{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T15:36:43Z","timestamp":1760369803104,"version":"3.37.3"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T00:00:00Z","timestamp":1646524800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T00:00:00Z","timestamp":1646524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100013105","name":"Shanghai Rising-Star Program","doi-asserted-by":"crossref","award":["21QA1400100"],"award-info":[{"award-number":["21QA1400100"]}],"id":[{"id":"10.13039\/501100013105","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"publisher","award":["20ZR1400800"],"award-info":[{"award-number":["20ZR1400800"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Sailing Program","award":["20YF1401600"],"award-info":[{"award-number":["20YF1401600"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2232020D-47"],"award-info":[{"award-number":["2232020D-47"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s00521-022-06989-w","type":"journal-article","created":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T15:02:11Z","timestamp":1646578931000},"page":"10039-10049","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Dual-stream shadow detection network:\u00a0biologically inspired shadow detection for remote sensing images"],"prefix":"10.1007","volume":"34","author":[{"given":"Dawei","family":"Li","sequence":"first","affiliation":[]},{"given":"Sifan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Shiyu","family":"Xiang","sequence":"additional","affiliation":[]},{"given":"Jinsheng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yanping","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Xue-Song","family":"Tang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,6]]},"reference":[{"issue":"4","key":"6989_CR1","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1109\/LGRS.2017.2650996","volume":"14","author":"Y Mostafa","year":"2017","unstructured":"Mostafa Y, Abdelhafiz A (2017) Accurate shadow detection from high-resolution satellite images. IEEE Geosci Remote Sens Lett 14(4):494\u2013498. https:\/\/doi.org\/10.1109\/LGRS.2017.2650996","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"3","key":"6989_CR2","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1109\/LGRS.2014.2345777","volume":"12","author":"J Hu","year":"2015","unstructured":"Hu J, Song Y, Jin T, Lu B, Zhu G, Zhou Z (2015) Shadow effect mitigation in indication of moving human behind wall via MIMO TWIR. IEEE Geosci Remote Sens Lett 12(3):453\u2013457. https:\/\/doi.org\/10.1109\/LGRS.2014.2345777","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"6989_CR3","doi-asserted-by":"crossref","unstructured":"Sirmacek B, Unsalan C (2009) Damaged building detection in aerial images using shadow information. In: International conference on recent advances in space technologies (RAST '09)","DOI":"10.1109\/RAST.2009.5158206"},{"key":"6989_CR4","doi-asserted-by":"publisher","first-page":"169","DOI":"10.14358\/PERS.71.2.169","volume":"71","author":"PM Dare","year":"2005","unstructured":"Dare PM (2005) Shadow analysis in high-resolution satellite imagery of urban areas. Photogramm Eng Remote Sens 71:169\u2013177","journal-title":"Photogramm Eng Remote Sens"},{"key":"6989_CR5","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.isprsjprs.2019.02.019","volume":"151","author":"J Huang","year":"2019","unstructured":"Huang J, Zhang X, Xin Q, Sun Y, Zhang P (2019) Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network. ISPRS J Photogramm Remote Sens 151:91\u2013105","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"6989_CR6","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/j.isprsjprs.2016.07.006","volume":"119","author":"G Liasis","year":"2016","unstructured":"Liasis G, Stavrou S (2016) Satellite images analysis for shadow detection and building height estimation. ISPRS J Photogramm Remote Sens 119:437\u2013450","journal-title":"ISPRS J Photogramm Remote Sens"},{"issue":"6","key":"6989_CR7","doi-asserted-by":"publisher","first-page":"1661","DOI":"10.1109\/TGRS.2006.869980","volume":"44","author":"VJD Tsai","year":"2006","unstructured":"Tsai VJD (2006) A comparative study on shadow compensation of color aerial images in invariant color models. IEEE Trans Geosci Remote Sens 44(6):1661\u20131671","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"6989_CR8","doi-asserted-by":"crossref","unstructured":"Yamazaki F, Liu W, Takasaki M (2010) Characteristics of shadow and removal of its effects for remote sensing imagery. In: IEEE geoscience and remote sensing symposium","DOI":"10.1109\/IGARSS.2009.5417404"},{"issue":"6","key":"6989_CR9","doi-asserted-by":"publisher","first-page":"2049","DOI":"10.1109\/TGRS.2010.2096515","volume":"49","author":"A Makarau","year":"2011","unstructured":"Makarau A, Richter R, Muller R et al (2011) Adaptive shadow detection using a blackbody radiator model. IEEE Trans Geosci Remote Sens 49(6):2049\u20132059","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"9","key":"6989_CR10","doi-asserted-by":"publisher","first-page":"5374","DOI":"10.1109\/TGRS.2013.2288500","volume":"52","author":"MI Elbakary","year":"2014","unstructured":"Elbakary MI, Iftekharuddin KM (2014) Shadow detection of man-made buildings in high-resolution panchromatic satellite images. IEEE Trans Geosci Remote Sens 52(9):5374\u20135386","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"6989_CR11","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/j.isprsjprs.2016.07.006","volume":"119","author":"G Liasis","year":"2016","unstructured":"Liasis G, Stavrou S (2016) Satellite images analysis for shadow detection and building height estimation. ISPRS J Photogramm Remote Sens 119:437\u2013450","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"6989_CR12","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.isprsjprs.2013.01.012","volume":"79","author":"X Tong","year":"2013","unstructured":"Tong X, Lin X, Feng T et al (2013) Use of shadows for detection of earthquake-induced collapsed buildings in high-resolution satellite imagery. ISPRS J Photogramm Remote Sens 79:53\u201367","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"6989_CR13","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1016\/j.isprsjprs.2016.05.004","volume":"119","author":"A Movia","year":"2016","unstructured":"Movia A, Beinat A, Crosilla F (2016) Shadow detection and removal in RGB VHR images for land use unsupervised classification. ISPRS J Photogramm Remote Sens 119:485\u2013495","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"6989_CR14","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"},{"key":"6989_CR15","doi-asserted-by":"crossref","unstructured":"Morales G, Arteaga D, Samuel G et al (2018) Shadow detection in high-resolution multispectral satellite imagery using generative adversarial networks. In: IEEE international conference on electronics, electrical engineering and computing","DOI":"10.1109\/INTERCON.2018.8526416"},{"issue":"5","key":"6989_CR16","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","volume":"14","author":"N Kussul","year":"2017","unstructured":"Kussul N, Lavreniuk M, Skakun S, Shelestov A (2017) Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci Remote Sens Lett 14(5):778\u2013782. https:\/\/doi.org\/10.1109\/LGRS.2017.2681128","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"10","key":"6989_CR17","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.1109\/LGRS.2019.2953497","volume":"17","author":"C Qiu","year":"2020","unstructured":"Qiu C, Mou L, Schmitt M, Zhu XX (2020) Fusing multiseasonal sentinel-2 imagery for urban land cover classification with multibranch residual convolutional neural networks. IEEE Geosci Remote Sens Lett 17(10):1787\u20131791. https:\/\/doi.org\/10.1109\/LGRS.2019.2953497","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"10","key":"6989_CR18","doi-asserted-by":"publisher","first-page":"1600","DOI":"10.1109\/LGRS.2018.2846802","volume":"15","author":"Z Yan","year":"2018","unstructured":"Yan Z et al (2018) Cloud and cloud shadow detection using multilevel feature fused segmentation network. IEEE Geosci Remote Sens Lett 15(10):1600\u20131604. https:\/\/doi.org\/10.1109\/LGRS.2018.2846802","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"6989_CR19","doi-asserted-by":"publisher","unstructured":"Wang T, Hu X, Wang Q, Heng P-A, Fu C-W (2020) Instance shadow detection. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 1877\u20131886, https:\/\/doi.org\/10.1109\/CVPR42600.2020.00195","DOI":"10.1109\/CVPR42600.2020.00195"},{"issue":"11","key":"6989_CR20","doi-asserted-by":"publisher","first-page":"2795","DOI":"10.1109\/TPAMI.2019.2919616","volume":"42","author":"X Hu","year":"2020","unstructured":"Hu X, Fu C-W, Zhu L, Qin J, Heng P-A (2020) Direction-aware spatial context features for shadow detection and removal. IEEE Trans Pattern Anal Mach Intell 42(11):2795\u20132808. https:\/\/doi.org\/10.1109\/TPAMI.2019.2919616","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6989_CR21","doi-asserted-by":"publisher","first-page":"4187","DOI":"10.1109\/TCSVT.2020.3047977","volume":"31","author":"N Inoue","year":"2020","unstructured":"Inoue N, Yamasaki T (2020) Learning from synthetic shadows for shadow detection and removal. IEEE Trans Circ Syst Video Technol 31:4187\u20134197. https:\/\/doi.org\/10.1109\/TCSVT.2020.3047977","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"6989_CR22","doi-asserted-by":"publisher","unstructured":"Chen Z, Zhu L, Wan L, Wang S, Feng W, Heng P-A (2020) A multi-task mean teacher for semi-supervised shadow detection. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 5610\u20135619. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00565","DOI":"10.1109\/CVPR42600.2020.00565"},{"key":"6989_CR23","doi-asserted-by":"crossref","unstructured":"Cun X, Pun CM, Shi C (2020) Towards ghost-free shadow removal via dual hierarchical aggregation network and shadow matting GAN. In: AAAI conference on artificial intelligence, pp 10680\u201310687","DOI":"10.1609\/aaai.v34i07.6695"},{"key":"6989_CR24","doi-asserted-by":"publisher","unstructured":"Ding B, Long C, Zhang L, Xiao C (2019) ARGAN: attentive recurrent generative adversarial network for shadow detection and removal. In: 2019 IEEE\/CVF international conference on computer vision (ICCV), pp 10212\u201310221. https:\/\/doi.org\/10.1109\/ICCV.2019.01031","DOI":"10.1109\/ICCV.2019.01031"},{"issue":"4","key":"6989_CR25","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1109\/TPAMI.2019.2948011","volume":"43","author":"L Hou","year":"2021","unstructured":"Hou L, Vicente TFY, Hoai M, Samaras D (2021) Large scale shadow annotation and detection using lazy annotation and stacked CNNs. IEEE Trans Pattern Anal Mach Intell 43(4):1337\u20131351. https:\/\/doi.org\/10.1109\/TPAMI.2019.2948011","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6989_CR26","doi-asserted-by":"publisher","unstructured":"Hu X, Jiang Y, Fu C, Heng P (2019) Mask-ShadowGAN: learning to remove shadows from unpaired data. In: 2019 IEEE\/CVF international conference on computer vision (ICCV), pp 2472\u20132481. https:\/\/doi.org\/10.1109\/ICCV.2019.00256","DOI":"10.1109\/ICCV.2019.00256"},{"key":"6989_CR27","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T et al (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861"},{"key":"6989_CR28","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L (2018) MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), Salt Lake City, UT, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"6989_CR29","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chu G, Liang CC, Chen B, Tan MX, Wang W, Zhu Y, Pang R, Vasudevan V, Le Q, Adam H (2019) Searching for MobileNetV3. arXiv:1905.02244","DOI":"10.1109\/ICCV.2019.00140"},{"key":"6989_CR30","first-page":"357","volume":"4","author":"LC Chen","year":"2014","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2014) Semantic image segmentation with deep convolutional nets and fully connected CRFs. Comput Sci 4:357\u2013361","journal-title":"Comput Sci"},{"key":"6989_CR31","unstructured":"Chen L, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2016) DeepLab: semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. In: IEEE conference on computer vision and pattern recognition (CVPR)"},{"key":"6989_CR32","unstructured":"Chen L, Papandreou G, Schroff F, Adam H (2017) Rethinking Atrous convolution for semantic image segmentation. arXiv: 1706.05587"},{"key":"6989_CR33","doi-asserted-by":"crossref","unstructured":"K. He, G. Gkioxari, P. Doll\u00e1r and R. Girshick, Mask R-CNN, 2017 IEEE International Conference on Computer Vision (ICCV), Venice, 2017, pp. 2980\u20132988.","DOI":"10.1109\/ICCV.2017.322"},{"key":"6989_CR34","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2016.90"},{"key":"6989_CR35","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.neucom.2020.07.038","volume":"417","author":"D Li","year":"2020","unstructured":"Li D, Wang S, Tang X-S, Kong W, Shi G, Chen Y (2020) Double-stream atrous network for shadow detection. Neurocomputing 417:167\u2013175","journal-title":"Neurocomputing"},{"key":"6989_CR36","doi-asserted-by":"crossref","unstructured":"G-S Xia, Bai X, Ding J et al (2018) DOTA: a large-scale dataset for object DeTection in aerial images. https:\/\/captain-whu.github.io\/DOTA\/index.html","DOI":"10.1109\/CVPR.2018.00418"},{"key":"6989_CR37","doi-asserted-by":"crossref","unstructured":"Maggiori E, Tarabalka Y, Charpiat G, Alliez P (2017) Can semantic labeling methods generalize to any city? The Inria aerial image labeling benchmark. In: IEEE international geoscience and remote sensing symposium (IGARSS). 2017.","DOI":"10.1109\/IGARSS.2017.8127684"},{"key":"6989_CR38","doi-asserted-by":"publisher","unstructured":"Isola P, Zhu J, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, pp 5967\u20135976. https:\/\/doi.org\/10.1109\/CVPR.2017.632","DOI":"10.1109\/CVPR.2017.632"},{"key":"6989_CR39","doi-asserted-by":"crossref","unstructured":"Pohlen T, Hermans A, Mathias M, Leibe B (2017) Full-resolution residual networks for semantic segmentation in street scenes. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, pp 3309\u20133318","DOI":"10.1109\/CVPR.2017.353"},{"key":"6989_CR40","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1590\/s1982-21702017000400038","volume":"23","author":"V Freitas","year":"2017","unstructured":"Freitas V, Reis B, Tommaselli A (2017) Automatic shadow detection in aerial and terrestrial images. Boletim de Ci\u00eancias Geod\u00e9sicas 23:578\u2013590","journal-title":"Boletim de Ci\u00eancias Geod\u00e9sicas"},{"key":"6989_CR41","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.isprsjprs.2017.11.005","volume":"140","author":"GF Silva","year":"2018","unstructured":"Silva GF, Carneiro GB, Doth R, Amaral LA, de Azevedo DFG (2018) Near real-time shadow detection and removal in aerial motion imagery application. ISPRS J Photogramm Remote Sens 140:104\u2013121","journal-title":"ISPRS J Photogramm Remote Sens"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-06989-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-06989-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-06989-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T06:15:55Z","timestamp":1652681755000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-06989-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,6]]},"references-count":41,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["6989"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-06989-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,3,6]]},"assertion":[{"value":"31 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2022","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 declared no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}