{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:09:48Z","timestamp":1742918988781,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031064579"},{"type":"electronic","value":"9783031064586"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-06458-6_14","type":"book-chapter","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T15:07:58Z","timestamp":1652368078000},"page":"178-187","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic Change Detection Based on the Independent Component Analysis and Fuzzy C-Means Methods"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6608-2905","authenticated-orcid":false,"given":"Abdelkrim","family":"Maarir","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0233-1132","authenticated-orcid":false,"given":"Es-said","family":"Azougaghe","sequence":"additional","affiliation":[]},{"given":"Belaid","family":"Bouikhalene","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,13]]},"reference":[{"key":"14_CR1","unstructured":"Kongapai, P., Sompongchaiyakul, P., Jitpraphai, S.: Assessing coastal land cover changes after the 2004 tsunami using remote sensing and GIS approaches. Walailak J. Sci. Tech. 13(9), 9 (2016)"},{"key":"14_CR2","doi-asserted-by":"publisher","unstructured":"Nackaerts, K., Vaesen, K., Muys, B., Coppin, P.: Comparative performance of a modified change vector analysis in forest change detection. Int. J. Remote Sens. 26(5), 5 (2005). https:\/\/doi.org\/10.1080\/0143116032000160462","DOI":"10.1080\/0143116032000160462"},{"key":"14_CR3","doi-asserted-by":"publisher","unstructured":"Lu, D., Mausel, P., Batistella, M., Moran, E.: Land\u2010cover binary change detection methods for use in the moist tropical region of the Amazon: a comparative study. Int. J. Remote Sens. 26(1), 1 (2005). https:\/\/doi.org\/10.1080\/01431160410001720748","DOI":"10.1080\/01431160410001720748"},{"key":"14_CR4","doi-asserted-by":"publisher","unstructured":"Gong, M., Zhou, Z., Ma, J.: Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans. Image Process. 21(4), 4 (2012). https:\/\/doi.org\/10.1109\/TIP.2011.2170702","DOI":"10.1109\/TIP.2011.2170702"},{"key":"14_CR5","doi-asserted-by":"publisher","unstructured":"Hou, B., Wei, Q., Zheng, Y., Wang, S.: Unsupervised change detection in SAR image based on gauss-log ratio image fusion and compressed projection. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 7(8), 8 (2014). https:\/\/doi.org\/10.1109\/JSTARS.2014.2328344","DOI":"10.1109\/JSTARS.2014.2328344"},{"key":"14_CR6","doi-asserted-by":"publisher","unstructured":"Wang, Y., Du, L., Dai, H.: Unsupervised SAR image change detection based on SIFT keypoints and region information. IEEE Geosci. Remote Sens. Lett. 13(7), 7 (2016). https:\/\/doi.org\/10.1109\/LGRS.2016.2554606","DOI":"10.1109\/LGRS.2016.2554606"},{"issue":"4","key":"14_CR7","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1109\/LGRS.2009.2025059","volume":"6","author":"T Celik","year":"2009","unstructured":"Celik, T.: Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geosci. Remote Sens. Lett. 6(4), 772\u2013776 (2009). https:\/\/doi.org\/10.1109\/LGRS.2009.2025059","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"14_CR8","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/978-3-030-36674-2_29","volume-title":"Advanced Intelligent Systems for Sustainable Development (AI2SD\u20192019)","author":"A Maarir","year":"2020","unstructured":"Maarir, A., Ider, A.A., Bouikhalene, B.: Hierarchical dimensionality reduction based fuzzy c-means methods for change detection in temporal satellite images. In: Ezziyyani, M. (ed.) AI2SD 2019. AISC, vol. 1105, pp. 273\u2013286. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-36674-2_29"},{"key":"14_CR9","unstructured":"Jolliffe, I.T.: Principal Component Analysis. 2nd ed. Springer-Verlag, New York (2002). https:\/\/www.springer.com\/gp\/book\/9780387954424. Accessed 11 June 2019"},{"key":"14_CR10","volume-title":"Pattern Classification (2nd Edition)","author":"RO Duda","year":"2000","unstructured":"Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification (2nd Edition). Wiley-Interscience, New York (2000)"},{"key":"14_CR11","doi-asserted-by":"publisher","unstructured":"Sarp, G., Ozcelik, M.: Water body extraction and change detection using time series: a case study of Lake Burdur, Turkey. J. Taibah Univ. Sci. 11(3), 3 (2017). https:\/\/doi.org\/10.1016\/j.jtusci.2016.04.005","DOI":"10.1016\/j.jtusci.2016.04.005"},{"key":"14_CR12","doi-asserted-by":"publisher","unstructured":"Zhu, B., Gao, H., Wang, X., Xu, M., Zhu, X.: Change detection based on the combination of improved SegNet neural network and morphology, pp. 55\u201359, June 2018. https:\/\/doi.org\/10.1109\/ICIVC.2018.8492747","DOI":"10.1109\/ICIVC.2018.8492747"},{"key":"14_CR13","doi-asserted-by":"publisher","unstructured":"Izadi, M., Saeedi, P.: Automatic building detection in aerial images using a hierarchical feature based image segmentation. In: 2010 20th International Conference on Pattern Recognition, pp. 472\u2013475, August 2010. https:\/\/doi.org\/10.1109\/ICPR.2010.123","DOI":"10.1109\/ICPR.2010.123"},{"key":"14_CR14","unstructured":"Maarir, A., Bouikhalene, B., Chajri, Y.: Building detection from satellite images based on curvature scale space method. Walailak J. Sci. Technol. (WJST) 14(6), 517\u2013525 (2016). 10.14456\/vol14iss6pp%p"},{"issue":"10","key":"14_CR15","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1080\/2150704X.2018.1492172","volume":"9","author":"Q Wang","year":"2018","unstructured":"Wang, Q., Zhang, X., Chen, G., Dai, F., Gong, Y., Zhu, K.: Change detection based on Faster R-CNN for high-resolution remote sensing images. Remote Sens. Lett. 9(10), 923\u2013932 (2018). https:\/\/doi.org\/10.1080\/2150704X.2018.1492172","journal-title":"Remote Sens. Lett."},{"key":"14_CR16","doi-asserted-by":"publisher","first-page":"1272","DOI":"10.3390\/RS11111272","volume":"11","author":"S Pirasteh","year":"2019","unstructured":"Pirasteh, S., et al.: Developing an algorithm for buildings extraction and determining changes from airborne LiDAR, and comparing with R-CNN method from drone images. Remote. Sens. 11, 1272 (2019). https:\/\/doi.org\/10.3390\/RS11111272","journal-title":"Remote. Sens."},{"key":"14_CR17","doi-asserted-by":"publisher","unstructured":"Pang, S., Hu, X., Zhang, M., Cai, Z., Liu, F.: Co-segmentation and superpixel-based graph cuts for building change detection from bi-temporal digital surface models and aerial images. Remote Sens. 11(6), 6(2019). https:\/\/doi.org\/10.3390\/rs11060729","DOI":"10.3390\/rs11060729"},{"issue":"8","key":"14_CR18","doi-asserted-by":"publisher","first-page":"3384","DOI":"10.1109\/JSTARS.2016.2569598","volume":"9","author":"C Huo","year":"2016","unstructured":"Huo, C., Chen, K., Ding, K., Zhou, Z., Pan, C.: Learning relationship for very high resolution image change detection. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 9(8), 3384\u20133394 (2016). https:\/\/doi.org\/10.1109\/JSTARS.2016.2569598","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"issue":"4","key":"14_CR19","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1109\/JSTARS.2019.2899881","volume":"12","author":"M Che","year":"2019","unstructured":"Che, M., Gamba, P.: Intra-urban change analysis using sentinel-1 and nighttime light data. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 12(4), 1134\u20131142 (2019). https:\/\/doi.org\/10.1109\/JSTARS.2019.2899881","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"14_CR20","doi-asserted-by":"publisher","unstructured":"Ghosh, A., Mishra, N.S., Ghosh, S.: Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Inf. Sci. 181(4), 4 (2011). https:\/\/doi.org\/10.1016\/j.ins.2010.10.016","DOI":"10.1016\/j.ins.2010.10.016"},{"key":"14_CR21","doi-asserted-by":"publisher","unstructured":"Tomowski, D., Ehlers, M., Klonus, S.: Colour and texture based change detection for urban disaster analysis. In: 2011 Joint Urban Remote Sensing Event, April 2011, pp. 329\u2013332 (2011). https:\/\/doi.org\/10.1109\/JURSE.2011.5764786","DOI":"10.1109\/JURSE.2011.5764786"},{"issue":"17","key":"14_CR22","doi-asserted-by":"publisher","first-page":"17719","DOI":"10.1007\/s11042-015-2960-3","volume":"76","author":"W Gu","year":"2015","unstructured":"Gu, W., Lv, Z., Hao, M.: Change detection method for remote sensing images based on an improved Markov random field. Multimed. Tools App. 76(17), 17719\u201317734 (2015). https:\/\/doi.org\/10.1007\/s11042-015-2960-3","journal-title":"Multimed. Tools App."},{"key":"14_CR23","doi-asserted-by":"publisher","unstructured":"Li, Z., Shi, W., Zhang, H., Hao, M.: Change detection based on Gabor wavelet features for very high resolution remote sensing images. IEEE Geosci. Remote Sens. Lett. 14(5), 5 (2017). https:\/\/doi.org\/10.1109\/LGRS.2017.2681198","DOI":"10.1109\/LGRS.2017.2681198"},{"key":"14_CR24","doi-asserted-by":"publisher","unstructured":"Wu, C., Du, B., Cui, X., Zhang, L.: A post-classification change detection method based on iterative slow feature analysis and Bayesian soft fusion. Remote Sens. Environ. 199(Suppl C), 241\u2013255 (2017). https:\/\/doi.org\/10.1016\/j.rse.2017.07.009","DOI":"10.1016\/j.rse.2017.07.009"},{"key":"14_CR25","doi-asserted-by":"crossref","unstructured":"Sharma, A., Gulati, T.: Change detection from remotely sensed images based on stationary wavelet transform. Int. J. Elect. Comput. Eng. (IJECE) 7(6), 6 (2017)","DOI":"10.11591\/ijece.v7i6.pp3395-3401"},{"key":"14_CR26","doi-asserted-by":"publisher","unstructured":"Shao, P., Shi, W., He, P., Hao, M., Zhang, X.: Novel approach to unsupervised change detection based on a robust semi-supervised FCM clustering algorithm. Remote Sens. 8(3), 3 (2016). https:\/\/doi.org\/10.3390\/rs8030264","DOI":"10.3390\/rs8030264"},{"key":"14_CR27","unstructured":"Taati, A., Sarmadian, F., Mousavi, A., Pour, C.T.H., Shahir, A.H.E.: Land use classification using support vector machine and maximum likelihood algorithms by Landsat 5 TM images. Walailak J. Sci. Tech. 12(8), 8 (2015)"},{"key":"14_CR28","doi-asserted-by":"publisher","unstructured":"Goel, S., Verma, A., Goel, S., Juneja, K.: ICA in image processing: a survey. In: 2015 IEEE International Conference on Computational Intelligence Communication Technology, February 2015, pp. 144\u2013149 (2015). https:\/\/doi.org\/10.1109\/CICT.2015.91","DOI":"10.1109\/CICT.2015.91"},{"key":"14_CR29","unstructured":"Lim, J.S.: Two-dimensional signal and image processing (1990). Accessed 27 Sep 2017. http:\/\/adsabs.harvard.edu\/abs\/1990ph...book.....l"},{"key":"14_CR30","volume-title":"Extrapolation, Interpolation, and Smoothing of Stationary Time Series: With Engineering Applications","author":"N Wiener","year":"1964","unstructured":"Wiener, N.: Extrapolation, Interpolation, and Smoothing of Stationary Time Series: With Engineering Applications, vol. 8. MIT Press, Cambridge (1964)"},{"key":"14_CR31","doi-asserted-by":"publisher","unstructured":"Matteson, D.S., Tsay, R.S.: Independent component analysis via distance covariance. J. Am. Statist. Assoc. 112(518), 518 (2017). https:\/\/doi.org\/10.1080\/01621459.2016.1150851","DOI":"10.1080\/01621459.2016.1150851"},{"key":"14_CR32","doi-asserted-by":"publisher","unstructured":"Shen, H., Jegelka, S., Gretton, A.: Fast kernel-based independent component analysis. IEEE Trans. Signal Process. 57(9), 9 (2009). https:\/\/doi.org\/10.1109\/TSP.2009.2022857","DOI":"10.1109\/TSP.2009.2022857"},{"issue":"4","key":"14_CR33","doi-asserted-by":"publisher","first-page":"445","DOI":"10.2004\/wjst.v9i4.211","volume":"9","author":"MRM Amin","year":"2012","unstructured":"Amin, M.R.M., Bejo, S.K., Ismail, W.I.W., Mashohor, S.: Colour extraction of agarwood images for fuzzy c-means classification. Walailak J. Sci. Technol. (WJST) 9(4), 445\u2013459 (2012). https:\/\/doi.org\/10.2004\/wjst.v9i4.211","journal-title":"Walailak J. Sci. Technol. (WJST)"},{"key":"14_CR34","doi-asserted-by":"publisher","unstructured":"Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3(3), 3 (1973). https:\/\/doi.org\/10.1080\/01969727308546046","DOI":"10.1080\/01969727308546046"},{"key":"14_CR35","doi-asserted-by":"publisher","unstructured":"Yang, M.-S.: A survey of fuzzy clustering. Math. Comput. Modell. 18(11), 11 (1993). https:\/\/doi.org\/10.1016\/0895-7177(93)90202-A","DOI":"10.1016\/0895-7177(93)90202-A"},{"key":"14_CR36","doi-asserted-by":"publisher","unstructured":"Li, H.C., Celik, T., Longbotham, N., Emery, W.J.: Gabor feature based unsupervised change detection of multitemporal SAR images based on two-level clustering. IEEE Geosci. Remote Sens. Lett. 12(12), 12 (2015). https:\/\/doi.org\/10.1109\/LGRS.2015.2484220","DOI":"10.1109\/LGRS.2015.2484220"},{"key":"14_CR37","doi-asserted-by":"publisher","unstructured":"Gong, M., Su, L., Jia, M., Chen, W.: Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images. IEEE Trans. Fuzzy Syst. 22(1), 1 (2014). https:\/\/doi.org\/10.1109\/TFUZZ.2013.2249072","DOI":"10.1109\/TFUZZ.2013.2249072"}],"container-title":["Lecture Notes in Business Information Processing","Business Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06458-6_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:30:22Z","timestamp":1710329422000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06458-6_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031064579","9783031064586"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06458-6_14","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"type":"print","value":"1865-1348"},{"type":"electronic","value":"1865-1356"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"13 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CBI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Business Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Khouribga","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cbi2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.cbi-bm.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"68","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"34% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}