{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T11:57:20Z","timestamp":1770465440674,"version":"3.49.0"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T00:00:00Z","timestamp":1690502400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T00:00:00Z","timestamp":1690502400000},"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":["Machine Vision and Applications"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s00138-023-01430-1","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T02:01:36Z","timestamp":1690509696000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["MFEMANet: an effective disaster image classification approach for practical risk assessment"],"prefix":"10.1007","volume":"34","author":[{"given":"Payal","family":"Bhadra","sequence":"first","affiliation":[]},{"given":"Avijit","family":"Balabantaray","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2603-5028","authenticated-orcid":false,"given":"Ajit Kumar","family":"Pasayat","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,28]]},"reference":[{"issue":"2","key":"1430_CR1","doi-asserted-by":"crossref","first-page":"285","DOI":"10.18280\/ijsse.100217","volume":"10","author":"D Salluri","year":"2020","unstructured":"Salluri, D., Bade, K., Madala, G.: Object detection using convolutional neural networks for natural disaster recovery. Int. J. Saf. Secur. Eng. 10(2), 285 (2020)","journal-title":"Int. J. Saf. Secur. Eng."},{"issue":"3","key":"1430_CR2","doi-asserted-by":"publisher","first-page":"3475","DOI":"10.1109\/JSYST.2020.2970157","volume":"14","author":"T Akram","year":"2020","unstructured":"Akram, T., Awais, M., Naqvi, R., Ahmed, A., Naeem, M.: Multicriteria uav base stations placement for disaster management. IEEE Syst. J. 14(3), 3475\u20133482 (2020)","journal-title":"IEEE Syst. J."},{"issue":"8","key":"1430_CR3","doi-asserted-by":"publisher","first-page":"2648","DOI":"10.3390\/s21082648","volume":"21","author":"M Aamir","year":"2021","unstructured":"Aamir, M., Ali, T., Irfan, M., Shaf, A., Azam, M.Z., Glowacz, A., Brumercik, F., Glowacz, W., Alqhtani, S., Rahman, S.: Natural disasters intensity analysis and classification based on multispectral images using multi-layered deep convolutional neural network. Sensors 21(8), 2648 (2021)","journal-title":"Sensors"},{"issue":"13","key":"1430_CR4","doi-asserted-by":"publisher","first-page":"17069","DOI":"10.1007\/s11042-017-5276-7","volume":"77","author":"L Lopez-Fuentes","year":"2018","unstructured":"Lopez-Fuentes, L., van de Weijer, J., Gonz\u2019alez-Hidalgo, M., Skinnemoen, H., Bagdanov, A.D.: Review on computer vision techniques in emergency situations. Multimedia Tools Appl. 77(13), 17069\u201317107 (2018)","journal-title":"Multimedia Tools Appl."},{"key":"1430_CR5","doi-asserted-by":"crossref","unstructured":"Kyrkou, C., Theocharides, T.: Deep-learning-based aerial image classification for emergency response applications using unmanned aerial vehicles. In: CVPR Workshops, pp. 517\u2013525 (2019)","DOI":"10.1109\/CVPRW.2019.00077"},{"issue":"14","key":"1430_CR6","doi-asserted-by":"publisher","first-page":"7547","DOI":"10.3390\/su13147547","volume":"13","author":"HS Munawar","year":"2021","unstructured":"Munawar, H.S., Ullah, F., Qayyum, S., Khan, S.I., Mojtahedi, M.: Uavs in disaster management: Application of integrated aerial imagery and convolutional neural network for flood detection. Sustainability 13(14), 7547 (2021)","journal-title":"Sustainability"},{"key":"1430_CR7","doi-asserted-by":"crossref","unstructured":"Osco, L.P., Junior, J.M., Ramos, A.P.M., Jorge, L.A.d.C., Fatholahi, S.N., Silva, J.d.A., Matsubara, E.T., Pistori, H., Gon\u00b8calves, W.N., Li, J.: A review on deep learning in uav remote sensing. arXiv preprint arXiv:2101.10861 (2021)","DOI":"10.1016\/j.jag.2021.102456"},{"issue":"5","key":"1430_CR8","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1109\/LGRS.2018.2810893","volume":"15","author":"T Wang","year":"2018","unstructured":"Wang, T., Sun, W., Qi, H., Ren, P.: Aerial image super resolution via wavelet multiscale convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 15(5), 769\u2013773 (2018)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"3","key":"1430_CR9","doi-asserted-by":"publisher","first-page":"231","DOI":"10.3390\/rs8030231","volume":"8","author":"A Vetrivel","year":"2016","unstructured":"Vetrivel, A., Gerke, M., Kerle, N., Vosselman, G.: Identification of structurally damaged areas in airborne oblique images using a visual-bagof-words approach. Remote Sens. 8(3), 231 (2016)","journal-title":"Remote Sens."},{"key":"1430_CR10","first-page":"1","volume":"26","author":"D Gonz'alez","year":"2019","unstructured":"Gonz\u2019alez, D., Patricio, M.A., Berlanga, A., Molina, J.M.: A super-resolution enhancement of uav images based on a convolutional neural network for mobile devices. Personal Ubiquit. Comput. 26, 1\u201312 (2019)","journal-title":"Personal Ubiquit. Comput."},{"key":"1430_CR11","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.isprsjprs.2015.03.016","volume":"105","author":"A Vetrivel","year":"2015","unstructured":"Vetrivel, A., Gerke, M., Kerle, N., Vosselman, G.: Identification of damage in buildings based on gaps in 3d point clouds from very high resolution oblique airborne images. ISPRS J. Photogramm. Remote. Sens. 105, 61\u201378 (2015)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"issue":"8","key":"1430_CR12","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.3390\/rs12081263","volume":"12","author":"Y Xiong","year":"2020","unstructured":"Xiong, Y., Guo, S., Chen, J., Deng, X., Sun, L., Zheng, X., Xu, W.: Improved srgan for remote sensing image super-resolution across locations and sensors. Remote Sens. 12(8), 1263 (2020)","journal-title":"Remote Sens."},{"key":"1430_CR13","doi-asserted-by":"crossref","unstructured":"Ma, W., Pan, Z., Guo, J., Lei, B.: Super-resolution of remote sensing images based on transferred generative adversarial network. In: IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, pp. 1148\u20131151. IEEE (2018)","DOI":"10.1109\/IGARSS.2018.8517442"},{"key":"1430_CR14","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.cmpb.2015.10.007","volume":"124","author":"J-J Yang","year":"2016","unstructured":"Yang, J.-J., Li, J., Shen, R., Zeng, Y., He, J., Bi, J., Li, Y., Zhang, Q., Peng, L., Wang, Q.: Exploiting ensemble learning for automatic cataract detection and grading. Comput. Methods Prog. Biomed. 124, 45\u201357 (2016)","journal-title":"Comput. Methods Prog. Biomed."},{"key":"1430_CR15","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.comnet.2017.05.021","volume":"124","author":"M Erdelj","year":"2017","unstructured":"Erdelj, M., Kr\u2019ol, M., Natalizio, E.: Wireless sensor networks and multiuav systems for natural disaster management. Comput. Netw. 124, 72\u201386 (2017)","journal-title":"Comput. Netw."},{"key":"1430_CR16","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.isprsjprs.2017.03.001","volume":"140","author":"A Vetrivel","year":"2018","unstructured":"Vetrivel, A., et al.: Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning. ISPRS J. Photogram. Remote Sens. 140, 45\u201359 (2018)","journal-title":"ISPRS J. Photogram. Remote Sens."},{"key":"1430_CR17","doi-asserted-by":"publisher","first-page":"8467","DOI":"10.1109\/TIP.2020.3016431","volume":"29","author":"S Li","year":"2020","unstructured":"Li, S., Yan, Q., Liu, P.: An efficient fire detection method based on multiscale feature extraction, implicit deep supervision and channel attention mechanism. IEEE Trans. Image Process. 29, 8467\u20138475 (2020)","journal-title":"IEEE Trans. Image Process."},{"issue":"15","key":"1430_CR18","doi-asserted-by":"publisher","first-page":"5920","DOI":"10.3390\/s22155920","volume":"22","author":"Z Hong","year":"2022","unstructured":"Hong, Z., et al.: Classification of building damage using a novel convolutional neural network based on post-disaster aerial images. Sensors 22(15), 5920 (2022)","journal-title":"Sensors"},{"issue":"9","key":"1430_CR19","doi-asserted-by":"publisher","first-page":"3163","DOI":"10.1007\/s00371-022-02535-w","volume":"38","author":"Z Ma","year":"2022","unstructured":"Ma, Z., et al.: Triple-strip attention mechanism-based natural disaster images classification and segmentation. Vis. Comput. 38(9), 3163\u20133173 (2022)","journal-title":"Vis. Comput."},{"key":"1430_CR20","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2020.07.005","volume":"415","author":"Y Li","year":"2020","unstructured":"Li, Y., et al.: Unsupervised domain adaptation with self-attention for post-disaster building damage detection. Neurocomputing 415, 27\u201339 (2020)","journal-title":"Neurocomputing"},{"issue":"9","key":"1430_CR21","doi-asserted-by":"publisher","first-page":"7296","DOI":"10.1109\/TGRS.2020.3033009","volume":"59","author":"X Peng","year":"2020","unstructured":"Peng, X., et al.: Optical remote sensing image change detection based on attention mechanism and image difference. IEEE Trans. Geosci. Remote Sens. 59(9), 7296\u20137307 (2020)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"1430_CR22","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1430_CR23","unstructured":"Kumar, A.D.: Novel deep learning model for traffic sign detection using capsule networks. arXiv:1805.04424 (2018)"},{"key":"1430_CR24","doi-asserted-by":"crossref","unstructured":"Pourashraf, P., Tomuro, N., Apostolova, E.: Genre-based image classification using ensemble learning for online flyers. In: Seventh International Conference on Digital Image Processing (ICDIP 2015), vol. 9631, p. 96310. International Society for Optics and Photonics (2015)","DOI":"10.1117\/12.2197138"},{"key":"1430_CR25","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.matdes.2018.11.060","volume":"162","author":"S Feng","year":"2019","unstructured":"Feng, S., Zhou, H., Dong, H.: Using deep neural network with small dataset to predict material defects. Mater. Des. 162, 300\u2013310 (2019)","journal-title":"Mater. Des."},{"issue":"6","key":"1430_CR26","doi-asserted-by":"publisher","first-page":"3813","DOI":"10.1109\/TGRS.2018.2888485","volume":"57","author":"S Zhou","year":"2019","unstructured":"Zhou, S., Xue, Z., Du, P.: Semisupervised stacked autoencoder with cotraining for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 57(6), 3813\u20133826 (2019)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"1430_CR27","doi-asserted-by":"publisher","first-page":"9021","DOI":"10.1109\/ACCESS.2017.2706363","volume":"5","author":"F Lv","year":"2017","unstructured":"Lv, F., Han, M., Qiu, T.: Remote sensing image classification based on ensemble extreme learning machine with stacked autoencoder. IEEE Access 5, 9021\u20139031 (2017)","journal-title":"IEEE Access"},{"issue":"47","key":"1430_CR28","doi-asserted-by":"publisher","first-page":"35425","DOI":"10.1007\/s11042-019-07839-z","volume":"79","author":"A Khamparia","year":"2020","unstructured":"Khamparia, A., Saini, G., Pandey, B., Tiwari, S., Gupta, D., Khanna, A.: Kdsae: chronic kidney disease classification with multimedia data learning using deep stacked autoencoder network. Multimedia Tools Appl. 79(47), 35425\u201335440 (2020)","journal-title":"Multimedia Tools Appl."},{"key":"1430_CR29","doi-asserted-by":"publisher","first-page":"1687","DOI":"10.1109\/JSTARS.2020.2969809","volume":"13","author":"C Kyrkou","year":"2020","unstructured":"Kyrkou, C., Theocharides, T.: Emergencynet: efficient aerial image classification for drone-based emergency monitoring using atrous convolutional feature fusion. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 13, 1687\u20131699 (2020)","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"issue":"1","key":"1430_CR30","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/MPRV.2017.11","volume":"16","author":"M Erdelj","year":"2017","unstructured":"Erdelj, M., Natalizio, E., Chowdhury, K.R., Akyildiz, I.F.: Help from the sky: leveraging uavs for disaster management. IEEE Pervasive Comput. 16(1), 24\u201332 (2017)","journal-title":"IEEE Pervasive Comput."},{"issue":"3","key":"1430_CR31","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1007\/s00502-010-0717-2","volume":"127","author":"M Quaritsch","year":"2010","unstructured":"Quaritsch, M., Kruggl, K., Wischounig-Strucl, D., Bhattacharya, S., Shah, M., Rinner, B.: Networked uavs as aerial sensor network for disaster management applications. e & I Elektrotechnik und Informationstechnik 127(3), 56\u201363 (2010)","journal-title":"e & I Elektrotechnik und Informationstechnik"},{"key":"1430_CR32","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: European Conference on Computer Vision, pp. 184\u2013199. Springer (2014)","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"1430_CR33","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646\u20131654 (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"1430_CR34","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz'ar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al.: Photorealistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681\u20134690 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"issue":"5","key":"1430_CR35","doi-asserted-by":"publisher","first-page":"810","DOI":"10.3390\/rs12050810","volume":"12","author":"P Burdziakowski","year":"2020","unstructured":"Burdziakowski, P.: Increasing the geometrical and interpretation quality of unmanned aerial vehicle photogrammetry products using super-resolution algorithms. Remote Sens. 12(5), 810 (2020)","journal-title":"Remote Sens."},{"key":"1430_CR36","doi-asserted-by":"crossref","unstructured":"Risojevi'c, V., Momi'c, S., Babi'c, Z.: Gabor descriptors for aerial image classification. In: International Conference on Adaptive and Natural Computing Algorithms, pp. 51\u201360. Springer (2011)","DOI":"10.1007\/978-3-642-20267-4_6"},{"issue":"3","key":"1430_CR37","doi-asserted-by":"publisher","first-page":"3357","DOI":"10.1007\/s11069-020-04133-2","volume":"103","author":"QD Cao","year":"2020","unstructured":"Cao, Q.D., Youngjun, C.: Building damage annotation on post-hurricane satellite imagery based on convolutional neural networks. Nat. Hazards 103(3), 3357\u20133376 (2020)","journal-title":"Nat. Hazards"},{"key":"1430_CR38","doi-asserted-by":"crossref","unstructured":"Zhang, D.Y., et al.: Crowd-assisted disaster scene assessment with human-ai interactive attention. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34. No. 03. (2020)","DOI":"10.1609\/aaai.v34i03.5658"},{"issue":"21","key":"1430_CR39","doi-asserted-by":"publisher","first-page":"4500","DOI":"10.3390\/app9214500","volume":"9","author":"VH Phung","year":"2019","unstructured":"Phung, V.H., Rhee, E.J., et al.: A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Appl. Sci. 9(21), 4500 (2019)","journal-title":"Appl. Sci."},{"key":"1430_CR40","doi-asserted-by":"publisher","first-page":"104603","DOI":"10.1109\/ACCESS.2020.2999816","volume":"8","author":"D Xue","year":"2020","unstructured":"Xue, D., Zhou, X., Li, C., Yao, Y., Rahaman, M.M., Zhang, J., Chen, H., Zhang, J., Qi, S., Sun, H.: An application of transfer learning and ensemble learning techniques for cervical histopathology image classification. IEEE Access 8, 104603\u2013104618 (2020)","journal-title":"IEEE Access"},{"key":"1430_CR41","doi-asserted-by":"publisher","first-page":"3892","DOI":"10.1109\/JSTARS.2020.3006879","volume":"13","author":"X Liu","year":"2020","unstructured":"Liu, X., Hu, Q., Cai, Y., Cai, Z.: Extreme learning machine-based ensemble transfer learning for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 13, 3892\u20133902 (2020)","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"issue":"3","key":"1430_CR42","doi-asserted-by":"publisher","first-page":"66","DOI":"10.3390\/drones5030066","volume":"5","author":"R Walambe","year":"2021","unstructured":"Walambe, R., Marathe, A., Kotecha, K.: Multiscale object detection from drone imagery using ensemble transfer learning. Drones 5(3), 66 (2021)","journal-title":"Drones"},{"key":"1430_CR43","doi-asserted-by":"publisher","first-page":"8297","DOI":"10.1109\/JSTARS.2021.3101511","volume":"14","author":"R Lei","year":"2021","unstructured":"Lei, R., Zhang, C., Liu, W., Zhang, L., Zhang, X., Yang, Y., Huang, J., Li, Z., Zhou, Z.: Hyperspectral remote sensing image classification using deep convolutional capsule network. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 14, 8297\u20138315 (2021)","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"issue":"23","key":"1430_CR44","doi-asserted-by":"publisher","first-page":"5632","DOI":"10.1080\/01431161.2016.1246775","volume":"37","author":"W Li","year":"2016","unstructured":"Li, W., Fu, H., Yu, L., Gong, P., Feng, D., Li, C., Clinton, N.: Stacked autoencoder-based deep learning for remote-sensing image classification: a case study of african land-cover mapping. Int. J. Remote Sens. 37(23), 5632\u20135646 (2016)","journal-title":"Int. J. Remote Sens."},{"key":"1430_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2019.101009","volume":"43","author":"Y Pi","year":"2020","unstructured":"Pi, Y., Nath, N.D., Behzadan, A.H.: Convolutional neural networks for object detection in aerial imagery for disaster response and recovery. Adv. Eng. Inform. 43, 101009 (2020)","journal-title":"Adv. Eng. Inform."},{"issue":"9","key":"1430_CR46","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1109\/TPAMI.2002.1033210","volume":"24","author":"S Baker","year":"2002","unstructured":"Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), 1167\u20131183 (2002)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1430_CR47","doi-asserted-by":"crossref","unstructured":"Tai, Y.-W., Liu, S., Brown, M.S., Lin, S.: Super resolution using edge prior and single image detail synthesis. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2400\u20132407. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5539933"},{"key":"1430_CR48","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.procs.2020.06.005","volume":"173","author":"R Gupta","year":"2020","unstructured":"Gupta, R., Sharma, A., Kumar, A.: Super-resolution using gans for medical imaging. Procedia Comput. Sci. 173, 28\u201335 (2020)","journal-title":"Procedia Comput. Sci."},{"issue":"1","key":"1430_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.52810\/TPRIS.2021.100005","volume":"1","author":"W Cai","year":"2021","unstructured":"Cai, W., et al.: Remote sensing image recognition based on multi-attention residual fusion networks. ASP Trans. Pattern Recognit. Intell. Syst. 1(1), 1\u20138 (2021)","journal-title":"ASP Trans. Pattern Recognit. Intell. Syst."},{"key":"1430_CR50","doi-asserted-by":"crossref","unstructured":"Meng, W., Tia, M.: Unmanned aerial vehicle classification and detection based on deep transfer learning. In: 2020 International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI), pp. 280\u2013285. IEEE (2020)","DOI":"10.1109\/ICHCI51889.2020.00067"},{"key":"1430_CR51","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"1430_CR52","doi-asserted-by":"crossref","unstructured":"Ahmad, F., Farooq, A., Ghani, M.U.: Deep ensemble model for classification of novel coronavirus in chest x-ray images. In: Computational Intelligence and Neuroscience 2021 (2021)","DOI":"10.1155\/2021\/8890226"},{"key":"1430_CR53","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision, pp. 630\u2013645. Springer (2016)","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"1430_CR54","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1430_CR55","doi-asserted-by":"crossref","unstructured":"Rezende, E., Ruppert, G., Carvalho, T., Ramos, F., De Geus, P.: Malicious software classification using transfer learning of resnet-50 deep neural network. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1011\u20131014. IEEE (2017)","DOI":"10.1109\/ICMLA.2017.00-19"},{"key":"1430_CR56","unstructured":"Evans, B.: Population-based ensemble learning with tree structures for classification (2019)"},{"issue":"13","key":"1430_CR57","doi-asserted-by":"publisher","first-page":"2445","DOI":"10.3390\/rs13132445","volume":"13","author":"X Ding","year":"2021","unstructured":"Ding, X., Li, Y., Yang, J., Li, H., Liu, L., Liu, Y., Zhang, C.: An adaptive capsule network for hyperspectral remote sensing classification. Remote Sens. 13(13), 2445 (2021)","journal-title":"Remote Sens."},{"key":"1430_CR58","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.imu.2018.12.001","volume":"14","author":"K Kruthika","year":"2019","unstructured":"Kruthika, K., Maheshappa, H., Initiative, A.D.N., et al.: Cbir system using capsule networks and 3d cnn for alzheimer\u2019s disease diagnosis. Inf. Med. Unlocked 14, 59\u201368 (2019)","journal-title":"Inf. Med. Unlocked"},{"issue":"6","key":"1430_CR59","doi-asserted-by":"publisher","first-page":"100","DOI":"10.3390\/jimaging7060100","volume":"7","author":"I Kandel","year":"2021","unstructured":"Kandel, I., Castelli, M., Popovi\u02c7c, A.: Comparing stacking ensemble techniques to improve musculoskeletal fracture image classification. J. Imaging 7(6), 100 (2021)","journal-title":"J. Imaging"},{"issue":"2","key":"1430_CR60","doi-asserted-by":"publisher","first-page":"181","DOI":"10.17694\/bajece.679662","volume":"8","author":"AA Abro","year":"2020","unstructured":"Abro, A.A., Tasci, E., Aybars, U.: A stacking-based ensemble learning method for outlier detection. Balkan J. Electr. Comput. Eng. 8(2), 181\u2013185 (2020)","journal-title":"Balkan J. Electr. Comput. Eng."},{"key":"1430_CR61","doi-asserted-by":"crossref","unstructured":"Niloy, F.F., Siddik Nayem, A.B., Sarker, A., Paul, O., Ashraful Amin, M., Ali, A.A., Zaber, M.I., Mahbubur Rahman, A.K.M.: A novel disaster image data-set and characteristics analysis using attention model. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 6116\u20136122. IEEE (2021)","DOI":"10.1109\/ICPR48806.2021.9412504"},{"issue":"3","key":"1430_CR62","doi-asserted-by":"publisher","first-page":"1220","DOI":"10.3390\/smartcities4030065","volume":"4","author":"HS Munawar","year":"2021","unstructured":"Munawar, H.S., Ullah, F., Qayyum, S., Heravi, A.: Application of deep learning on uav-based aerial images for flood detection. Smart Cities 4(3), 1220\u20131242 (2021)","journal-title":"Smart Cities"},{"key":"1430_CR63","doi-asserted-by":"crossref","unstructured":"Chowdhury, T., Murphy, R., Rahnemoonfar, M.: Rescuenet: a high resolution UAV semantic segmentation benchmark dataset for natural disaster damage assessment. arXiv:2202.12361\u00a0(2022)","DOI":"10.1109\/IGARSS47720.2021.9553712"},{"issue":"10","key":"1430_CR64","doi-asserted-by":"publisher","first-page":"636","DOI":"10.3390\/ijgi10100636","volume":"10","author":"Z Zou","year":"2021","unstructured":"Zou, Z., Gan, H., Huang, Q., Cai, T., Cao, K.: Disaster image classification by fusing multimodal social media data. ISPRS Int. J. Geo Inf. 10(10), 636 (2021)","journal-title":"ISPRS Int. J. Geo Inf."},{"key":"1430_CR65","doi-asserted-by":"crossref","unstructured":"Dinani, S.T., Caragea, D.: Disaster image classification using capsule networks. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9534448"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01430-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-023-01430-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01430-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T05:42:54Z","timestamp":1729834974000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-023-01430-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,28]]},"references-count":65,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["1430"],"URL":"https:\/\/doi.org\/10.1007\/s00138-023-01430-1","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,28]]},"assertion":[{"value":"6 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 July 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"76"}}