{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T15:21:51Z","timestamp":1788448911773,"version":"build-2803163510"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T00:00:00Z","timestamp":1670371200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T00:00:00Z","timestamp":1670371200000},"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":["SIViP"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s11760-022-02394-x","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T12:04:53Z","timestamp":1670414693000},"page":"1831-1839","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi-source domain adaptation of GPR data for IED detection"],"prefix":"10.1007","volume":"17","author":[{"given":"Mehmet","family":"Oturak","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seniha Esen","family":"Yuksel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sefa","family":"Kucuk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,7]]},"reference":[{"key":"2394_CR1","unstructured":"Iraq coalition casualty count, deaths by IED (2009). https:\/\/web.archive.org\/web\/20090113201909\/. http:\/\/icasualties.org\/oif\/IED.aspx"},{"issue":"5","key":"2394_CR2","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1109\/TPAMI.2013.197","volume":"36","author":"T Tommasi","year":"2014","unstructured":"Tommasi, T., Orabona, F., Caputo, B.: Learning categories from few examples with multi model knowledge transfer. IEEE Trans. Pattern Anal. Mach. Intell 36(5), 928\u2013941 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"issue":"2","key":"2394_CR3","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1037\/0033-295X.94.2.115","volume":"94","author":"I Biederman","year":"1987","unstructured":"Biederman, I.: Recognition-by-components: a theory of human image understanding. Psychol. Rev. 94(2), 115 (1987)","journal-title":"Psychol. Rev."},{"key":"2394_CR4","doi-asserted-by":"publisher","unstructured":"Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., et al.: Generalizing to unseen domains: a survey on domain generalization. IEEE Trans. Knowl. Data Eng. (2022). https:\/\/doi.org\/10.1109\/TKDE.2022.3178128","DOI":"10.1109\/TKDE.2022.3178128"},{"issue":"10","key":"2394_CR5","doi-asserted-by":"publisher","first-page":"5308","DOI":"10.1109\/TNNLS.2021.3070085","volume":"33","author":"L Zhou","year":"2022","unstructured":"Zhou, L., Ye, M., Zhang, D., et al.: Prototype-based multisource domain adaptation. IEEE Trans. Neural Netw. Learn. Syst. 33(10), 5308\u20135320 (2022)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"12","key":"2394_CR6","doi-asserted-by":"publisher","first-page":"5588","DOI":"10.1109\/TNNLS.2020.2973293","volume":"31","author":"F Liu","year":"2020","unstructured":"Liu, F., Zhang, F., Lu, J.: Heterogeneous domain adaptation: an unsupervised approach. IEEE Trans. Neural Netw. Learn. Syst. 31(12), 5588\u20135602 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"2394_CR7","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.1109\/TIP.2019.2929421","volume":"29","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Tu, W., Du, B., et al.: Homologous component analysis for domain adaptation. IEEE Trans. Image Process. 29, 1074\u20131089 (2020)","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"2394_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3391229","volume":"11","author":"H Wu","year":"2020","unstructured":"Wu, H., Yan, Y., Ng, M.K., Zhang, L., Wu, Q.: Domain-attention conditional wasserstein distance for multi-source domain adaptation. ACM Trans. Intell. Syst. Technol. 11(4), 1\u201319 (2020)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"2394_CR9","doi-asserted-by":"crossref","unstructured":"Peng, X., Bai, Q., Xia, X., Huang, Z., et al.: Moment matching for multi-source domain adaptation. In: IEEE ICCV, pp. 1406\u20131415 (2019)","DOI":"10.1109\/ICCV.2019.00149"},{"key":"2394_CR10","doi-asserted-by":"crossref","unstructured":"Li, Y., Yuan, L., Chen, Y.: Dynamic transfer for multi-source domain adaptation. In: IEEE CVPR, pp. 10998\u201311007 (2021)","DOI":"10.1109\/CVPR46437.2021.01085"},{"key":"2394_CR11","doi-asserted-by":"publisher","first-page":"2122","DOI":"10.1109\/TIP.2022.3152052","volume":"31","author":"CX Ren","year":"2022","unstructured":"Ren, C.X., Liu, Y., Zhang, X., Huang, K.: Multi-source unsupervised domain adaptation via pseudo target domain. IEEE Trans. Image Process. 31, 2122\u20132135 (2022)","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"2394_CR12","first-page":"2810","volume":"34","author":"H Wu","year":"2022","unstructured":"Wu, H., Yan, Y., Lin, G., Yang, M., Ng, M.K., Wu, Q.: Iterative refinement for multi-source visual domain adaptation. IEEE Trans. Knowl. Data Eng. 34(6), 2810\u20132823 (2022)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"2394_CR13","doi-asserted-by":"crossref","unstructured":"Peng X., Saenko, K.: Synthetic to real adaptation with generative correlation alignment networks. In: IEEE WACV, pp. 1982\u20131991 (2018)","DOI":"10.1109\/WACV.2018.00219"},{"key":"2394_CR14","doi-asserted-by":"crossref","unstructured":"Pinheiro, P. O.: Unsupervised domain adaptation with similarity learning. In: IEEE CVPR, pp. 8004\u20138013 (2018)","DOI":"10.1109\/CVPR.2018.00835"},{"issue":"10","key":"2394_CR15","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"2394_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3(1), 1\u201340 (2016)","journal-title":"J. Big Data"},{"issue":"10","key":"2394_CR17","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1016\/j.conbuildmat.2005.06.007","volume":"19","author":"A Giannopoulos","year":"2005","unstructured":"Giannopoulos, A.: Modelling ground penetrating radar by GprMax. Constr. Build. Mater. 19(10), 755\u2013762 (2005)","journal-title":"Constr. Build. Mater."},{"key":"2394_CR18","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.cpc.2016.08.020","volume":"209","author":"C Warren","year":"2016","unstructured":"Warren, C., Giannopoulos, A., Giannakis, I.: gprMax: open source software to simulate electromagnetic wave propagation for ground penetrating radar. Comput. Phys. Commun. 209, 163\u2013170 (2016)","journal-title":"Comput. Phys. Commun."},{"issue":"8","key":"2394_CR19","doi-asserted-by":"publisher","first-page":"2254","DOI":"10.1109\/TGRS.2008.917211","volume":"46","author":"G Borgioli","year":"2008","unstructured":"Borgioli, G., Capineri, L., Falorni, P., et al.: The detection of buried pipes from time-of-flight radar data. IEEE Trans. Geosci. Remote Sens. 46(8), 2254\u20132266 (2008)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"2","key":"2394_CR20","doi-asserted-by":"publisher","DOI":"10.1117\/1.JRS.13.026503","volume":"13","author":"A Genc","year":"2019","unstructured":"Genc, A., Akar, G.B.: Combination of physics-based and image-based features for landmine identification in ground penetrating radar data. J. Appl. Remote Sens. 13(2), 026503 (2019)","journal-title":"J. Appl. Remote Sens."},{"issue":"12","key":"2394_CR21","doi-asserted-by":"publisher","first-page":"6766","DOI":"10.1109\/TGRS.2015.2447576","volume":"53","author":"SE Yuksel","year":"2015","unstructured":"Yuksel, S.E., Bolton, J., Gader, P.: Multiple-instance hidden Markov models with applications to landmine detection. IEEE Trans. Geosci. Remote Sens. 53(12), 6766\u20136775 (2015)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"2394_CR22","doi-asserted-by":"crossref","unstructured":"Torrione, P.A., Morton, K.D., Sakaguchi, R., et al.: Histograms of oriented gradients for landmine detection in ground-penetrating radar data. Trans. Geosci. Remote Sens. 52(3), 1539\u20131550 (2013)","DOI":"10.1109\/TGRS.2013.2252016"},{"key":"2394_CR23","doi-asserted-by":"crossref","unstructured":"Baydar, B., Akar, G.B., Yuksel, S.E., et al.: Fusion of KLMS and blob based pre-screener for buried landmine detection using ground penetrating radar. In: Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 9823, 98231D (2016)","DOI":"10.1117\/12.2223743"},{"key":"2394_CR24","doi-asserted-by":"crossref","unstructured":"Aydin E. and Y\u00fcksel, S.E.: Buried target detection with ground penetrating radar using deep learning method. In: IEEE SIU, pp. 1\u20134 (2017)","DOI":"10.1109\/SIU.2017.7960299"},{"key":"2394_CR25","doi-asserted-by":"crossref","unstructured":"Pham, M. T., Lef\u00e8vre, S.: Buried object detection from B-scan ground penetrating radar data using faster-RCNN. In: IEEE IGARSS, pp. 6804\u20136807 (2018)","DOI":"10.1109\/IGARSS.2018.8517683"},{"key":"2394_CR26","doi-asserted-by":"publisher","first-page":"1567","DOI":"10.1007\/s11760-018-1313-x","volume":"12","author":"U Ozkaya","year":"2018","unstructured":"Ozkaya, U., Seyfi, L.: Deep dictionary learning application in GPR B-scan images. SIVIP 12, 1567\u20131575 (2018)","journal-title":"SIVIP"},{"issue":"9","key":"2394_CR27","doi-asserted-by":"publisher","first-page":"6929","DOI":"10.1109\/TGRS.2019.2909665","volume":"57","author":"JM Malof","year":"2019","unstructured":"Malof, J.M., Reichman, D., Karem, A., et al.: A large-scale multi-institutional evaluation of advanced discrimination algorithms for buried threat detection in ground penetrating radar. IEEE Trans. Geosci. Remote Sens. 57(9), 6929\u20136945 (2019)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"8","key":"2394_CR28","doi-asserted-by":"publisher","first-page":"2560","DOI":"10.1109\/TGRS.2007.900993","volume":"45","author":"JN Wilson","year":"2007","unstructured":"Wilson, J.N., et al.: A large-scale systematic evaluation of algorithms using ground-penetrating radar for landmine detection and discrimination. IEEE Trans. Geosci. Remote Sens 45(8), 2560\u20132572 (2007)","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"issue":"1","key":"2394_CR29","first-page":"1","volume":"22","author":"Y Chen","year":"2021","unstructured":"Chen, Y.: A transfer learning model with multi-source domains for biomedical event trigger extraction. BMC Genomics 22(1), 1\u201318 (2021)","journal-title":"BMC Genomics"},{"key":"2394_CR30","doi-asserted-by":"crossref","unstructured":"Guo, J., Shah, D., Barzilay, R.: Multi-source domain adaptation with mixture of experts. In: EMNLP, pp. 4694\u20134703 (2018)","DOI":"10.18653\/v1\/D18-1498"},{"key":"2394_CR31","doi-asserted-by":"crossref","unstructured":"Rakshit, S., Tamboli, D., Meshram, P.S., et al.: Multi-source open-set deep adversarial domain adaptation. In: ECCV, pp. 735\u2013750 (2020)","DOI":"10.1007\/978-3-030-58574-7_44"},{"issue":"2","key":"2394_CR32","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis 60(2), 91\u2013110 (2004)","journal-title":"Int. J. Comput. Vis"},{"issue":"3","key":"2394_CR33","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"JA Suykens","year":"1999","unstructured":"Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293\u2013300 (1999)","journal-title":"Neural Process. Lett."},{"key":"2394_CR34","doi-asserted-by":"crossref","unstructured":"Cawley, G.C.: Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs. In: IEEE IJCNN, pp. 1661\u20131668 (2006)","DOI":"10.1109\/IJCNN.2006.246634"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02394-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-022-02394-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02394-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:15:24Z","timestamp":1684368924000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-022-02394-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,7]]},"references-count":34,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["2394"],"URL":"https:\/\/doi.org\/10.1007\/s11760-022-02394-x","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,7]]},"assertion":[{"value":"10 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 October 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 December 2022","order":4,"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 have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}