{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T03:52:13Z","timestamp":1784001133523,"version":"3.55.0"},"reference-count":80,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T00:00:00Z","timestamp":1708473600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T00:00:00Z","timestamp":1708473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42075130"],"award-info":[{"award-number":["42075130"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42275156"],"award-info":[{"award-number":["42275156"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s00521-024-09477-5","type":"journal-article","created":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T14:02:34Z","timestamp":1708524154000},"page":"7779-7798","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Cross-dimensional feature attention aggregation network for cloud and snow recognition of high satellite images"],"prefix":"10.1007","volume":"36","author":[{"given":"Kai","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enwei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4681-9129","authenticated-orcid":false,"given":"Min","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiqin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoling","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haifeng","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,21]]},"reference":[{"key":"9477_CR1","doi-asserted-by":"crossref","unstructured":"Chen X, Liang S, Cao Y, He T, Wang D (2015) Observed contrast changes in snow cover phenology in northern middle and high latitudes from 2001\u20132014. Sci Rep 5(1):1\u20139","DOI":"10.1038\/srep16820"},{"key":"9477_CR2","doi-asserted-by":"crossref","unstructured":"Miao S, Xia M, Qian M, Zhang Y, Liu J, Lin H (2022) Cloud\/shadow segmentation based on multi-level feature enhanced network for remote sensing imagery. Int J Remote Sens 43(15\u201316):5940\u20135960","DOI":"10.1080\/01431161.2021.2014077"},{"key":"9477_CR3","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1201\/9781315166636-1","volume-title":"Remote sensing time series image processing","author":"Z Zhu","year":"2018","unstructured":"Zhu Z, Qiu S, He B, Deng C (2018) Cloud and cloud shadow detection for Landsat images: the fundamental basis for analyzing Landsat time series. In: Weng Q (ed) Remote sensing time series image processing. CRC Press, Boca Raton, pp 3\u201323"},{"key":"9477_CR4","unstructured":"Paltridge GW, Platt CMR (1976) Radiative processes in meteorology and climatology. Elsevier Scientific Pub. C., Amsterdam"},{"key":"9477_CR5","doi-asserted-by":"crossref","unstructured":"Chen L, Xia M, Qian M, Chen B (2022) Dual-branch network for cloud and cloud shadow segmentation. IEEE Trans Geosci Remote Sens 60:5410012","DOI":"10.1109\/TGRS.2022.3175613"},{"key":"9477_CR6","doi-asserted-by":"crossref","unstructured":"Song L, Xia M, Jin J, Qian M, Zhang Y (2021) SUACDNet: attentional change detection network based on Siamese u-shaped structure. Int J Appl Earth Obs Geoinf 105:102597","DOI":"10.1016\/j.jag.2021.102597"},{"key":"9477_CR7","doi-asserted-by":"crossref","unstructured":"Junchang J, Roy DP (2008) The availability of cloud-free Landsat ETM+ data over the conterminous united states and globally. Remote Sens Environ 112(3):1196\u20131211","DOI":"10.1016\/j.rse.2007.08.011"},{"key":"9477_CR8","doi-asserted-by":"crossref","unstructured":"Dozier J (1989) Spectral signature of alpine snow cover from the Landsat thematic mapper. Remote Sens Environ 28:9\u201322","DOI":"10.1016\/0034-4257(89)90101-6"},{"key":"9477_CR9","doi-asserted-by":"crossref","unstructured":"Roy DP, Junchang J, Kline K, Scaramuzza PL, Kovalskyy V, Hansen M, Loveland TR, Vermote E, Zhang C (2010) Web-enabled Landsat data (weld): Landsat ETM+ composited mosaics of the conterminous United States. Remote Sens Environ 114(1):35\u201349","DOI":"10.1016\/j.rse.2009.08.011"},{"key":"9477_CR10","doi-asserted-by":"crossref","unstructured":"Huete A, Didan K, Tomoaki ME, Rodriguez P, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83(1\u20132):195\u2013213","DOI":"10.1016\/S0034-4257(02)00096-2"},{"issue":"2\u20133","key":"9477_CR11","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/S0034-4257(02)00034-2","volume":"82","author":"Y Zhang","year":"2002","unstructured":"Zhang Y, Guindon B, Cihlar J (2002) An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images. Remote Sens Environ 82(2\u20133):173\u2013187","journal-title":"Remote Sens Environ"},{"key":"9477_CR12","doi-asserted-by":"crossref","unstructured":"Weng L, Pang K, Xia M, Lin H, Qian M, Zhu C (2023) Sgformer: a local and global features coupling network for semantic segmentation of land cover. IEEE J Sel Top Appl Earth Obs Remote Sens 16:6812\u20136824","DOI":"10.1109\/JSTARS.2023.3295729"},{"key":"9477_CR13","doi-asserted-by":"crossref","unstructured":"Zhu Z, Woodcock CE (2014) Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: an algorithm designed specifically for monitoring land cover change. Remote Sens Environ 152:217\u2013234","DOI":"10.1016\/j.rse.2014.06.012"},{"key":"9477_CR14","doi-asserted-by":"crossref","unstructured":"Xie F, Shi M, Shi Z, Yin J, Zhao D (2017) Multilevel cloud detection in remote sensing images based on deep learning. IEEE J Sel Top Appl Earth Obs Remote Sens 10(8):3631\u20133640","DOI":"10.1109\/JSTARS.2017.2686488"},{"key":"9477_CR15","doi-asserted-by":"crossref","unstructured":"Zhang C, Weng L, Ding L, Xia M, Lin H (2023) CRSNet: cloud and cloud shadow refinement segmentation networks for remote sensing imagery. Remote Sens 15(6):96","DOI":"10.3390\/rs15061664"},{"key":"9477_CR16","doi-asserted-by":"crossref","unstructured":"Ji H, Xia M, Zhang D, Lin H (2023) Multi-supervised feature fusion attention network for clouds and shadows detection. ISPRS Int J Geo-Inf 12(6):247","DOI":"10.3390\/ijgi12060247"},{"key":"9477_CR17","doi-asserted-by":"crossref","unstructured":"Braaten JD, Cohen WB, Yang Z (2015) Automated cloud and cloud shadow identification in Landsat MSS imagery for temperate ecosystems. Remote Sens Environ 169:128\u2013138","DOI":"10.1016\/j.rse.2015.08.006"},{"key":"9477_CR18","doi-asserted-by":"crossref","unstructured":"Li Z, Shen H, Li H, Xia G, Gamba P, Zhang L (2017) Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery. Remote Sens Environ 191:342\u2013358","DOI":"10.1016\/j.rse.2017.01.026"},{"key":"9477_CR19","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1016\/j.solener.2012.11.015","volume":"95","author":"R Tapakis","year":"2013","unstructured":"Tapakis R, Charalambides AG (2013) Equipment and methodologies for cloud detection and classification: a review. Solar Energy 95:392\u2013430","journal-title":"Solar Energy"},{"key":"9477_CR20","doi-asserted-by":"crossref","unstructured":"Ping BS, Yunshan FM (2020) A cloud and cloud shadow detection method based on fuzzy c-means algorithm. IEEE J Sel Top Appl Earth Obs Remote Sens 13:1714\u20131727","DOI":"10.1109\/JSTARS.2020.2987844"},{"key":"9477_CR21","doi-asserted-by":"crossref","unstructured":"An Z, Shi Z (2015) Scene learning for cloud detection on remote-sensing images. IEEE J Sel Top Appl Earth Obs Remote Sens 8:4206\u20134222","DOI":"10.1109\/JSTARS.2015.2438015"},{"key":"9477_CR22","volume":"9","author":"Z Fang","year":"2021","unstructured":"Fang Z, Ji W, Wang X, Li L, Li Y (2021) Automatic cloud and snow detection for GF-1 and PRSS-1 remote sensing images. J Appl Remote Sens 9:024516","journal-title":"J Appl Remote Sens"},{"issue":"8","key":"9477_CR23","doi-asserted-by":"publisher","first-page":"4591","DOI":"10.1109\/TGRS.2013.2265413","volume":"51","author":"OD Corneliu","year":"2013","unstructured":"Corneliu OD, Mihai D (2013) Information content of very high resolution SAR images: study of feature extraction and imaging parameters. IEEE Trans Geosci Remote Sens 51(8):4591\u20134610","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"9477_CR24","doi-asserted-by":"crossref","unstructured":"Geng J, Fan J, Wang H, Ma X, Li B, Chen F (2015) High-resolution SAR image classification via deep convolutional autoencoders. IEEE Geosci Remote Sens Lett 12(11):2351\u20132355","DOI":"10.1109\/LGRS.2015.2478256"},{"key":"9477_CR25","doi-asserted-by":"crossref","unstructured":"Liu M, Yan W, Zhao W, Zhang Q, Li M, Liao G (2013) Dempster\u2013Shafer fusion of multiple sparse representation and statistical property for SAR target configuration recognition. IEEE Geosci Remote Sens Lett 11(6):1106\u20131110","DOI":"10.1109\/LGRS.2013.2287295"},{"issue":"18","key":"9477_CR26","doi-asserted-by":"publisher","first-page":"3855","DOI":"10.1080\/01431160010006926","volume":"22","author":"Gregory P Asner","year":"2001","unstructured":"Asner Gregory P (2001) Cloud cover in Landsat observations of the Brazilian amazon. Int J Remote Sens 22(18):3855\u20133862","journal-title":"Int J Remote Sens"},{"key":"9477_CR27","doi-asserted-by":"crossref","unstructured":"Bossu J, Hautiere N, Tarel J-P (2011) Rain or snow detection in image sequences through use of a histogram of orientation of streaks. Int J Comput Vis 93(3):348\u2013367","DOI":"10.1007\/s11263-011-0421-7"},{"key":"9477_CR28","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","volume":"118","author":"Z Zhu","year":"2012","unstructured":"Zhu Z, Woodcock CE (2012) Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens Environ 118:83\u201394","journal-title":"Remote Sens Environ"},{"issue":"6","key":"9477_CR29","doi-asserted-by":"publisher","first-page":"4907","DOI":"10.3390\/rs6064907","volume":"6","author":"HM Joseph","year":"2014","unstructured":"Joseph HM, Hayes DJ (2014) Automated detection of cloud and cloud shadow in single-date Landsat imagery using neural networks and spatial post-processing. Remote Sens 6(6):4907\u20134926","journal-title":"Remote Sens"},{"key":"9477_CR30","doi-asserted-by":"crossref","unstructured":"Zhu Z, Wang S, Woodcock CE (2015) Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsat\u2019s 4\u20137, 8, and sentinel 2 images. Remote Sens Environ 159:269\u2013277","DOI":"10.1016\/j.rse.2014.12.014"},{"key":"9477_CR31","doi-asserted-by":"crossref","unstructured":"Le Goff M , Tourneret J-Y, H Wendt, M Ortner, M Spigai (2017) Deep learning for cloud detection. In: 8th International conference of pattern recognition systems (ICPRS 2017). IET, pp 1\u20136","DOI":"10.1049\/cp.2017.0139"},{"key":"9477_CR32","volume":"113","author":"Q Chen","year":"2022","unstructured":"Chen Q, Zhang Z, Chen S, Wen S, Ma H, Zhihua X (2022) A self-attention based global feature enhancing network for semantic segmentation of large-scale urban street-level point clouds. Int J Appl Earth Obs Geoinf 113:102974","journal-title":"Int J Appl Earth Obs Geoinf"},{"issue":"1","key":"9477_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JRS.16.016513","volume":"16","author":"J Gao","year":"2022","unstructured":"Gao J, Weng L, Xia M, Lin H (2022) MLNet: multichannel feature fusion lozenge network for land segmentation. J Appl Remote Sens 16(1):1\u201319","journal-title":"J Appl Remote Sens"},{"key":"9477_CR34","volume":"113","author":"X Jiang","year":"2022","unstructured":"Jiang X, Li Y, Jiang T, Xie J, Yilong W, Cai Q, Jiang J, Jiaming X, Zhang H (2022) Roadformer: pyramidal deformable vision transformers for road network extraction with remote sensing images. Int J Appl Earth Obs Geoinf 113:102987","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"9477_CR35","first-page":"32","volume":"16","author":"L Song","year":"2023","unstructured":"Song L, Xia M, Weng L, Lin H, Qian M, Chen B (2023) Axial cross attention meets CNN: bi-branch fusion network for change detection. IEEE J Sel Top Appl Earth Obs Remote Sen 16:32\u201343","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sen"},{"key":"9477_CR36","volume":"103","author":"D Peng","year":"2021","unstructured":"Peng D, Bruzzone L, Zhang Y, Guan H, He P (2021) SCDNet: a novel convolutional network for semantic change detection in high resolution optical remote sensing imagery. Int J Appl Earth Obs Geoinf 103:102465","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"9477_CR37","doi-asserted-by":"crossref","unstructured":"Gao W, Li X, Han Y, Liu Y (2022) Multi-scale vertical cross-layer feature aggregation and attention fusion network for object detection. In: International conference on artificial neural networks. Springer, pp 139\u2013150","DOI":"10.1007\/978-3-031-15937-4_12"},{"key":"9477_CR38","doi-asserted-by":"publisher","first-page":"4005","DOI":"10.3390\/rs15164005","volume":"15","author":"X Dai","year":"2023","unstructured":"Dai X, Chen K, Xia M, Weng L, Lin H (2023) LPMSNet: location pooling multi-scale network for cloud and cloud shadow segmentation. Remote Sens 15:4005","journal-title":"Remote Sens"},{"key":"9477_CR39","doi-asserted-by":"crossref","unstructured":"Ma Z, Xia M, Weng L, Lin H (2023) Local feature search network for building and water segmentation of remote sensing image. Sustainability 15(4):3034","DOI":"10.3390\/su15043034"},{"key":"9477_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106196","volume":"123","author":"H Kai","year":"2023","unstructured":"Kai H, Weng C, Shen C, Wang T, Weng L, Xia M (2023) A multi-stage underwater image aesthetic enhancement algorithm based on a generative adversarial network. Eng Appl Artif Intell 123:106196","journal-title":"Eng Appl Artif Intell"},{"key":"9477_CR41","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"9477_CR42","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"9477_CR43","unstructured":"Badrinarayanan V, Handa A, Cipolla R (2015) SEGNet: a deep convolutional encoder\u2013decoder architecture for robust semantic pixel-wise labelling. Comput Sci"},{"key":"9477_CR44","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, pp 2881\u20132890","DOI":"10.1109\/CVPR.2017.660"},{"key":"9477_CR45","unstructured":"Li H, Xiong P, An J, Wang L (2018) Pyramid attention network for semantic segmentation. arXiv preprint arXiv:1805.10180"},{"key":"9477_CR46","doi-asserted-by":"crossref","unstructured":"Sun K, Xiao B, Liu D, Wang J (2019) Deep high-resolution representation learning for human pose estimation. arXiv e-prints,","DOI":"10.1109\/CVPR.2019.00584"},{"key":"9477_CR47","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.rse.2019.03.007","volume":"225","author":"D Chai","year":"2019","unstructured":"Chai D, Newsam S, Zhang HK, Qiu Y, Huang J (2019) Cloud and cloud shadow detection in Landsat imagery based on deep convolutional neural networks. Remote Sens Environ 225:307\u2013316","journal-title":"Remote Sens Environ"},{"issue":"23","key":"9477_CR48","doi-asserted-by":"publisher","first-page":"4805","DOI":"10.3390\/rs13234805","volume":"13","author":"G Zhang","year":"2021","unstructured":"Zhang G, Gao X, Yang Y, Wang M, 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(23):4805","journal-title":"Remote Sens"},{"key":"9477_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2020.112045","volume":"250","author":"Y Li","year":"2020","unstructured":"Li Y, Chen W, Zhang Y, Tao C, Xiao R, Tan Y (2020) Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning. Remote Sens Environ 250:112045","journal-title":"Remote Sens Environ"},{"key":"9477_CR50","unstructured":"Hongcai D, Li K, Guo J, Zhang J, Yang J (2019) Cloud and snow detection from remote sensing imagery based on convolutional neural network. In: Optoelectronic imaging and multimedia technology VI"},{"key":"9477_CR51","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.isprsjprs.2021.01.023","volume":"174","author":"W Xi","year":"2021","unstructured":"Xi W, Shi Z, Zou Z (2021) A geographic information-driven method and a new large scale dataset for remote sensing cloud\/snow detection. ISPRS J Photogramm Remote Sens 174:87\u2013104","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"9477_CR52","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.isprsjprs.2019.02.017","volume":"150","author":"Z Li","year":"2019","unstructured":"Li Z, Shen H, Cheng Q, Liu Y, You S, He Z (2019) Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors. ISPRS J Photogramm Remote Sens 150:197\u2013212","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"9477_CR53","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778,","DOI":"10.1109\/CVPR.2016.90"},{"key":"9477_CR54","unstructured":"Li Z, Shen H, Cheng Q, Liu Y, You S, He Z (2018) Deep learning based cloud detection for remote sensing images by the fusion of multi-scale convolutional features. arXiv preprint arXiv:1810.05801,"},{"issue":"4","key":"9477_CR55","doi-asserted-by":"publisher","first-page":"3155","DOI":"10.1109\/TPWRD.2021.3124528","volume":"37","author":"Z Wang","year":"2022","unstructured":"Wang Z, Xia M, Min L, Pan L, Liu J (2022) Parameter identification in power transmission systems based on graph convolution network. IEEE Trans Power Deliv 37(4):3155\u20133163","journal-title":"IEEE Trans Power Deliv"},{"key":"9477_CR56","unstructured":"Howard A, Zhmoginov A, Chen L-C, Sandler M, Menglong Z (2018) Mobile networks for classification, detection and segmentation, inverted residuals and linear bottlenecks"},{"key":"9477_CR57","doi-asserted-by":"publisher","first-page":"3837","DOI":"10.1007\/s00371-022-02519-w","volume":"39","author":"A Liu","year":"2022","unstructured":"Liu A, Li S, Chang Y (2022) Cross-resolution feature attention network for image super-resolution. Vis Comput 39(9):3837\u20133849","journal-title":"Vis Comput"},{"key":"9477_CR58","unstructured":"Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122,"},{"issue":"15\u201316","key":"9477_CR59","doi-asserted-by":"publisher","first-page":"5874","DOI":"10.1080\/01431161.2022.2073795","volume":"43","author":"B Chen","year":"2022","unstructured":"Chen B, Xia M, Qian M, Huang J (2022) MANet: a multi-level aggregation network for semantic segmentation of high-resolution remote sensing images. Int J Remote Sens 43(15\u201316):5874\u20135894","journal-title":"Int J Remote Sens"},{"key":"9477_CR60","doi-asserted-by":"crossref","unstructured":"Dai Y, Gieseke F, Oehmcke S, Wu Y, Barnard K (2021) Attentional feature fusion. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 3560\u20133569","DOI":"10.1109\/WACV48630.2021.00360"},{"key":"9477_CR61","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"9477_CR62","doi-asserted-by":"crossref","unstructured":"Li H, Xiong P, Fan H, Sun J (2019) DFANet: deep feature aggregation for real-time semantic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9522\u20139531","DOI":"10.1109\/CVPR.2019.00975"},{"key":"9477_CR63","doi-asserted-by":"crossref","unstructured":"Yang M, Kun Y, Zhang C, Li Z, Yang K (2018) Denseaspp for semantic segmentation in street scenes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3684\u20133692","DOI":"10.1109\/CVPR.2018.00388"},{"key":"9477_CR64","unstructured":"Haiping W, Bin X, Noel C, Mengchen L, Xiyang D, Lu Y, Lei Z (2021) Cvt: introducing convolutions to vision transformers. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 22\u201331"},{"issue":"8","key":"9477_CR65","doi-asserted-by":"publisher","first-page":"6149","DOI":"10.1007\/s00521-021-06802-0","volume":"34","author":"L Chen","year":"2022","unstructured":"Chen L, Xia M, Lin H (2022) Multi-scale strip pooling feature aggregation network for cloud and cloud shadow segmentation. Neural Comput Appl 34(8):6149\u20136162","journal-title":"Neural Comput Appl"},{"key":"9477_CR66","unstructured":"Paszke A, Chaurasia A, Kim S, Culurciello E (2016) ENet: A deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv:1606.02147,"},{"issue":"4","key":"9477_CR67","doi-asserted-by":"publisher","DOI":"10.1117\/1.JRS.15.046512","volume":"15","author":"M Xia","year":"2021","unstructured":"Xia M, Yi Q, Lin H (2021) PANDA: parallel asymmetric network with double attention for cloud and its shadow detection. J Appl Remote Sens 15(4):046512","journal-title":"J Appl Remote Sens"},{"key":"9477_CR68","unstructured":"Li G, Yun I, Kim J, Kim J (2019) DABNet: depth-wise asymmetric bottleneck for real-time semantic segmentation. arXiv preprint arXiv:1907.11357,"},{"key":"9477_CR69","doi-asserted-by":"crossref","unstructured":"Mehta S, Rastegari M, Shapiro L, Hajishirzi H (2019) Espnetv2: a light-weight, power efficient, and general purpose convolutional neural network, pp 9190\u20139200","DOI":"10.1109\/CVPR.2019.00941"},{"key":"9477_CR70","doi-asserted-by":"crossref","unstructured":"Huang Z, Wang X, Huang L, Huang C, Wei Y, Liu, W (2019) CCNet: Criss-cross attention for semantic segmentation. In: International conference on computer vision","DOI":"10.1109\/ICCV.2019.00069"},{"key":"9477_CR71","doi-asserted-by":"crossref","unstructured":"Wang W, Xie E, Li X, Fan DP, Shao L (2021) Pyramid vision transformer: a versatile backbone for dense prediction without convolutions, pp 568\u2013578","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"9477_CR72","unstructured":"Hong Y, Pan H, Sun W, Member S, Jia Y (2021) Deep dual-resolution networks for real-time and accurate semantic segmentation of road scenes. arXiv preprint arXiv:2101.06085,"},{"issue":"11","key":"9477_CR73","doi-asserted-by":"publisher","first-page":"3051","DOI":"10.1007\/s11263-021-01515-2","volume":"129","author":"C Yu","year":"2021","unstructured":"Yu C, Gao C, Wang J, Yu G, Shen C, Sang N (2021) Bisenet v2: bilateral network with guided aggregation for real-time semantic segmentation. Int J Comput Vis 129(11):3051\u20133068","journal-title":"Int J Comput Vis"},{"key":"9477_CR74","doi-asserted-by":"crossref","unstructured":"Zhang F, Chen Y, Li Z, Hong Z, Ding E (2019) ACFnet: attentional class feature network for semantic segmentation. IEEE","DOI":"10.1109\/ICCV.2019.00690"},{"key":"9477_CR75","doi-asserted-by":"crossref","unstructured":"Yuan Y, Chen X, Chen X, Wang J (2019) Segmentation transformer: object-contextual representations for semantic segmentation. arXiv preprint arXiv:1909.11065","DOI":"10.1007\/978-3-030-58539-6_11"},{"key":"9477_CR76","doi-asserted-by":"crossref","unstructured":"Yu C, Wang J, Peng C, Gao C, Yu G, Sang N (2018) Learning a discriminative feature network for semantic segmentation. IEEE","DOI":"10.1109\/CVPR.2018.00199"},{"key":"9477_CR77","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Encoder\u2013decoder with atrous separable convolution for semantic image segmentation","author":"LC Chen","year":"2018","unstructured":"Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder\u2013decoder with atrous separable convolution for semantic image segmentation. Springer, Cham"},{"key":"9477_CR78","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2021.104940","volume":"157","author":"Qu Yi","year":"2021","unstructured":"Yi Qu, Xia Min, Zhang Yonghong (2021) Strip pooling channel spatial attention network for the segmentation of cloud and cloud shadow. Comput Geosci 157:104940","journal-title":"Comput Geosci"},{"key":"9477_CR79","doi-asserted-by":"publisher","first-page":"731","DOI":"10.3390\/rs13040731","volume":"13","author":"Bingyu Chen","year":"2021","unstructured":"Chen Bingyu, Xia Min, Huang Junqing (2021) CDUNet: cloud detection Unet for remote sensing imagery. Remote Sens 13:731","journal-title":"Remote Sens"},{"issue":"6","key":"9477_CR80","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.1080\/01431161.2020.1849852","volume":"42","author":"Min Xia","year":"2021","unstructured":"Xia Min, Wang Tao, Zhang Yonghong, Liu Jia, Yiqing Xu (2021) Cloud\/shadow segmentation based on global attention feature fusion residual network for remote sensing imagery. Int J Remote Sens 42(6):2022\u20132045","journal-title":"Int J Remote Sens"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09477-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09477-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09477-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,13]],"date-time":"2024-04-13T20:13:42Z","timestamp":1713039222000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09477-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,21]]},"references-count":80,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["9477"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09477-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,21]]},"assertion":[{"value":"25 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}