{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:21:00Z","timestamp":1740108060374,"version":"3.37.3"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T00:00:00Z","timestamp":1666656000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T00:00:00Z","timestamp":1666656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100012046","name":"Vietnam Academy of Science and Technology","doi-asserted-by":"crossref","award":["VAST01.07\/22-23"],"award-info":[{"award-number":["VAST01.07\/22-23"]}],"id":[{"id":"10.13039\/100012046","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s00521-022-07928-5","type":"journal-article","created":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T06:03:05Z","timestamp":1666677785000},"page":"4519-4548","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A new co-learning method in spatial complex fuzzy inference systems for change detection from satellite images"],"prefix":"10.1007","volume":"35","author":[{"given":"Le Truong","family":"Giang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6356-0046","authenticated-orcid":false,"given":"Le Hoang","family":"Son","sequence":"additional","affiliation":[]},{"given":"Nguyen Long","family":"Giang","sequence":"additional","affiliation":[]},{"given":"Tran Manh","family":"Tuan","sequence":"additional","affiliation":[]},{"given":"Nguyen Van","family":"Luong","sequence":"additional","affiliation":[]},{"given":"Mai Dinh","family":"Sinh","sequence":"additional","affiliation":[]},{"given":"Ganeshsree","family":"Selvachandran","sequence":"additional","affiliation":[]},{"given":"Vassilis C.","family":"Gerogiannis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,25]]},"reference":[{"key":"7928_CR1","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.3390\/rs9111135","volume":"9","author":"W Liu","year":"2017","unstructured":"Liu W, Jie Y, Zhao J, Le YA (2017) Novel method of unsupervised change detection using multi-temporal PolSAR images. Remote Sens 9:1135","journal-title":"Remote Sens"},{"key":"7928_CR2","first-page":"1","volume":"40","author":"W Ma","year":"2018","unstructured":"Ma W, Wu Y, Gong M, Xiong Y, Yang H, Hu T (2018) Change detection in SAR images based on matrix factorisation and a Bayes classifier. Int J Remote Sens 40:1\u201326","journal-title":"Int J Remote Sens"},{"issue":"6","key":"7928_CR3","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1080\/01431168908903939","volume":"10","author":"A Singh","year":"1989","unstructured":"Singh A (1989) Review article digital change detection techniques using remotely\u2014sensed data. Int J Remote Sens 10(6):989\u20131003","journal-title":"Int J Remote Sens"},{"issue":"12","key":"7928_CR4","doi-asserted-by":"publisher","first-page":"2365","DOI":"10.1080\/0143116031000139863","volume":"25","author":"D Lu","year":"2004","unstructured":"Lu D, Mausel P, Brondizio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25(12):2365\u20132401","journal-title":"Int J Remote Sens"},{"key":"7928_CR5","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.isprsjprs.2013.03.006","volume":"80","author":"M Hussain","year":"2013","unstructured":"Hussain M, Chen D, Cheng A, Wei H, Stanley D (2013) Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J Photogramm Remote Sens 80:91\u2013106","journal-title":"ISPRS J Photogramm Remote Sens"},{"issue":"15","key":"7928_CR6","doi-asserted-by":"publisher","first-page":"2460","DOI":"10.3390\/rs12152460","volume":"12","author":"Y You","year":"2020","unstructured":"You Y, Cao J, Zhou W (2020) A survey of change detection methods based on remote sensing images for multi-source and multi-objective scenarios. Remote Sens 12(15):2460","journal-title":"Remote Sens"},{"key":"7928_CR7","doi-asserted-by":"publisher","DOI":"10.1201\/9780429464348","volume-title":"Image analysis, classification, and change detection in remote sensing","author":"MJ Canty","year":"2019","unstructured":"Canty MJ (2019) Image analysis, classification, and change detection in remote sensing. Taylor & Francis Group, Abingdon-on-Thames. https:\/\/doi.org\/10.1201\/9780429464348"},{"issue":"10","key":"7928_CR8","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.3390\/rs12101688","volume":"12","author":"W Shi","year":"2020","unstructured":"Shi W, Zhang M, Zhang R, Chen S, Zhan Z (2020) Change detection based on artificial intelligence: state-of-the-art and challenges. Remote Sens 12(10):1688","journal-title":"Remote Sens"},{"issue":"9","key":"7928_CR9","doi-asserted-by":"publisher","first-page":"8228","DOI":"10.1109\/JIOT.2020.2984011","volume":"7","author":"M Zhang","year":"2020","unstructured":"Zhang M, Zhou Y, Quan W, Zhu J, Zheng R, Wu Q (2020) Online learning for IoT optimization: a Frank-Wolfe Adam-based algorithm. IEEE Internet Things J 7(9):8228\u20138237","journal-title":"IEEE Internet Things J"},{"key":"7928_CR10","doi-asserted-by":"publisher","unstructured":"Shen Z, Zhang Y, Lu J, Xu J, Xiao G (2018) SeriesNet: a generative time series forecasting model. In: 2018 international joint conference on neural networks (IJCNN), pp 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN.2018.8489522","DOI":"10.1109\/IJCNN.2018.8489522"},{"issue":"12","key":"7928_CR11","doi-asserted-by":"publisher","first-page":"9976","DOI":"10.1109\/TGRS.2019.2930682","volume":"57","author":"B Du","year":"2019","unstructured":"Du B, Ru L, Wu C, Zhang L (2019) Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images. IEEE Trans Geosci Remote Sens 57(12):9976\u20139992","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"12","key":"7928_CR12","doi-asserted-by":"publisher","first-page":"10157","DOI":"10.1007\/s00521-022-06999-8","volume":"34","author":"S Chu","year":"2022","unstructured":"Chu S, Li P, Xia M (2022) MFGAN: multi feature guided aggregation network for remote sensing image. Neural Comput Appl 34(12):10157\u201310173","journal-title":"Neural Comput Appl"},{"key":"7928_CR13","doi-asserted-by":"crossref","unstructured":"Nguyen CH, Nguyen TC, Tang TN, Phan NL (2021) Improving object detection by label assignment distillation. arXiv preprint, arXiv:2108.10520","DOI":"10.1109\/WACV51458.2022.00139"},{"key":"7928_CR14","doi-asserted-by":"crossref","unstructured":"Daudt RC, Le Saux, B, Boulch A, Gousseau Y (2018) Urban change detection for multispectral earth observation using convolutional neural networks. In IGARSS 2018\u20142018 IEEE international geoscience and remote sensing symposium. IEEE, pp 2115\u20132118","DOI":"10.1109\/IGARSS.2018.8518015"},{"key":"7928_CR15","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1007\/978-3-030-69143-1_7","volume-title":"Information and communication technology and applications. ICTA 2020. Communications in computer and information science","author":"SN Odaudu","year":"2021","unstructured":"Odaudu SN, Umoh IJ, Adedokun EA, Jonathan C (2021) LearnFuse: An efficient distributed big data fusion architecture using ensemble learning technique. In: Misra S, Muhammad-Bello B (eds) Information and communication technology and applications. ICTA 2020. Communications in computer and information science, vol 1350. Springer, Cham, pp 80\u201392. https:\/\/doi.org\/10.1007\/978-3-030-69143-1_7"},{"key":"7928_CR16","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1016\/j.eswa.2017.12.038","volume":"97","author":"D Qin","year":"2018","unstructured":"Qin D, Zhou X, Zhou W, Huang G, Ren Y, Horan B, He H, Kito N (2018) MSIM: a change detection framework for damage assessment in natural disasters. Expert Syst Appl 97:372\u2013383","journal-title":"Expert Syst Appl"},{"issue":"6","key":"7928_CR17","doi-asserted-by":"publisher","first-page":"3677","DOI":"10.1109\/TGRS.2018.2886643","volume":"57","author":"S Saha","year":"2019","unstructured":"Saha S, Bovolo F, Bruzzone L (2019) Unsupervised deep change vector analysis for multiple-change detection in VHR images. IEEE Trans Geosci Remote Sens 57(6):3677\u20133693","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"10","key":"7928_CR18","doi-asserted-by":"publisher","first-page":"1845","DOI":"10.1109\/LGRS.2017.2738149","volume":"14","author":"Y Zhan","year":"2017","unstructured":"Zhan Y, Fu K, Yan M, Sun X, Wang H, Qiu X (2017) Change detection based on deep siamese convolutional network for optical aerial images. IEEE Geosci Remote Sens Lett 14(10):1845\u20131849","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"7928_CR19","doi-asserted-by":"crossref","unstructured":"Cao Z et al (2020) Detection of small changed regions in remote sensing imagery using convolutional neural network. In: IOP conference series earth and environmental science, vol 502, p 012017","DOI":"10.1088\/1755-1315\/502\/1\/012017"},{"issue":"9","key":"7928_CR20","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.1109\/LGRS.2020.3005140","volume":"18","author":"R Liu","year":"2021","unstructured":"Liu R, Wang R, Huang J, Li J, Jiao L (2021) Change detection in SAR images using multiobjective optimization and ensemble strategy. IEEE Geosci Remote Sens Lett 18(9):1585\u20131589","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"4","key":"7928_CR21","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1109\/LGRS.2009.2025059","volume":"6","author":"T Celik","year":"2009","unstructured":"Celik T (2009) Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geosci Remote Sens Lett 6(4):772\u2013776","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"7928_CR22","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-030-79150-6_15","volume-title":"Artificial intelligence applications and innovations. AIAI 2021. IFIP advances in information and communication technology","author":"PK Saha","year":"2021","unstructured":"Saha PK, Logofatu D (2021) Efficient approaches for density-based spatial clustering of applications with noise. In: Maglogiannis I, Macintyre J, Iliadis L (eds) Artificial intelligence applications and innovations. AIAI 2021. IFIP advances in information and communication technology, vol 627. Springer, Cham, pp 184\u2013195. https:\/\/doi.org\/10.1007\/978-3-030-79150-6_15"},{"key":"7928_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107295","volume":"228","author":"C Wu","year":"2021","unstructured":"Wu C, Peng Q, Lee J, Leibnitz K, Xia Y (2021) Effective hierarchical clustering based on structural similarities in nearest neighbor graphs. Knowl-Based Syst 228:107295","journal-title":"Knowl-Based Syst"},{"issue":"4","key":"7928_CR24","first-page":"35","volume":"4","author":"S Ghosh","year":"2013","unstructured":"Ghosh S, Dubey SK (2013) Comparative analysis of k-means and fuzzy c-means algorithms. Int J Adv Comput Sci Appl 4(4):35\u201339","journal-title":"Int J Adv Comput Sci Appl"},{"issue":"7","key":"7928_CR25","doi-asserted-by":"publisher","first-page":"3033","DOI":"10.1109\/TCYB.2019.2905157","volume":"50","author":"D Zhang","year":"2019","unstructured":"Zhang D, Yao L, Chen K, Wang S, Chang X, Liu Y (2019) Making sense of spatio-temporal preserving representations for EEG-based human intention recognition. IEEE Trans Cybern 50(7):3033\u20133044","journal-title":"IEEE Trans Cybern"},{"issue":"5","key":"7928_CR26","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1109\/TNNLS.2019.2927224","volume":"31","author":"K Chen","year":"2019","unstructured":"Chen K, Yao L, Zhang D, Wang X, Chang X, Nie F (2019) A semisupervised recurrent convolutional attention model for human activity recognition. IEEE Trans Neural Netw Learn Syst 31(5):1747\u20131756","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7928_CR27","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Fandi\u00f1o J, Garea AS, Heras DB, Arg\u00fcello F (2018) Stacked autoencoders for multiclass change detection in hyperspectral images. In: Proceedings of the 2018 IEEE international geoscience and remote sensing symposium (IGARSS), pp 1906\u20131909","DOI":"10.1109\/IGARSS.2018.8518338"},{"issue":"12","key":"7928_CR28","doi-asserted-by":"publisher","first-page":"2255","DOI":"10.1049\/iet-ipr.2018.6248","volume":"13","author":"F Samadi","year":"2019","unstructured":"Samadi F, Akbarizadeh G, Kaabi H (2019) Change detection in SAR images using deep belief network: a new training approach based on morphological images. IET Image Proc 13(12):2255\u20132264","journal-title":"IET Image Proc"},{"issue":"11","key":"7928_CR29","doi-asserted-by":"publisher","first-page":"1382","DOI":"10.3390\/rs11111382","volume":"11","author":"D Peng","year":"2019","unstructured":"Peng D, Zhang Y, Guan H (2019) End-to-end change detection for high resolution satellite images using improved UNet++. Remote Sens 11(11):1382","journal-title":"Remote Sens"},{"issue":"2","key":"7928_CR30","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1109\/TCYB.2017.2647904","volume":"48","author":"M Luo","year":"2017","unstructured":"Luo M, Chang X, Nie L, Yang Y, Hauptmann AG, Zheng Q (2017) An adaptive semisupervised feature analysis for video semantic recognition. IEEE Trans Cybern 48(2):648\u2013660","journal-title":"IEEE Trans Cybern"},{"key":"7928_CR31","doi-asserted-by":"publisher","unstructured":"Mou L, Zhu XX (2018) A recurrent convolutional neural network for land cover change detection in multispectral images. In: IGARSS 2018\u20142018 IEEE international geoscience and remote sensing symposium, 2018, pp 4363\u20134366. https:\/\/doi.org\/10.1109\/IGARSS.2018.8517375","DOI":"10.1109\/IGARSS.2018.8517375"},{"key":"7928_CR32","doi-asserted-by":"crossref","unstructured":"Zheng Z, Ma A, Zhang L, Zhong Y (2021) Change is everywhere: single-temporal supervised object change detection in remote sensing imagery. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 15193\u201315202","DOI":"10.1109\/ICCV48922.2021.01491"},{"key":"7928_CR33","doi-asserted-by":"crossref","unstructured":"Xu G, Li H, Zang Y, Xie L, Bai C (2020) Change detection based on IR-MAD model for GF-5 remote sensing imagery. In: IOP conference series: materials science and engineering, vol 768, no 7. IOP Publishing, p 072073","DOI":"10.1088\/1757-899X\/768\/7\/072073"},{"key":"7928_CR34","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1016\/j.rse.2017.09.029","volume":"204","author":"SP Healey","year":"2018","unstructured":"Healey SP, Cohen WB, Yang Z, Kenneth Brewer C, Brooks EB, Gorelick N, Hernandez AJ, Huang C, Joseph Hughes M, Kennedy RE, Loveland TR, Moisen GG, Schroeder TA, Stehman SV, Vogelmann JE, Woodcock CE, Yang L, Zhu Z (2018) Mapping forest change using stacked generalization: an ensemble approach. Remote Sens Environ 204:717\u2013728","journal-title":"Remote Sens Environ"},{"issue":"5","key":"7928_CR35","doi-asserted-by":"publisher","first-page":"755","DOI":"10.3390\/rs10050755","volume":"10","author":"W Jiang","year":"2018","unstructured":"Jiang W, He G, Long T, Ni Y, Liu H, Peng Y, Lv K, Wang G (2018) Multilayer perceptron neural network for surface water extraction in Landsat 8 OLI satellite images. Remote Sens 10(5):755","journal-title":"Remote Sens"},{"key":"7928_CR36","doi-asserted-by":"publisher","first-page":"5536170","DOI":"10.1155\/2021\/5536170","volume":"2021","author":"C Sharma","year":"2021","unstructured":"Sharma C, Amandeep B, Sobti R, Lohani TK, Shabaz M (2021) A secured frame selection based video watermarking technique to address quality loss of data: Combining graph based transform, singular valued decomposition, and hyperchaotic encryption. Secur Commun Netw 2021:5536170","journal-title":"Secur Commun Netw"},{"issue":"3","key":"7928_CR37","doi-asserted-by":"publisher","first-page":"3987","DOI":"10.1109\/JSYST.2020.2966686","volume":"14","author":"MA Jarrahi","year":"2020","unstructured":"Jarrahi MA, Samet H, Ghanbari T (2020) Novel change detection and fault classification scheme for AC microgrids. IEEE Syst J 14(3):3987\u20133998","journal-title":"IEEE Syst J"},{"issue":"3","key":"7928_CR38","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1016\/j.rse.2005.09.008","volume":"99","author":"J Im","year":"2005","unstructured":"Im J, Jensen JR (2005) A change detection model based on neighborhood correlation image analysis and decision tree classification. Remote Sens Environ 99(3):326\u2013340","journal-title":"Remote Sens Environ"},{"issue":"3","key":"7928_CR39","doi-asserted-by":"publisher","first-page":"264","DOI":"10.3390\/rs8030264","volume":"8","author":"P Shao","year":"2016","unstructured":"Shao P, Shi W, He P, Hao M, Zhang X (2016) Novel approach to unsupervised change detection based on a robust semi-supervised FCM clustering algorithm. Remote Sens 8(3):264","journal-title":"Remote Sens"},{"issue":"9","key":"7928_CR40","doi-asserted-by":"publisher","first-page":"5057","DOI":"10.1109\/TGRS.2017.2702061","volume":"55","author":"H Zhang","year":"2017","unstructured":"Zhang H, Wang Q, Shi W, Hao M (2017) A novel adaptive fuzzy local information C-means clustering algorithm for remotely sensed imagery classification. IEEE Trans Geosci Remote Sens 55(9):5057\u20135068","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"18","key":"7928_CR41","doi-asserted-by":"publisher","first-page":"3750","DOI":"10.3390\/rs13183750","volume":"13","author":"R Shao","year":"2021","unstructured":"Shao R, Du C, Chen H, Li J (2021) SUNet: Change detection for heterogeneous remote sensing images from satellite and UAV using a dual-channel fully convolution network. Remote Sens 13(18):3750","journal-title":"Remote Sens"},{"issue":"3","key":"7928_CR42","doi-asserted-by":"publisher","first-page":"1790","DOI":"10.1109\/TGRS.2019.2948659","volume":"58","author":"B Hou","year":"2019","unstructured":"Hou B, Liu Q, Wang H, Wang Y (2019) From W-Net to CDGAN: bitemporal change detection via deep learning techniques. IEEE Trans Geosci Remote Sens 58(3):1790\u20131802","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"22","key":"7928_CR43","doi-asserted-by":"publisher","first-page":"3815","DOI":"10.3390\/rs12223815","volume":"12","author":"R Kou","year":"2020","unstructured":"Kou R, Fang B, Chen G, Wang L (2020) Progressive domain adaptation for change detection using season-varying remote sensing images. Remote Sens 12(22):3815","journal-title":"Remote Sens"},{"issue":"2","key":"7928_CR44","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1109\/91.995119","volume":"10","author":"D Ramot","year":"2002","unstructured":"Ramot D, Milo R, Friedman M, Kandel A (2002) Complex fuzzy sets. IEEE Trans Fuzzy Syst 10(2):171\u2013186","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"4","key":"7928_CR45","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1109\/TFUZZ.2003.814832","volume":"11","author":"D Ramot","year":"2003","unstructured":"Ramot D, Friedman M, Langholz G, Kandel A (2003) Complex fuzzy logic. IEEE Trans Fuzzy Syst 11(4):450\u2013461","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"4","key":"7928_CR46","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1109\/TFUZZ.2019.2961350","volume":"29","author":"G Selvachandran","year":"2021","unstructured":"Selvachandran G, Quek SG, Lan LTH, Son LH, Giang NL, Ding W, Abdel-Basset M, De Albuquerque VHC (2021) A new design of Mamdani complex fuzzy inference system for multi-attribute decision making problems. IEEE Trans Fuzzy Syst 29(4):716\u2013730","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"2","key":"7928_CR47","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","volume":"10","author":"JC Bezdek","year":"1984","unstructured":"Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2):191\u2013203","journal-title":"Comput Geosci"},{"key":"7928_CR48","unstructured":"Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: Proceedings of the 3rd international conference on learning representations (ICLR)"},{"issue":"1","key":"7928_CR49","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1080\/00401706.2000.10485983","volume":"42","author":"AE Hoerl","year":"2000","unstructured":"Hoerl AE, Kennard RW (2000) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 42(1):80\u201386","journal-title":"Technometrics"},{"issue":"1","key":"7928_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10489-016-0811-1","volume":"46","author":"LH Son","year":"2017","unstructured":"Son LH, Thong PH (2017) Some novel hybrid forecast methods based on picture fuzzy clustering for weather nowcasting from satellite image sequences. Appl Intell 46(1):1\u201315","journal-title":"Appl Intell"},{"key":"7928_CR51","unstructured":"National Oceanic and Atmospheric Administration (2015) MTSAT west color infrared loop. Retrieved from, https:\/\/www.star.nesdis.noaa.gov\/GOES\/index.php"},{"issue":"10","key":"7928_CR52","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.3390\/rs11101202","volume":"11","author":"M Ji","year":"2019","unstructured":"Ji M, Liu L, Du R, Buchroithner MF (2019) A comparative study of texture and convolutional neural network features for detecting collapsed buildings after earthquakes using pre- and post-event satellite imagery. Remote Sens 11(10):1202","journal-title":"Remote Sens"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07928-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07928-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07928-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,29]],"date-time":"2023-01-29T08:13:14Z","timestamp":1674979994000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07928-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,25]]},"references-count":52,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["7928"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07928-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,10,25]]},"assertion":[{"value":"16 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they do not have any conflict of interests. All authors have checked and agreed the submission.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}