Building change detection through multi-scale GEOBIA approach by integrating deep belief networks with fuzzy ontologies

References

• Agouris, P., Doucette, P., and Stefanidis, A., 2004. Automation and digital photogrammetric workstations. In: C. McGlone, E. Mikhail, and J. Bethel, eds. Manual of photogrammetry. 5th ed. Bethesda, MD: American Society of Photogrammetry and Remote Sensing. 949–981.
   Google Scholar
• Argialas, D. and Harlow, C., 1990. Computational image interpretation models: an overview and a perspective. Photogrammetric Engineering and Remote Sensing, 56 (6), 871–886.
   Web of Science ®Google Scholar
• Argialas, D., Michailidou, S., and Tzotsos, A., 2013. Change detection of buildings in suburban areas from high resolution satellite data developed through object based image analysis. Survey Review, 45 (333), 441–450. doi:10.1179/1752270613Y.0000000058
   Web of Science ®Google Scholar
• Argyridis, A. and Argialas, D., 2015. A fuzzy spatial reasoner for multi-scale GEOBIA ontologies. Photogrammetric Engineering & Remote Sensing, 81 (6), 491–498. doi:10.14358/PERS.81.6.491
   Web of Science ®Google Scholar
• Arvor, D., et al., 2013. Advances in geographic object-based image analysis with ontologies: a review of main contributions and limitations from a remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing, 82, 125–137. doi:10.1016/j.isprsjprs.2013.05.003
   Web of Science ®Google Scholar
• Baatz, M. and Schäpe, A., 2000. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. In: J. Strobl, T. Blaschke, and G. Griesebner, eds. Angewandte geographische informations - verarbeitung XII. Heindelberg: Wichmann-Verlag, 12–23.
   Google Scholar
• Bannour, H. and Hudelot, C., 2014. Building and using fuzzy multimedia ontologies for semantic image annotation. Multimedia Tools and Applications, 72 (3), 2107–2141. doi:10.1007/s11042-013-1491-z
   Web of Science ®Google Scholar
• Bao, J., et al., 2012. OWL 2 web ontology language document overview (Second Edition) [online]. Available from: http://www.w3.org/TR/owl2-overview/ [Accessed 19 December 2015].
   Google Scholar
• Belgiu, M., et al., 2014. Ontology-based classification of building types detected from airborne laser scanning data. Remote Sensing, 6 (2), 1347–1366. doi:10.3390/rs6021347
   Web of Science ®Google Scholar
• Belgiu, M. and Lampoltshammer, T., 2013. Ontology based interpretation of very high resolution imageries – grounding ontologies on visual interpretation keys. In: AGILE, ed. Sixteenth AGILE international conference on geographic information science, 14–17 May 2013 Leuven Belgium, AGILE, 1–5.
   Google Scholar
• Bengio, Y., et al., 2007. Greedy layer-wise training of deep networks. Advances in Neural Information Processing Systems, 19, 153–160.
   Google Scholar
• Bertrand de Beuvron, F., et al., 2013. From expert knowledge to formal ontologies for semantic interpretation of the urban environment from satellite images. International Journal of Knowledge-Based and Intelligent Engineering Systems, 17 (1), 55–65.
   Google Scholar
• Bishop, C.M., 2006. Pattern recognition and machine learning. Berlin, Heidelberg: Springer, 738.
   Google Scholar
• Blaschke, T., et al., 2014. Geographic object-based image analysis – towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 180–191. doi:10.1016/j.isprsjprs.2013.09.014
   PubMed Web of Science ®Google Scholar
• Bobillo, F. and Straccia, U., 2011. Fuzzy ontology representation using OWL 2. International Journal of Approximate Reasoning, 52 (7), 1073–1094. doi:10.1016/j.ijar.2011.05.003
   Web of Science ®Google Scholar
• Bouziani, M., Goïta, K., and He, D.-C., 2010. Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge. ISPRS Journal of Photogrammetry and Remote Sensing, 65 (1), 143–153. doi:10.1016/j.isprsjprs.2009.10.002
   Web of Science ®Google Scholar
• Brunner, D., Lemoine, G., and Bruzzone, L., 2010. Earthquake damage assessment of buildings using VHR optical and SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 48 (5), 2403–2420. doi:10.1109/TGRS.2009.2038274
   Web of Science ®Google Scholar
• Brusa, G., Caliusco, M., and Chiotti, O., 2006. A process for building a domain ontology: an experience in developing a government budgetary ontology. In: M. Orgun and T. Meyer, eds. Proceedings of the AOW second Australasian workshop on advances in ontologies, 5 December 2006 Hobalt, Australia. Darlinghurst: Australian Computer Society Inc., Vol. 72, 7–15.
   Google Scholar
• Deng, L. and Yu, D., 2014. Deep learning: methods and applications. Foundations and Trends in Signal Processing, 7 (3–4), 197–387. doi:10.1561/2000000039
   Google Scholar
• Ding, L., et al., 2007. Using ontologies in the semantic web: a survey. Integrated Series in Information Systems, 14, 79–113.
   Google Scholar
• Doxani, G., Karantzalos, K., and Tsakiri, M., 2012. Monitoring urban changes based on scale-space filtering and object-oriented classification. International Journal of Applied Earth Observation and Geoinformation, 15, 38–48. doi:10.1016/j.jag.2011.07.002
   Web of Science ®Google Scholar
• Durand, N., et al., 2007. Ontology-based object recognition for remote sensing image interpretation. In: IEEE, ed. 19th IEEE international conference on tools with artificial intelligence, ICTAI 2007, 29–31 October Patras. IEEE, Vol. 1, 472–479.
   Google Scholar
• Forestier, G., et al., 2012. Knowledge-based region labeling for remote sensing image interpretation. Computers, Environment and Urban Systems, 36 (5), 470–480. doi:10.1016/j.compenvurbsys.2012.01.003
   Web of Science ®Google Scholar
• Gruber, T., 1995. Toward principles for the design of ontologies used for knowledge sharing? International Journal of Human-Computer Studies, 43 (5–6), 907–928. doi:10.1006/ijhc.1995.1081
   Web of Science ®Google Scholar
• Guarino, N., 1997. Semantic matching: formal ontological distinctions for information organization, extraction, and integration. In: M. Pazienza, ed. Information extraction: a multidisciplinary approach to an emerging information technology. Berlin, Heidelberg: Springer, Vol. 1299, 139–170.
   Google Scholar
• Hebel, M., Arens, M., and Stilla, U., 2013. Change detection in urban areas by object-based analysis and on-the-fly comparison of multi-view ALS data. ISPRS Journal of Photogrammetry and Remote Sensing, 86, 52–64. doi:10.1016/j.isprsjprs.2013.09.005
   Web of Science ®Google Scholar
• Hinton, G.E., 2009. Deep belief networks [online]. Geoffrey E. Hinton. Available from: http://www.scholarpedia.org/article/Deep_belief_networks [Accessed 8 July 2015].
   Google Scholar
• Hinton, G.E., Osindero, S., and Teh, Y., 2006. A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527–1554. doi:10.1162/neco.2006.18.7.1527
   PubMed Web of Science ®Google Scholar
• Hinton, G.E. and Salakhutdinov, R.R., 2006. Reducing the dimensionality of data with neural networks. Science, 313 (5786), 504–507. doi:10.1126/science.1127647
   PubMed Web of Science ®Google Scholar
• Hofmann, P., et al., 2015. Towards a framework for agent-based image analysis of remote-sensing data. International Journal of Image and Data Fusion, 6 (2), 115–137. doi:10.1080/19479832.2015.1015459
   PubMed Web of Science ®Google Scholar
• Hudelot, C., Atif, J., and Bloch, I., 2008. Fuzzy spatial relation ontology for image interpretation. Fuzzy Sets and Systems, 159 (15), 1929–1951. doi:10.1016/j.fss.2008.02.011
   Web of Science ®Google Scholar
• Ioannidis, C., Psaltis, C., and Potsiou, C., 2009. Towards a strategy for control of suburban informal buildings through automatic change detection. Computers, Environment and Urban Systems, 33 (1), 64–74. doi:10.1016/j.compenvurbsys.2008.09.010
   Web of Science ®Google Scholar
• Janowicz, K., 2010. Semantic interoperability. In: B. Warf, ed. Encyclopedia of geography. London: SAGE Publications.
   Google Scholar
• Karantzalos, K., 2015. Recent advances on 2D and 3D change detection in urban environments from remote sensing data. In: M. Heibich, J. Arsanjiani, and M. Leitner, eds. Computational approaches for urban environments. Berlin, Heidelberg: Springer, 237–272.
   Google Scholar
• Karantzalos, K. and Argialas, D., 2006. Improving edge detection and watershed segmentation with anisotropic diffusion and morphological levellings. International Journal of Remote Sensing, 27 (24), 5427–5434. doi:10.1080/01431160600944010
   Web of Science ®Google Scholar
• Karantzalos, K., Argialas, D., and Paragios, N., 2007. Comparing morphological levelings constrained by different markers. In: C. Hendriks, G. Borgefors, and R. Strand, eds. Mathematical morphology and its applications to signal and image processing. Berlin, Heidelberg: Springer, 113–124.
   Google Scholar
• Kohli, D., et al., 2012. An ontology of slums for image-based classification. Computers, Environment and Urban Systems, 36 (2), 154–163. doi:10.1016/j.compenvurbsys.2011.11.001
   Web of Science ®Google Scholar
• Meyer, F., 2004. Levelings, image simplification filters for segmentation. International Journal of Mathematical Imaging and Vision, 20 (1/2), 59–72. doi:10.1023/B:JMIV.0000011319.21884.39
   Web of Science ®Google Scholar
• Meyer, F. and Maragos, P., 2000. Nonlinear scale-space representation with morphological levelings. Journal of Visual Communication and Image Representation, 11 (2), 245–265. doi:10.1006/jvci.1999.0447
   Web of Science ®Google Scholar
• Mnih, V. and Hinton, G.E., 2010. Learning to detect roads in high-resolution aerial images. In: K. Daniilidis, P. Maragos, and N. Paragios, eds. Lecture notes in computer science. Berlin, Heidelberg: Springer, Vol. 6316, 210–223.
   Google Scholar
• Paslaru, E., Simprel, B., and Temprich, C., 2006. Ontology engineering: a reality check, on the move to meaningful internet systems 2006. In: R. Meersman and Z. Tari, eds. CoopIS, DOA, GADA, and ODBASE. Berlin, Heidelberg: Springer, Vol. 4275, 836–854.
   Google Scholar
• PostgreSQL Global Development Group, 2014. PostgreSQL 9.3.0 documentation. PostgreSQL global development group [online]. Available from: http://www.postgresql.org/docs [Accessed 19 December 2015].
   Google Scholar
• QGIS Development Team, 2014. QGIS 2.6, geographic information system user guide. Open source geospatial foundation project [online]. Available from: http://www.qgis.org/en/docs/index [Accessed 19 December 2015].
   Google Scholar
• Smeulders, W.M.A., et al., 2000. Content-based image retrieval at the end of the early years. IEEE Transanction on Pattern Analysis and machine Intelligence, 22 (12), 1349–1380. doi:10.1109/34.895972
   Web of Science ®Google Scholar
• Tieleman, T., 2008. Training restricted boltzmann machines using approximations to the likelihood gradient. In: ACM, ed. Machine learning, proceedings of the 25th international conference ACM, 5-9 July 2008, Helsinki. New York, NY: ACM.
   Google Scholar
• Trimble, 2011. eCognition developer 8.64.0 reference book. München, Germany: Trimble, 206.
   Google Scholar
• Tzotsos, A. and Argialas, D., 2008. Support vector machine classification for object-based image analysis. In: T. Blaschke, S. Lang, and G. Hay, eds. Object-based image analysis, lecture notes in geoinformation and cartography. Berlin, Heidelberg: Springer, 663–677.
   Google Scholar
• Wang, H., Cai, Y., and Chen, L., 2014. A vehicle detection algorithm based on deep belief network. Scientific World Journal [Online]. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4052056/#B11 [Accessed 19 December 2015].
   Google Scholar
• Wang, H., Liu, S., and Chia, L., 2006. Does ontology help in image retrieval? — a comparison between keyword, text ontology and multi-modality ontology approaches. In: ACM, ed. 14th annual ACM international conference on multimedia, 23–27 October 2006 Santa Barbara, CA. New York, NY: ACM, 109–112.
   Google Scholar
• Wurm, M., et al., 2011. Object-based image information fusion using multisensor earth observation data over urban areas. International Journal of Image and Data Fusion, 2 (2), 121–147. doi:10.1080/19479832.2010.543934
   Google Scholar
• Zadeh, L., 1965. Fuzzy sets. Information and Control, 8 (3), 338–353. doi:10.1016/S0019-9958(65)90241-X
   Google Scholar