{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T11:01:48Z","timestamp":1772103708046,"version":"3.50.1"},"reference-count":100,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-026-21299-2","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T08:18:20Z","timestamp":1772093900000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Semantic segmentation performance of aerial image segmentation using weighted ensemble trained networks CNNs"],"prefix":"10.1007","volume":"85","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2616-8448","authenticated-orcid":false,"given":"Zahra","family":"Faska","sequence":"first","affiliation":[]},{"given":"Lahbib","family":"Khrissi","sequence":"additional","affiliation":[]},{"given":"Khalid","family":"Haddouch","sequence":"additional","affiliation":[]},{"given":"Nabil","family":"El Akkad","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"21299_CR1","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1007\/s12065-020-00544-z","volume":"15","author":"L Khrissi","year":"2022","unstructured":"Khrissi L, El Akkad N, Satori H, Satori K (2022) Clustering method and sine cosine algorithm for image segmentation. Evol Intell 15:669\u2013682. https:\/\/doi.org\/10.1007\/s12065-020-00544-z","journal-title":"Evol Intell"},{"issue":"6","key":"21299_CR2","doi-asserted-by":"publisher","first-page":"423","DOI":"10.14569\/ijacsa.2021.0120647","volume":"12","author":"L Khrissi","year":"2021","unstructured":"Khrissi L, Satori H, Satori K, El Akkad N (2021) An efficient image clustering technique based on fuzzy C-means and cuckoo search algorithm. Int J Adv Comput Sci Appl 12(6):423\u2013432. https:\/\/doi.org\/10.14569\/ijacsa.2021.0120647","journal-title":"Int J Adv Comput Sci Appl"},{"key":"21299_CR3","doi-asserted-by":"publisher","DOI":"10.1186\/s44147-023-00226-4","volume":"70","author":"Z Faska","year":"2023","unstructured":"Faska Z, Khrissi L, Haddouch K et al (2023) A robust and consistent stack generalized ensemble-learning framework for image segmentation. J Eng Appl Sci 70:74. https:\/\/doi.org\/10.1186\/s44147-023-00226-4","journal-title":"J Eng Appl Sci"},{"key":"21299_CR4","doi-asserted-by":"publisher","unstructured":"Khrissi L, El Akkad N, Satori H, Satori K (2023) A feature selection approach based on Archimedes\u2019 optimization algorithm for optimal data classification. Int J Interact Multimed Artif Intell. https:\/\/doi.org\/10.9781\/ijimai.2023.01.005","DOI":"10.9781\/ijimai.2023.01.005"},{"key":"21299_CR5","doi-asserted-by":"publisher","first-page":"98088","DOI":"10.1109\/access.2025.3574555","volume":"13","author":"Z Faska","year":"2025","unstructured":"Faska Z et al (2025) A coherent approach-based fine-tuning of segment anything model plus watershed algorithm for instance segmentation of mitochondria in electron microscopy images. IEEE Access 13:98088\u201398105. https:\/\/doi.org\/10.1109\/access.2025.3574555","journal-title":"IEEE Access"},{"issue":"2","key":"21299_CR6","doi-asserted-by":"publisher","first-page":"159","DOI":"10.47839\/ijc.21.2.2584","volume":"21","author":"L Khrissi","year":"2022","unstructured":"Khrissi L, El Akkad N, Satori H, Satori K (2022) A performant clustering approach based on an improved sine cosine algorithm. Int J Comput 21(2):159\u2013168","journal-title":"Int J Comput"},{"issue":"4","key":"21299_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-023-01802-4","volume":"4","author":"H Moussaoui","year":"2023","unstructured":"Moussaoui H, El Akkad N, Benslimane M (2023) A brain tumor segmentation and detection technique based on birch and marker watershed. SN Comput Sci 4(4):339","journal-title":"SN Comput Sci"},{"key":"21299_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICCAD64771.2025.11099155","volume-title":"\"satellite imagery semantic segmentation using InceptionResNetV2-Unet transfer learning model,\" 2025 international conference on control, automation and diagnosis (ICCAD)","author":"Z Faska","year":"2025","unstructured":"Faska Z et al (2025) \"satellite imagery semantic segmentation using InceptionResNetV2-Unet transfer learning model,\" 2025 international conference on control, automation and diagnosis (ICCAD). Barcelona, Spain, pp 1\u20136. https:\/\/doi.org\/10.1109\/ICCAD64771.2025.11099155"},{"key":"21299_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICCSC62074.2024.10616428","volume-title":"\u201cSeg_UResNet: a deep hybrid convolutional neural network of road scenes for semantic segmentation,\u201d 2024 international conference on circuit","author":"Z Faska","year":"2024","unstructured":"Faska Z, Khrissi L, Haddouch K, El Akkad N (2024) \u201cSeg_UResNet: a deep hybrid convolutional neural network of road scenes for semantic segmentation,\u201d 2024 international conference on circuit. Morocco, Systems and Communication (ICCSC), Fes, pp 1\u20138. https:\/\/doi.org\/10.1109\/ICCSC62074.2024.10616428"},{"issue":"1","key":"21299_CR10","doi-asserted-by":"publisher","first-page":"95","DOI":"10.19139\/soic-2310-5070-1549","volume":"11","author":"H Moussaoui","year":"2023","unstructured":"Moussaoui H, El Akkad N, Benslimane M (2023) A hybrid skin lesions segmentation approach based on image processing methods. Stat Optim Inf Comput 11(1):95\u2013105","journal-title":"Stat Optim Inf Comput"},{"key":"21299_CR11","first-page":"9268754","volume-title":"4th International Conference on Intelligent Computing in Data Sciences, ICDS 2020","author":"L Khrissi","year":"2020","unstructured":"Khrissi L, Akkad NEL, Satori H, Satori K (2020) Simple and Efficient Clustering Approach Based on Cuckoo Search Algorithm. In: 4th International Conference on Intelligent Computing in Data Sciences, ICDS 2020. IEEE, p 9268754"},{"key":"21299_CR12","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1007\/978-3-031-29860-8_52","volume-title":"Digital Technologies and Applications: Proceedings of ICDTA\u201923, Fez, Morocco, Volume 2","author":"Z Faska","year":"2023","unstructured":"Faska Z, Khrissi L, Haddouch K et al (2023) Random Forest for Semantic Segmentation Using Pre-Trained CNN (VGG16) Features. In: Digital Technologies and Applications: Proceedings of ICDTA\u201923, Fez, Morocco, Volume 2. Cham, Springer Nature Switzerland, pp 510\u2013520"},{"key":"21299_CR13","first-page":"8931358","volume-title":"Color image segmentation based on hybridization between Canny and k-means. 7th Mediterranean Congress of Telecommunications 2019","author":"L Khrissi","year":"2019","unstructured":"Khrissi L, Akkad NE, Satori H, Satori K (2019) Color image segmentation based on hybridization between Canny and k-means. 7th Mediterranean Congress of Telecommunications 2019. CMT, p 8931358"},{"key":"21299_CR14","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1007\/978-981-33-6893-4_74","volume-title":"WITS 2020","author":"H Moussaoui","year":"2022","unstructured":"Moussaoui H, Benslimane M, El Akkad N (2022) Image segmentation approach based on hybridization between K-means and mask R-CNN. In: WITS 2020. Springer, Singapore, pp 821\u2013830"},{"key":"21299_CR15","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1007\/978-3-030-73882-2_81","volume-title":"International conference on digital technologies and applications","author":"Z Faska","year":"2021","unstructured":"Faska Z, Khrissi L, Haddouch K, El Akkad N (2021) A powerful and efficient method of image segmentation based on random forest algorithm. In: International conference on digital technologies and applications. Springer, Cham, pp 893\u2013903 8"},{"issue":"1","key":"21299_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-65272-1","volume":"14","author":"H Moussaoui","year":"2024","unstructured":"Moussaoui H, El Akkad N, Benslimane M, El-Shafai W, Baihan A, Hewage C, Singh Rathore R (2024) Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition. Sci Rep 14(1):14389","journal-title":"Sci Rep"},{"key":"21299_CR17","unstructured":"Ohta Y-i, Kanade T, Sakai T (1978) An analysis systemfor scenes containing objects with substructures, proceedings of the fourth international joint conference on. Pattern Recogn:752\u2013754"},{"key":"21299_CR18","doi-asserted-by":"crossref","unstructured":"A. Garcia-Garcia, S. Orts-Escolano, S. Oprea, V. Villena-Martinez, J. Garcia-Rodriguez, A Reviewon Deep Learning Techniques Applied to Semantic Segmentation, 2017. Preprint at https:\/\/arxiv.org\/abs\/1704.06857.","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"21299_CR19","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.neucom.2018.03.037","volume":"304","author":"H Yu","year":"2018","unstructured":"Yu H, Yang Z, Tan L, Wang Y, Sun W, Sun M, Tang Y (2018) Methods and datasets on semantic segmentation: a review. Neurocomputing 304:82\u2013103","journal-title":"Neurocomputing"},{"key":"21299_CR20","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1364\/ON.15.5.000008","volume":"15","author":"S Edelman","year":"1989","unstructured":"Edelman S, Poggio T (1989) Integrating visual cues for object segmentation and recognition. Opt News 15:8","journal-title":"Opt News"},{"key":"21299_CR21","unstructured":"A. Kirillov, K. He, R. Girshick, C. Rother, P. Doll\u00e1r, Panoptic Segmentation, 2018. Preprint at https:\/\/arxiv.org\/abs\/1801.00868."},{"key":"21299_CR22","doi-asserted-by":"crossref","unstructured":"B. Cheng, M.D. Collins, Y. Zhu, T. Liu, T.S. Huang, H. Adam, L.-C. Chen, Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-up Panoptic Segmentation, https:\/\/arxiv.org\/abs\/1911.10194 2019.","DOI":"10.1109\/CVPR42600.2020.01249"},{"key":"21299_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-29860-8_52","volume-title":"International conference on digital technologies and applications","author":"Z Faska","year":"2023","unstructured":"Faska Z, Khrissi L, Haddouch K, Akkad EL, N. (2023) Random Forest for semantic segmentation using pre-trained CNN (VGG16) features. In: International conference on digital technologies and applications. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-031-29860-8_52"},{"key":"21299_CR24","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436","journal-title":"Nature"},{"key":"21299_CR25","volume-title":"Courville","author":"Y Goodfellow","year":"2016","unstructured":"Goodfellow Y, Bengio A (2016) Courville. MIT Press, Deep Learning"},{"key":"21299_CR26","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to documentrecognition. Proc IEEE 86:2278\u20132324","journal-title":"Proc IEEE"},{"key":"21299_CR27","unstructured":"Z.C. Lipton, J. Berkowitz, C. Elkan, A Critical Review of Recurrent Neural Networks for Sequence Learning, https:\/\/arxiv.org\/abs\/1506.00019 2015."},{"key":"21299_CR28","first-page":"41","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops","author":"F Visin","year":"2016","unstructured":"Visin F, Ciccone M, Romero A, Kastner K, Cho K, Bengio Y, Matteucci M, Courville A (2016) Reseg: a recurrent neural network-based model for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 41\u201348"},{"key":"21299_CR29","first-page":"2","volume":"1","author":"A Ess","year":"2009","unstructured":"Ess A, M\u00fcller T, Grabner H, Van Gool LJ (2009) Segmentation-based urban traffic scene understanding. BMVC 1:2","journal-title":"BMVC"},{"key":"21299_CR30","doi-asserted-by":"publisher","first-page":"3354","DOI":"10.1109\/CVPR.2012.6248074","volume-title":"2012 IEEE Conference on Computer Vision and Pattern Recognition","author":"A Geiger","year":"2012","unstructured":"Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? The KITTI vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 3354\u20133361. https:\/\/doi.org\/10.1109\/CVPR.2012.6248074"},{"key":"21299_CR31","doi-asserted-by":"crossref","unstructured":"Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B (2016) The cityscapes dataset for semantic urban scene understanding. Proc IEEE Conf Comput Vis Pattern Recognit:3213\u20133223","DOI":"10.1109\/CVPR.2016.350"},{"key":"21299_CR32","unstructured":"M. Oberweger, P. Wohlhart, V. Lepetit, Hands Deep in Deep Learning for Hand Pose Estimation, 2015. Preprint at https:\/\/arxiv.org\/abs\/1502.06807."},{"key":"21299_CR33","first-page":"24","volume-title":"Proceedings of the IEEE International Conference on Computer Vision Workshops","author":"Y Yoon","year":"2015","unstructured":"Yoon Y, Jeon H-G, Yoo D, Lee J-Y, So Kweon I (2015) Learning a deep convolutional network for light-field image super-resolution. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp 24\u201332"},{"key":"21299_CR34","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1145\/2647868.2654948","volume-title":"Proceedings of the 22nd ACM International Conference on Multimedia","author":"J Wan","year":"2014","unstructured":"Wan J, Wang D, Hoi SCH, Wu P, Zhu J, Zhang Y, Li J (2014) Deep learning for content-based image retrieval: a comprehensive study. In: Proceedings of the 22nd ACM International Conference on Multimedia. ACM, pp 157\u2013166"},{"key":"21299_CR35","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jvcir.2015.10.012","volume":"34","author":"H Zhu","year":"2016","unstructured":"Zhu H, Meng F, Cai J, Lu S (2016) Beyond pixels: a comprehensive survey from bottom-up to semantic image segmentation and cosegmentation. J Vis Commun Image Represent 34:12\u201327. https:\/\/doi.org\/10.1016\/j.jvcir.2015.10.012","journal-title":"J Vis Commun Image Represent"},{"key":"21299_CR36","first-page":"770","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"K He","year":"2016","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"},{"issue":"4","key":"21299_CR37","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic seg-mentation. IEEE Trans Pattern Anal Mach Intell 39(4):640\u2013651","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"21299_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.108337","volume":"103","author":"S Zhu","year":"2022","unstructured":"Zhu S, Ma W, Yao J (2022) Global and local geometric constrained feature matching for high resolution remote sensing images. Comput Electr Eng 103:108337","journal-title":"Comput Electr Eng"},{"issue":"9","key":"21299_CR39","doi-asserted-by":"publisher","first-page":"7918","DOI":"10.1109\/TGRS.2020.3044655","volume":"59","author":"X Wang","year":"2021","unstructured":"Wang X, Wang S, Ning C, Zhou H (2021) Enhanced feature pyramid network with deep semantic embedding for remote sensing scene classification. IEEE Trans Geosci Remote Sens 59(9):7918\u20137932","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"21299_CR40","doi-asserted-by":"publisher","first-page":"2030","DOI":"10.1109\/JSTARS.2021.3051569","volume":"14","author":"X Tang","year":"2021","unstructured":"Tang X, Ma Q, Zhang X, Liu F, Ma J, Jiao L (2021) Attention consistent network for remote sensing scene classification. IEEE J Sel Top Appl Earth Observ Remote Sens 14:2030\u20132045","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"key":"21299_CR41","first-page":"1106","volume-title":"Proc. adv","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton G (2012) ImageNet classification with deep convolutional neural networks. In: Proc. adv. NIPS, pp 1106\u20131114"},{"key":"21299_CR42","doi-asserted-by":"publisher","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp 3431\u20133440. https:\/\/doi.org\/10.1109\/CVPR.2015.7298965","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"21299_CR43","unstructured":"Sherrah J. Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery. 2016, Preprint at https:\/\/arxiv.org\/abs\/1606.02585."},{"issue":"11","key":"21299_CR44","doi-asserted-by":"publisher","first-page":"6054","DOI":"10.1109\/TGRS.2017.2719738","volume":"55","author":"P Kaiser","year":"2017","unstructured":"Kaiser P, Wegner J, Lucchi A, Jaggi M, Hofmann T, Schindler K (2017) Learning aerial image segmentation from online maps. IEEE Trans Geosci Remote Sens 55(11):6054\u20136068","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"21299_CR45","first-page":"234","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"O Ronneberger","year":"2015","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, Cham, pp 234\u2013241"},{"issue":"12","key":"21299_CR46","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Handa A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"21299_CR47","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2018","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2018) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"21299_CR48","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1109\/MSP.2017.2749125","volume":"35","author":"J Han","year":"2018","unstructured":"Han J, Zhang D, Cheng G, Liu N, Xu D (2018) Advanced deep-learning techniques for salient and category-specific object detection: a survey. IEEE Signal Process Mag 35:84\u2013100","journal-title":"IEEE Signal Process Mag"},{"key":"21299_CR49","unstructured":"K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014. Preprint at arXiv preprint https:\/\/arxiv.org\/abs\/1409.1556."},{"key":"21299_CR50","doi-asserted-by":"publisher","first-page":"1840","DOI":"10.1109\/ACCESS.2019.2962284","volume":"8","author":"S Yu","year":"2019","unstructured":"Yu S, Xie L, Liu L, Xia D (2019) Learning long-term temporal features with deep neural networks for human action recognition. IEEE Access 8:1840\u20131850","journal-title":"IEEE Access"},{"key":"21299_CR51","first-page":"2818","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"C Szegedy","year":"2016","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 2818\u20132826"},{"key":"21299_CR52","first-page":"4700","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"G Huang","year":"2017","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4700\u20134708"},{"key":"21299_CR53","doi-asserted-by":"publisher","first-page":"61085","DOI":"10.1109\/ACCESS.2020.2982906","volume":"8","author":"A Al-Dhamari","year":"2020","unstructured":"Al-Dhamari A, Sudirman R, Mahmood NH (2020) Transfer deep learning along with binary support vector machine for abnormal behavior detection. IEEE Access 8:61085\u201361095","journal-title":"IEEE Access"},{"issue":"5","key":"21299_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TEVC.2019.2890858","volume":"23","author":"J Su","year":"2019","unstructured":"Su J, Vargas DV, Sakurai K (2019) One pixel attack for fooling deep neural networks. IEEE Trans Evol Comput 23(5):1\u201313","journal-title":"IEEE Trans Evol Comput"},{"key":"21299_CR55","unstructured":"Luo W., Wu C., Zhou N., Ni L. Random Directional Attack for Fooling Deep Neural Networks. 2019, Preprint at https:\/\/arxiv.org\/abs\/1908.02658."},{"key":"21299_CR56","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/978-3-642-46466-9_18","volume-title":"Competition and cooperation in neural nets","author":"Y LeCun","year":"1982","unstructured":"LeCun Y, Boser B, Fukushima JK, Miyake S (1982) Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition. In: Competition and cooperation in neural nets. Springer, pp 267\u2013285. https:\/\/doi.org\/10.1007\/978-3-642-46466-9_18"},{"issue":"4","key":"21299_CR57","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"S Denker","year":"1989","unstructured":"Denker S, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541\u2013551. https:\/\/doi.org\/10.1162\/neco.1989.1.4.541","journal-title":"Neural Comput"},{"issue":"6","key":"21299_CR58","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"issue":"3","key":"21299_CR59","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) ImageNet large scale visual recognition challenge (ILSVRC). Int J Comput Vis 115(3):211\u2013252. https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int J Comput Vis"},{"key":"21299_CR60","doi-asserted-by":"publisher","DOI":"10.1117\/1.JRS.11.042609","volume":"11","author":"JE Ball","year":"2017","unstructured":"Ball JE, Anderson DT, Chan CS (2017) Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community. J Appl Remote Sens 11:042609. https:\/\/doi.org\/10.1117\/1.JRS.11.042609","journal-title":"J Appl Remote Sens"},{"key":"21299_CR61","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015. Springer International Publishing, Cham, pp 234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"21299_CR62","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J., 2017. Pyramid scene parsing network. Preprint at https:\/\/arxiv.org\/abs\/1612.01105.","DOI":"10.1109\/CVPR.2017.660"},{"key":"21299_CR63","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., Adam, H., 2017. Rethinking Atrous convolution for semantic image segmentation. Preprint at https:\/\/arxiv.org\/abs\/1706.05587."},{"key":"21299_CR64","unstructured":"Simonyan, K., Zisserman, A., 2015. Very deep convolutional networks for large-scale image recognition. Preprint at https:\/\/arxiv.org\/abs\/1409.1556."},{"key":"21299_CR65","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3038225","author":"A Abdollahi","year":"2020","unstructured":"Abdollahi A, Pradhan B, Gite S, Alamri A (2020) Building footprint extraction from high resolution aerial images using generative adversarial network (GAN) architecture. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2020.3038225","journal-title":"IEEE Access"},{"key":"21299_CR66","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","volume":"55","author":"E Maggiori","year":"2016","unstructured":"Maggiori E, Tarabalka Y, Charpiat G, Alliez P (2016) Convolutional neural networks for large-scale remote-sensing image classification. IEEE Trans Geosci Remote Sens 55:645\u2013657","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"21299_CR67","doi-asserted-by":"publisher","first-page":"2793","DOI":"10.1109\/TPAMI.2017.2750680","volume":"40","author":"J Yuan","year":"2017","unstructured":"Yuan J (2017) Learning building extraction in aerial scenes with convolutional networks. IEEE Trans Pattern Anal Mach Intell 40:2793\u20132798","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"21299_CR68","first-page":"1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"C Szegedy","year":"2015","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139"},{"key":"21299_CR69","doi-asserted-by":"publisher","first-page":"2528","DOI":"10.1109\/CVPR.2010.5539957","volume-title":"2010 IEEE computer society conference on computer vision and pattern recognition","author":"MD Zeiler","year":"2010","unstructured":"Zeiler MD, Krishnan D, Taylor GW, Fergus R (2010) Deconvolutional networks. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 2528\u20132535"},{"key":"21299_CR70","first-page":"1520","volume-title":"Proceedings of the IEEE international conference on computer vision","author":"H Noh","year":"2015","unstructured":"Noh H, Hong S, Han B (2015) Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE international conference on computer vision, pp 1520\u20131528"},{"key":"21299_CR71","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder\u2013decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39:2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"21299_CR72","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","volume":"5","author":"XX Zhu","year":"2017","unstructured":"Zhu XX, Tuia D, Mou L, Xia GS, Zhang L, Xu F, Fraundorfer F (2017) Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geosci Remote Sens Mag 5:8\u201336. https:\/\/doi.org\/10.1109\/MGRS.2017.2762307","journal-title":"IEEE Geosci Remote Sens Mag"},{"key":"21299_CR73","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3089623","volume":"60","author":"K Lee","year":"2022","unstructured":"Lee K, Kim JH, Lee H, Park J, Choi JP, Hwang JY (2022) Boundary-oriented binary building segmentation model with two scheme learning for aerial images. IEEE Trans Geosci Remote Sens 60:1\u201317. https:\/\/doi.org\/10.1109\/TGRS.2021.3089623","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"10","key":"21299_CR74","doi-asserted-by":"publisher","first-page":"1777","DOI":"10.1109\/LGRS.2019.2953523","volume":"17","author":"X Zhang","year":"2020","unstructured":"Zhang X, Ma W, Li C, Wu J, Tang X, Jiao L (2020) Fully convolutional networkbased ensemble method for road extraction from aerial images. IEEE Geosci Remote Sens Lett 17(10):1777\u20131781. https:\/\/doi.org\/10.1109\/LGRS.2019.2953523","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"1","key":"21299_CR75","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13640-015-0087-0","volume":"2015","author":"U Verma","year":"2015","unstructured":"Verma U, Rossant F, Bloch I (2015) Segmentation and size estimation of tomatoes from sequences of paired images. EURASIP J Image Video Process 2015(1):1\u201323. https:\/\/doi.org\/10.1186\/s13640-015-0087-0","journal-title":"EURASIP J Image Video Process"},{"issue":"7","key":"21299_CR76","doi-asserted-by":"publisher","first-page":"1713","DOI":"10.1109\/TITS.2016.2622280","volume":"18","author":"H Zhou","year":"2017","unstructured":"Zhou H, Kong H, Wei L, Creighton D, Nahavandi S (2017) On detecting road regions in a single UAV image. IEEE Trans Intell Transp Syst 18(7):1713\u20131722. https:\/\/doi.org\/10.1109\/TITS.2016.2622280","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"14","key":"21299_CR77","doi-asserted-by":"publisher","DOI":"10.3390\/rs13142787","volume":"13","author":"MBA Gibril","year":"2021","unstructured":"Gibril MBA, Shafri HZM, Shanableh A, Al-Ruzouq R, Wayayok A, Hashim SJ (2021) Deep convolutional neural network for large-scale date palm tree mapping from UAV-based images. Remote Sens 13(14):2787. https:\/\/doi.org\/10.3390\/rs13142787","journal-title":"Remote Sens"},{"key":"21299_CR78","doi-asserted-by":"publisher","unstructured":"Pandey A, Jain K (2021) An intelligent system for crop identification and classification from UAV images using conjugated dense convolutional neural network. Comput Electron Agric:106\u2013543. https:\/\/doi.org\/10.1016\/j.compag.2021.106543","DOI":"10.1016\/j.compag.2021.106543"},{"key":"21299_CR79","unstructured":"R.K. Srivastava, K. Greff, J. Schmidhuber, Highway networks, 2015, Preprint at http:\/\/arxiv.org\/abs\/1505.00387."},{"key":"21299_CR80","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00496","volume-title":"RoadTracer: automatic extraction of road networks from aerial images","author":"F Bastani","year":"2018","unstructured":"Bastani F, He S, Abbar S, Alizadeh M, Balakrishnan H, Chawla S, Madden S, DeWitt D (2018) RoadTracer: automatic extraction of road networks from aerial images. Computer Vision and Pattern Recognition. https:\/\/doi.org\/10.1109\/CVPR.2018.00496"},{"key":"21299_CR81","doi-asserted-by":"publisher","unstructured":"Sun T, Chen Z, Yang W, Wang Y Stacked U-nets with multi-output for road extraction. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol 2018, pp 187\u2013191. https:\/\/doi.org\/10.1109\/CVPRW.2018.00033","DOI":"10.1109\/CVPRW.2018.00033"},{"key":"21299_CR82","unstructured":"A. Van Etten, You only look twice: rapid multi-scale object detection in satellite imagery, 2018, Preprint at http:\/\/arxiv.org\/abs\/1805.09512."},{"key":"21299_CR83","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/CVPRW.2018.00031","volume-title":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","author":"I Demir","year":"2018","unstructured":"Demir I, Koperski K, Lindenbaum D, Pang G, Huang J, Basu S, Hughes F, Tuia D, Raska R (2018) DeepGlobe 2018: A challenge to parse the earth through satellite images. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp 172\u2013181. https:\/\/doi.org\/10.1109\/CVPRW.2018.00031"},{"issue":"9","key":"21299_CR84","doi-asserted-by":"publisher","DOI":"10.3390\/app14093712","volume":"14","author":"BA Khan","year":"2024","unstructured":"Khan BA, Jung JW (2024) Semantic segmentation of aerial imagery using U-net with self-attention and separable convolutions. Appl Sci 14(9):3712. https:\/\/doi.org\/10.3390\/app14093712","journal-title":"Appl Sci"},{"issue":"1","key":"21299_CR85","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-84795-1","volume":"15","author":"LT Ramos","year":"2025","unstructured":"Ramos LT, Sappa AD (2025) Leveraging U-net and selective feature extraction for land cover classification using remote sensing imagery. Sci Rep 15(1):784. https:\/\/doi.org\/10.1038\/s41598-024-84795-1","journal-title":"Sci Rep"},{"key":"21299_CR86","doi-asserted-by":"publisher","first-page":"2077","DOI":"10.3390\/rs16122077","volume":"16","author":"I Dimitrovski","year":"2024","unstructured":"Dimitrovski I, Spasev V, Loshkovska S, Kitanovski I (2024) U-net ensemble for enhanced semantic segmentation in remote sensing imagery. Remote Sens 16:2077. https:\/\/doi.org\/10.3390\/rs16122077","journal-title":"Remote Sens"},{"key":"21299_CR87","first-page":"282","volume-title":"9th International Conference, ICISP 2020, Marrakesh, Morocco, June 4\u20136, 2020, Proceedings 9","author":"A Benali Amjoud","year":"2020","unstructured":"Benali Amjoud A, Amrouch M (2020) Convolutional neural networks backbones for object detection, Image and Signal Processing. In: 9th International Conference, ICISP 2020, Marrakesh, Morocco, June 4\u20136, 2020, Proceedings 9. Springer, pp 282\u2013289"},{"key":"21299_CR88","doi-asserted-by":"publisher","first-page":"1743","DOI":"10.1109\/CVPR.2017.189","volume-title":"Proceedings of the - 30th IEEE conference on computer vision and pattern recognition, CVPR","author":"C Peng","year":"2017","unstructured":"Peng C, Zhang X, Yu G, Luo G, Sun J (2017) Large kernel matters - Improve semantic segmentation by global convolutional network. In: Proceedings of the - 30th IEEE conference on computer vision and pattern recognition, CVPR, pp 1743\u20131751. https:\/\/doi.org\/10.1109\/CVPR.2017.189"},{"key":"21299_CR89","first-page":"4278","volume-title":"Proceedings of the 31st AAAI conference on artificial intelligence","author":"C Szegedy","year":"2017","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Proceedings of the 31st AAAI conference on artificial intelligence. AAAI, pp 4278\u20134284"},{"key":"21299_CR90","first-page":"1","volume-title":"Proceedings of the IEEE computer society conference on computer vision and pattern recognition","author":"C Szegedy","year":"2014","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2014) Going deeper with convolutions. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 1\u20139"},{"key":"21299_CR91","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2022.100251","volume":"7","author":"M Shahhosseini","year":"2022","unstructured":"Shahhosseini M, Hu G, Pham H (2022) Optimizing ensemble weights and hyperparameters of machine learning models for regression problems. Machine Learning with Applications 7:100251","journal-title":"Machine Learning with Applications"},{"issue":"1","key":"21299_CR92","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1175\/1520-0434(1996)011<0003:TFAASE>2.0.CO;2","volume":"11","author":"AH Murphy","year":"1996","unstructured":"Murphy AH (1996) The finley affair: a signal event in the history of forecast verification. Weather Forecast 11(1):3","journal-title":"Weather Forecast"},{"issue":"2","key":"21299_CR93","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1111\/j.1469-8137.1912.tb05611.x","volume":"11","author":"P Jaccard","year":"1912","unstructured":"Jaccard P (1912) The distribution of the flora in the Alpine Zone.1. New Phytol 11(2):37\u201350. https:\/\/doi.org\/10.1111\/j.1469-8137.1912.tb05611.x","journal-title":"New Phytol"},{"issue":"4","key":"21299_CR94","first-page":"1","volume":"5","author":"T S\u00f8rensen","year":"1948","unstructured":"S\u00f8rensen T (1948) A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. Kong Danske Vidensk Selsk 5(4):1\u201334","journal-title":"Kong Danske Vidensk Selsk"},{"issue":"3","key":"21299_CR95","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297\u2013302. https:\/\/doi.org\/10.2307\/1932409","journal-title":"Ecology"},{"key":"21299_CR96","doi-asserted-by":"publisher","first-page":"6568","DOI":"10.1109\/ICCV.2019.00668","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","author":"K Sun","year":"2019","unstructured":"Sun K, Zhao Y, Jiang B, Cheng T, Xiao B, Liu D, Mu Y, Wang X, Liu W, Wang J (2019) High-resolution representations for labeling pixels and regions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp 6568\u20136577. https:\/\/doi.org\/10.1109\/ICCV.2019.00668"},{"key":"21299_CR97","doi-asserted-by":"publisher","DOI":"10.3390\/rs12152350","volume":"12","author":"J Ma","year":"2020","unstructured":"Ma J et al (2020) Building extraction of aerial images by a global and multi-scale encoder decoder network. Remote Sens 12:2350","journal-title":"Remote Sens"},{"key":"21299_CR98","unstructured":"V. Iglovikov, A. Shvets, \u201cTernausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation.\u201dPreprint at https:\/\/arxiv.org\/abs\/1801.05746, 2018."},{"key":"21299_CR99","unstructured":"G. Chhor, C. B. Aramburu, I. Bougdal-Lambert, Satellite image segmentation for building detection using U-Net. http:\/\/cs229.stanford.edu\/proj2017\/final-reports\/5243715.pdf, 2017."},{"issue":"1","key":"21299_CR100","first-page":"100","volume":"11","author":"\u0130 Ata\u015f","year":"2023","unstructured":"Ata\u015f \u0130 (2023) Performance evaluation of JaccardDice coefficient on building segmentation from high resolution satellite images. BAJECE 11(1):100\u2013106","journal-title":"BAJECE"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21299-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-026-21299-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21299-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T10:02:57Z","timestamp":1772100177000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-026-21299-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,26]]},"references-count":100,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["21299"],"URL":"https:\/\/doi.org\/10.1007\/s11042-026-21299-2","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,26]]},"assertion":[{"value":"27 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This research does not contain any studies with human participations or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"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":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"213"}}