints learning for age estimation","volume":"15","author":"Xia","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Cao, J., Li, Y., Sun, M., Chen, Y., Lischinski, D., Cohen-Or, D., Chen, B., and Tu, C. (2022). Do-conv: Depthwise over-parameterized convolutional layer. IEEE Trans. Image Process.","DOI":"10.1109\/TIP.2022.3175432"},{"key":"ref_65","unstructured":"Hendrycks, D., and Gimpel, K. (2016). Gaussian error linear units (gelus). arXiv."},{"key":"ref_66","unstructured":"Xia, X., Li, J., Wu, J., Wang, X., Wang, M., Xiao, X., Zheng, M., and Wang, R. (2022). TRT-ViT: TensorRT-oriented Vision Transformer. arXiv."},{"key":"ref_67","unstructured":"Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., and Han, J. (2019). On the variance of the adaptive learning rate and beyond. arXiv."},{"key":"ref_68","unstructured":"Li, Z., Shen, H., Cheng, Q., Liu, Y., You, S., and He, Z. (2018). Deep learning based cloud detection for remote sensing images by the fusion of multi-scale convolutional features. arXiv."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"4907","DOI":"10.3390\/rs6064907","article-title":"Automated detection of cloud and cloud shadow in single-date Landsat imagery using neural networks and spatial post-processing","volume":"6","author":"Hughes","year":"2014","journal-title":"Remote Sens."},{"key":"ref_70","unstructured":"Hughes, M. (2016). L8 SPARCS Cloud Validation Masks."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Li, H., Xiong, P., Fan, H., and Sun, J. (2019, January 15\u201320). Dfanet: Deep feature aggregation for real-time semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00975"},{"key":"ref_72","unstructured":"Li, G., Yun, I., Kim, J., and Kim, J. (2019). Dabnet: Depth-wise asymmetric bottleneck for real-time semantic segmentation. arXiv."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 15\u201320). Deep high-resolution representation learning for human pose estimation. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_74","unstructured":"Li, H., Xiong, P., An, J., and Wang, L. (2018). Pyramid attention network for semantic segmentation. arXiv."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1007\/s11263-021-01515-2","article-title":"Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation","volume":"129","author":"Yu","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"6149","DOI":"10.1007\/s00521-021-06802-0","article-title":"Multi-scale strip pooling feature aggregation network for cloud and cloud shadow segmentation","volume":"34","author":"Lu","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"104940","DOI":"10.1016\/j.cageo.2021.104940","article-title":"Strip pooling channel spatial attention network for the segmentation of cloud and cloud shadow","volume":"157","author":"Qu","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Hu, K., Zhang, D., and Xia, M. (2021). Cdunet: Cloud detection unet for remote sensing imagery. Remote Sens., 13.","DOI":"10.3390\/rs13224533"},{"key":"ref_79","unstructured":"Badrinarayanan, V., Handa, A., and Cipolla, R. (2015). Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling. arXiv."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Mehta, S., Rastegari, M., Shapiro, L., and Hajishirzi, H. (2019, January 15\u201320). Espnetv2: A light-weight, power efficient, and general purpose convolutional neural network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00941"},{"key":"ref_81","unstructured":"Zhang, F., Chen, Y., Li, Z., Hong, Z., Liu, J., Ma, F., Han, J., and Ding, E. (November, January 27). Acfnet: Attentional class feature network for semantic segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_82","unstructured":"Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., and Liu, W. (November, January 27). Ccnet: Criss-cross attention for semantic segmentation. Proceedings of the IEEE\/CVF international Conference on Computer Vision, Seoul, Korea."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1055\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:36:11Z","timestamp":1760121371000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1055"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,15]]},"references-count":82,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15041055"],"URL":"https:\/\/doi.org\/10.3390\/rs15041055","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,15]]}}}