{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:32:05Z","timestamp":1771065125479,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"36","license":[{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"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\/s11042-025-20928-6","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T06:31:00Z","timestamp":1749450660000},"page":"45633-45658","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Satellite imagery for land cover classification using machine learning techniques"],"prefix":"10.1007","volume":"84","author":[{"given":"Ramalingam","family":"Sugumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D.","family":"Suganya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,9]]},"reference":[{"issue":"3","key":"20928_CR1","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3390\/rs13030394","volume":"13","author":"W Zhang","year":"2021","unstructured":"Zhang W, Tang P, Corpetti T, Zhao L (2021) WTS: a weakly towards strongly supervised learning framework for remote sensing land cover classification using segmentation models. Remote Sens 13(3):394. https:\/\/doi.org\/10.3390\/rs13030394","journal-title":"Remote Sens"},{"key":"20928_CR2","doi-asserted-by":"publisher","first-page":"116744","DOI":"10.1109\/ACCESS.2020.3003914","volume":"8","author":"B Cui","year":"2020","unstructured":"Cui B, Chen X, Lu Y (2020) Semantic segmentation of remote sensing images using transfer learning and deep convolutional neural network with dense connection. Ieee Access 8:116744\u2013116755. https:\/\/doi.org\/10.1109\/ACCESS.2020.3003914","journal-title":"Ieee Access"},{"key":"20928_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3155765","volume":"60","author":"D Singh","year":"2022","unstructured":"Singh D, Kaur M, Jabarulla MY, Kumar V, Lee HN (2022) Evolving fusion-based visibility restoration model for hazy remote sensing images using dynamic differential evolution. IEEE Trans Geosci Remote Sens 60:1\u201314. https:\/\/doi.org\/10.1109\/TGRS.2022.3155765","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"20928_CR4","doi-asserted-by":"publisher","first-page":"95934","DOI":"10.1109\/ACCESS.2020.2995805","volume":"8","author":"G Sumbul","year":"2020","unstructured":"Sumbul G, Dem\u0130r B (2020) A deep multi-attention driven approach for multi-label remote sensing image classification. IEEE Access 8:95934\u201395946. https:\/\/doi.org\/10.1109\/ACCESS.2020.2995805","journal-title":"IEEE Access"},{"key":"20928_CR5","doi-asserted-by":"publisher","first-page":"14078","DOI":"10.1109\/ACCESS.2021.3051085","volume":"9","author":"H Alhichri","year":"2021","unstructured":"Alhichri H, Alswayed AS, Bazi Y, Ammour N, Alajlan NA (2021) Classification of remote sensing images using efficientNet-B3 CNN model with attention. IEEE Access 9:14078\u201314094. https:\/\/doi.org\/10.1109\/ACCESS.2021.3051085","journal-title":"IEEE Access"},{"issue":"3","key":"20928_CR6","doi-asserted-by":"publisher","first-page":"1269","DOI":"10.1109\/TNNLS.2020.3041646","volume":"33","author":"H Zhang","year":"2020","unstructured":"Zhang H, Liao Y, Yang H, Yang G, Zhang L (2020) A local\u2013global dual-stream network for building extraction from very-high-resolution remote sensing images. IEEE Trans Neural Networks Learn Syst 33(3):1269\u20131283. https:\/\/doi.org\/10.1109\/TNNLS.2020.3041646","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"key":"20928_CR7","doi-asserted-by":"publisher","unstructured":"Kaur G, Saini KS, Singh D, Kaur M (2021) A comprehensive study on computational pansharpening techniques for remote sensing images. Arch Comput Methods Eng 1\u201318. https:\/\/doi.org\/10.1007\/s11831-021-09565-y","DOI":"10.1007\/s11831-021-09565-y"},{"issue":"5","key":"20928_CR8","doi-asserted-by":"publisher","first-page":"2486","DOI":"10.1109\/TGRS.2016.2645610","volume":"55","author":"Y Long","year":"2017","unstructured":"Long Y, Gong Y, Xiao Z, Liu Q (2017) Accurate object localization in remote sensing images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 55(5):2486\u20132498. https:\/\/doi.org\/10.1109\/TGRS.2016.2645610","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"9","key":"20928_CR9","doi-asserted-by":"publisher","first-page":"3277","DOI":"10.1080\/01431161.2020.1871094","volume":"42","author":"W Zhang","year":"2021","unstructured":"Zhang W, Tang P, Zhao L (2021) Fast and accurate land-cover classification on medium-resolution remote-sensing images using segmentation models. Int J Remote Sens 42(9):3277\u20133301. https:\/\/doi.org\/10.1080\/01431161.2020.1871094","journal-title":"Int J Remote Sens"},{"key":"20928_CR10","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5\u20139, 2015, proceedings, part III 18 (pp. 234\u2013241). Springer international publishing. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"20928_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2021.3098774","volume":"19","author":"X Li","year":"2021","unstructured":"Li X, He M, Li H, Shen H (2021) A combined loss-based multiscale fully convolutional network for high-resolution remote sensing image change detection. IEEE Geosci Remote Sens Lett 19:1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2021.3098774","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"20928_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2021.3065148","volume":"19","author":"D Xiao","year":"2021","unstructured":"Xiao D, Yin L, Fu Y (2021) Open-pit mine road extraction from high-resolution remote sensing images using RATT-UNet. IEEE Geosci Remote Sens Lett 19:1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2021.3065148","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"6","key":"20928_CR13","doi-asserted-by":"publisher","first-page":"2153","DOI":"10.3390\/s21062153","volume":"21","author":"Y Hou","year":"2021","unstructured":"Hou Y, Liu Z, Zhang T, Li Y (2021) C-UNet: complement UNet for remote sensing road extraction. Sensors 21(6):2153. https:\/\/doi.org\/10.3390\/s21062153","journal-title":"Sensors"},{"issue":"6","key":"20928_CR14","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1109\/LGRS.2019.2938555","volume":"17","author":"Q Sang","year":"2019","unstructured":"Sang Q, Zhuang Y, Dong S, Wang G, Chen H (2019) FRF-Net: land cover classification from large-scale VHR optical remote sensing images. IEEE Geosci Remote Sens Lett 17(6):1057\u20131061. https:\/\/doi.org\/10.1109\/LGRS.2019.2938555","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"20928_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2021.3086117","volume":"19","author":"R Zhang","year":"2021","unstructured":"Zhang R, Chen J, Feng L, Li S, Yang W, Guo D (2021) A refined pyramid scene parsing network for polarimetric SAR image semantic segmentation in agricultural areas. IEEE Geosci Remote Sens Lett 19:1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2021.3086117","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"7","key":"20928_CR16","doi-asserted-by":"publisher","first-page":"138","DOI":"10.3390\/jimaging9070138","volume":"9","author":"W Bakasa","year":"2023","unstructured":"Bakasa W, Viriri S (2023) Vgg16 feature extractor with extreme gradient boost classifier for pancreas cancer prediction. J Imaging 9(7):138. https:\/\/doi.org\/10.3390\/jimaging9070138","journal-title":"J Imaging"},{"issue":"10","key":"20928_CR17","doi-asserted-by":"publisher","first-page":"942","DOI":"10.1080\/2150704X.2018.1500044","volume":"9","author":"D Singh","year":"2018","unstructured":"Singh D, Kaur M, Singh H (2018) Remote sensing image fusion using fuzzy logic and gyrator transform. Remote Sens Lett 9(10):942\u2013951. https:\/\/doi.org\/10.1080\/2150704X.2018.1500044","journal-title":"Remote Sens Lett"},{"key":"20928_CR18","doi-asserted-by":"publisher","first-page":"63121","DOI":"10.1109\/ACCESS.2020.2984310","volume":"8","author":"Y Li","year":"2020","unstructured":"Li Y, Pei X, Huang Q, Jiao L, Shang R, Marturi N (2020) Anchor-free single stage detector in remote sensing images based on multiscale dense path aggregation feature pyramid network. IEEE Access 8:63121\u201363133. https:\/\/doi.org\/10.1109\/ACCESS.2020.2984310","journal-title":"IEEE Access"},{"key":"20928_CR19","doi-asserted-by":"publisher","first-page":"128774","DOI":"10.1109\/ACCESS.2019.2940527","volume":"7","author":"Y Liu","year":"2019","unstructured":"Liu Y, Gross L, Li Z, Li X, Fan X, Qi W (2019) Automatic building extraction on high-resolution remote sensing imagery using deep convolutional encoder-decoder with spatial pyramid pooling. IEEE Access 7:128774\u2013128786. https:\/\/doi.org\/10.1109\/ACCESS.2019.2940527","journal-title":"IEEE Access"},{"key":"20928_CR20","doi-asserted-by":"publisher","first-page":"56267","DOI":"10.1109\/ACCESS.2022.3175978","volume":"10","author":"Z Fan","year":"2022","unstructured":"Fan Z, Zhan T, Gao Z, Li R, Liu Y, Zhang L, Jin Z, Xu S (2022) Land cover classification of resources survey remote sensing images based on segmentation model. IEEE Access 10:56267\u201356281. https:\/\/doi.org\/10.1109\/ACCESS.2022.3175978","journal-title":"IEEE Access"},{"key":"20928_CR21","doi-asserted-by":"publisher","unstructured":"Rashmi A, Brindha S, Sandhya J, Lakshmi G (2022) Mapping the surface water using convolution neural networks for remote sensing technology. In:\u00a02022 International Conference on Communication, Computing and Internet of Things (IC3IoT) (pp. 1\u20136). IEEE. https:\/\/doi.org\/10.1109\/IC3IOT53935.2022.9767955","DOI":"10.1109\/IC3IOT53935.2022.9767955"},{"key":"20928_CR22","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.isprsjprs.2022.02.013","volume":"186","author":"Y Li","year":"2022","unstructured":"Li Y, Zhou Y, Zhang Y, Zhong L, Wang J, Chen J (2022) Domain knowledge-guided deep collaborative fusion network for multimodal unitemporal remote sensing land cover classification. ISPRS J Photogrammetry Remote Sens 186:170\u2013189. https:\/\/doi.org\/10.1016\/j.isprsjprs.2022.02.013","journal-title":"ISPRS J Photogrammetry Remote Sens"},{"key":"20928_CR23","doi-asserted-by":"publisher","first-page":"6916","DOI":"10.1109\/JSTARS.2021.3090085","volume":"14","author":"M Ye","year":"2021","unstructured":"Ye M, Ruiwen N, Chang Z, He G, Tianli H, Shijun L, Yu S, Tong Z, Ying G (2021) A lightweight model of VGG-16 for remote sensing image classification. IEEE J Sel Top Appl Earth Observations Remote Sens 14:6916\u20136922. https:\/\/doi.org\/10.1109\/JSTARS.2021.3090085","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sens"},{"key":"20928_CR24","doi-asserted-by":"publisher","unstructured":"Yadav CS, Pradhan MK, Gangadharan SMP, Chaudhary JK, Singh J, Khan AA, Haq MA, Alhussen A, Wechtaisong C, Imran H, Alzamil ZS (2022) Multi-class pixel certainty active learning model for classification of land cover classes using hyperspectral imagery. Electronics 11(17). https:\/\/doi.org\/10.3390\/electronics11172799","DOI":"10.3390\/electronics11172799"},{"key":"20928_CR25","doi-asserted-by":"publisher","first-page":"101955","DOI":"10.1016\/j.ecoinf.2022.101955","volume":"74","author":"YG Yuh","year":"2023","unstructured":"Yuh YG, Tracz W, Matthews HD, Turner SE (2023) Application of machine learning approaches for land cover monitoring in Northern Cameroon. Ecol Inf 74:101955. https:\/\/doi.org\/10.1016\/j.ecoinf.2022.101955","journal-title":"Ecol Inf"},{"key":"20928_CR26","doi-asserted-by":"publisher","unstructured":"Chaudhari S, Sardar V, Rahul DS, Chandan M, Shivakale MS, Harini KR (2021) Performance analysis of CNN, Alexnet and vggnet models for drought prediction using satellite images. In:\u00a02021 Asian Conference on Innovation in Technology (ASIANCON) (pp. 1\u20136). IEEE. https:\/\/doi.org\/10.1109\/ASIANCON51346.2021.9545068","DOI":"10.1109\/ASIANCON51346.2021.9545068"},{"issue":"5","key":"20928_CR27","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TPAMI.2020.3048268","volume":"44","author":"X Gu","year":"2020","unstructured":"Gu X, Angelov PP, Zhang C, Atkinson PM (2020) A semi-supervised deep rule-based approach for complex satellite sensor image analysis. IEEE Trans Pattern Anal Mach Intell 44(5):2281\u20132292. https:\/\/doi.org\/10.1109\/TPAMI.2020.3048268","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"20928_CR28","doi-asserted-by":"publisher","unstructured":"Li H, Hu B, Li Q, Jing L (2020) CNN-based tree species classification using airborne lidar data and high-resolution satellite image. In:\u00a0IGARSS 2020\u20132020 IEEE International Geoscience and Remote Sensing Symposium (pp. 2679\u20132682). IEEE. https:\/\/doi.org\/10.1109\/IGARSS39084.2020.9324011","DOI":"10.1109\/IGARSS39084.2020.9324011"},{"key":"20928_CR29","doi-asserted-by":"publisher","unstructured":"Senecal JJ, Sheppard JW, Shaw JA (2019) Efficient convolutional neural networks for multi-spectral image classification. In: 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1\u20138). IEEE. https:\/\/doi.org\/10.1109\/IJCNN.2019.8851840","DOI":"10.1109\/IJCNN.2019.8851840"},{"key":"20928_CR30","doi-asserted-by":"publisher","unstructured":"Rotich G, Aakur S, Minetto R, Segundo MP, Sarkar S (2018) Using semantic relationships among objects for geospatial land use classification. In: 2018 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) (pp. 1\u20137). IEEE. https:\/\/doi.org\/10.1109\/AIPR.2018.8707405","DOI":"10.1109\/AIPR.2018.8707405"},{"key":"20928_CR31","doi-asserted-by":"publisher","unstructured":"Campos A, Aboshehwa F, Li L, Zhang W (2020) Deep convolutional neural networks for road extraction. In: 2020 IEEE Green Energy and Smart Systems Conference (IGESSC) (pp. 1\u20135). IEEE. https:\/\/doi.org\/10.1109\/IGESSC50231.2020.9285011","DOI":"10.1109\/IGESSC50231.2020.9285011"},{"key":"20928_CR32","doi-asserted-by":"publisher","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907. https:\/\/doi.org\/10.48550\/arXiv.1609.02907","DOI":"10.48550\/arXiv.1609.02907"},{"key":"20928_CR33","doi-asserted-by":"publisher","first-page":"4325","DOI":"10.1109\/JSTARS.2020.3011333","volume":"13","author":"J Liang","year":"2020","unstructured":"Liang J, Deng Y, Zeng D (2020) A deep neural network combined CNN and GCN for remote sensing scene classification. IEEE J Sel Top Appl Earth Observations Remote Sens 13:4325\u20134338. https:\/\/doi.org\/10.1109\/JSTARS.2020.3011333","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sens"},{"issue":"15","key":"20928_CR34","doi-asserted-by":"publisher","first-page":"2914","DOI":"10.3390\/rs13152914","volume":"13","author":"C Cruz-Ramos","year":"2021","unstructured":"Cruz-Ramos C, Garcia-Salgado BP, Reyes-Reyes R, Ponomaryov V, Sadovnychiy S (2021) Gabor features extraction and land-cover classification of urban hyperspectral images for remote sensing applications. Remote Sens 13(15):2914. https:\/\/doi.org\/10.3390\/rs13152914","journal-title":"Remote Sens"},{"issue":"6","key":"20928_CR35","doi-asserted-by":"publisher","first-page":"951","DOI":"10.3390\/electronics11060951","volume":"11","author":"HC Chen","year":"2022","unstructured":"Chen HC, Widodo AM, Wisnujati A, Rahaman M, Lin JCW, Chen L, Weng CE (2022) AlexNet convolutional neural network for disease detection and classification of tomato leaf. Electronics 11(6):951. https:\/\/doi.org\/10.3390\/electronics11060951","journal-title":"Electronics"},{"key":"20928_CR36","doi-asserted-by":"publisher","unstructured":"Chen W, Ayoub M, Liao M, Shi R, Zhang M, Su F, Huang Z, Li Y, Wang Y, Wong KK (2023) A fusion of VGG-16 and ViT models for improving bone tumor classification in computed tomography. J Bone Oncol 43:100508. https:\/\/doi.org\/10.1016\/j.jbo.2023.100508","DOI":"10.1016\/j.jbo.2023.100508"},{"key":"20928_CR37","doi-asserted-by":"publisher","unstructured":"Chen LC, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587. https:\/\/doi.org\/10.48550\/arXiv.1706.05587","DOI":"10.48550\/arXiv.1706.05587"},{"issue":"9","key":"20928_CR38","doi-asserted-by":"publisher","first-page":"2019","DOI":"10.1111\/2041-210X.13043","volume":"9","author":"NJ Murray","year":"2018","unstructured":"Murray NJ, Keith DA, Simpson D, Wilshire JH, Lucas RM (2018) Remap: an online remote sensing application for land cover classification and monitoring. Methods Ecol Evol 9(9):2019\u20132027. https:\/\/doi.org\/10.1111\/2041-210X.13043","journal-title":"Methods Ecol Evol"},{"key":"20928_CR39","doi-asserted-by":"crossref","unstructured":"J\u00e9gou S, Drozdzal M, Vazquez D, Romero A, Bengio Y (2017) The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 11\u201319)","DOI":"10.1109\/CVPRW.2017.156"},{"key":"20928_CR40","doi-asserted-by":"publisher","first-page":"215943","DOI":"10.1109\/ACCESS.2020.3041645","volume":"8","author":"H Shao","year":"2020","unstructured":"Shao H, Li Y, Ding Y, Zhuang Q, Chen Y (2020) Land use classification using high-resolution remote sensing images based on structural topic model. IEEE Access 8:215943\u2013215955. https:\/\/doi.org\/10.1109\/ACCESS.2020.3041645","journal-title":"IEEE Access"},{"key":"20928_CR41","doi-asserted-by":"publisher","unstructured":"Schor L, Bacivarov I, Yang H, Thiele L (2014) Adapnet: adapting process networks in response to resource variations. In: Proceedings of the 2014 International Conference on Compilers, Architecture and Synthesis for Embedded Systems (pp. 1\u201310). https:\/\/doi.org\/10.1145\/2656106.2656112","DOI":"10.1145\/2656106.2656112"},{"key":"20928_CR42","doi-asserted-by":"publisher","first-page":"1509","DOI":"10.1109\/JSTARS.2020.3046245","volume":"14","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Jiang T, Liu C, Zhang L (2020) An effective classification method for hyperspectral image with very high resolution based on encoder\u2013decoder architecture. IEEE J Sel Top Appl Earth Observations Remote Sens 14:1509\u20131519. https:\/\/doi.org\/10.1109\/JSTARS.2020.3046245","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sens"},{"key":"20928_CR43","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"},{"issue":"10","key":"20928_CR44","doi-asserted-by":"publisher","first-page":"3680","DOI":"10.1109\/JSTARS.2018.2865187","volume":"11","author":"X Li","year":"2018","unstructured":"Li X, Yao X, Fang Y (2018) Building-a-nets: robust Building extraction from high-resolution remote sensing images with adversarial networks. IEEE J Sel Top Appl Earth Observations Remote Sens 11(10):3680\u20133687. https:\/\/doi.org\/10.1109\/JSTARS.2018.2865187","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sens"},{"key":"20928_CR45","doi-asserted-by":"publisher","first-page":"7881","DOI":"10.1109\/JSTARS.2021.3101203","volume":"14","author":"X Gao","year":"2021","unstructured":"Gao X, Chen T, Niu R, Plaza A (2021) Recognition and mapping of landslide using a fully convolutional densenet and influencing factors. IEEE J Sel Top Appl Earth Observations Remote Sens 14:7881\u20137894. https:\/\/doi.org\/10.1109\/JSTARS.2021.3101203","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sens"},{"issue":"1","key":"20928_CR46","doi-asserted-by":"publisher","first-page":"016520","DOI":"10.1117\/1.JRS.15.016520","volume":"15","author":"X Guo","year":"2021","unstructured":"Guo X, Chen Z, Wang C (2021) Fully convolutional densenet with adversarial training for semantic segmentation of high-resolution remote sensing images. J Appl Remote Sens 15(1):016520\u2013016520. https:\/\/doi.org\/10.1117\/1.JRS.15.016520","journal-title":"J Appl Remote Sens"},{"issue":"6","key":"20928_CR47","doi-asserted-by":"publisher","first-page":"230","DOI":"10.3390\/info12060230","volume":"12","author":"SD Khan","year":"2021","unstructured":"Khan SD, Alarabi L, Basalamah S (2021) Deep hybrid network for land cover semantic segmentation in high-spatial resolution satellite images. Information 12(6):230. https:\/\/doi.org\/10.3390\/info12060230","journal-title":"Information"},{"key":"20928_CR48","doi-asserted-by":"publisher","unstructured":"Sokolova M, Japkowicz N, Szpakowicz S (2006) Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: Australasian joint conference on artificial intelligence (pp. 1015\u20131021). Berlin, Heidelberg: Springer Berlin Heidelberg. https:\/\/doi.org\/10.1007\/11941439_114","DOI":"10.1007\/11941439_114"},{"key":"20928_CR49","doi-asserted-by":"publisher","unstructured":"Goutte C, Gaussier E (2005) A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: European conference on information retrieval (pp. 345\u2013359). Berlin, Heidelberg: Springer Berlin Heidelberg. https:\/\/doi.org\/10.1007\/978-3-540-31865-1_25","DOI":"10.1007\/978-3-540-31865-1_25"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20928-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-025-20928-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20928-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T22:34:42Z","timestamp":1765319682000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-025-20928-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,9]]},"references-count":49,"journal-issue":{"issue":"36","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["20928"],"URL":"https:\/\/doi.org\/10.1007\/s11042-025-20928-6","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,9]]},"assertion":[{"value":"28 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 May 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No ethics approval is required.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and human participation"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors declare that they have no conflict of interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}