A procedure for semi-automated cadastral boundary feature extraction from high-resolution satellite imagery

References

• Adenew, B. and Abdi, F., 2005. Land registration in Amhara region, Ethiopia. Addis Ababa, Ethiopia: International Institute for Environment and Developmen (IIED).
   Google Scholar
• Ali, Z. and Ahmed, S., 2013. Extracting parcel boundaries from satellite imagery for a Land Information System. In: 2013 6th International conference on recent advances in space technologies (RAST). Istanbul, Turkey: IEEE, 79–81. doi:10.1109/RAST.2013.6581319
   Google Scholar
• Artner, N., 2008. A comparison of mean shift tracking methods. In: 12th Central european seminar on computer graphics. Available from: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.309.5140&rep=rep1&type=pdf
   Google Scholar
• Awad, M.M., 2013. A morphological model for extracting road networks from high-resolution satellite images. Journal of Engineering, 2013, 1–9. doi:10.1155/2013/243021.
   Google Scholar
• Babawuro, U. and Beiji, Z., 2012. Satellite imagery cadastral features extractions using image processing algorithms: a viable option for cadastral science. IJCSI, 9 (4(2)). Available from: https://www.ijcsi.org/papers/IJCSI-9-4-2-30-38.pdf
   Google Scholar
• Bennett, R., Kitchingman, A., and Leach, J., 2010. On the nature and utility of natural boundaries for land and marine administration. Land Use Policy, 27 (3), 772–779. doi:10.1016/j.landusepol.2009.10.008
   Web of Science ®Google Scholar
• Comaniciu, D. and Meer, P., 1997. Robust analysis of feature spaces: color image segmentation. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition. C (8855), IEEE Comput. Soc., 750–755. doi:10.1109/CVPR.1997.609410
   Google Scholar
• Comaniciu, D. and Meer, P., 2002. Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (5), 603–619. doi:10.1109/34.1000236.
   Web of Science ®Google Scholar
• Dale, P.F. and Mclaughlin, J.D., 1988. Land information management: an introduction with special reference to cadastral problems in third world countries. New York, NY: Oxford University Press.
   Google Scholar
• Du, K., et al., 2012. Object tracking based on improved MeanShift and SIFT. In: 2012 2nd International conference on consumer electronics, communications and networks (CECNet). Yichang: IEEE, 2716–2719. doi:10.1109/CECNet.2012.6201691
   Google Scholar
• Eker, O. and Seker, D.Z., 2008. Semi-automatic extraction of features from digital imagery. ISPRS Congress, 37 (4), 443–446.
   Google Scholar
• Enemark, S., et al., 2014. Fit-for-purpose land administration. Copenhagen, Denmark: Joint FIG / World Bank Publication.
   Google Scholar
• FAO, 2012. Voluntary guidelines on the responsible governance of tenure of land, fisheries and forests in the context of national food security. Rome, Italy: Food and Agricultural Organization of the United Nation (FAO).
   Google Scholar
• FIG and World Bank, 1995. Statement on the Cadastre. (11). Copenhagen, Denmark: International Federation of Surveyors (FIG).
   Google Scholar
• Freedman, D. and Kisilev, P., 2009. Fast mean shift by compact density representation. In: 2009 IEEE computer society conference on computer vision and pattern recognition workshops, cvpr workshops 2009. Miami, FL: IEEE, 1818–1825. doi:10.1109/CVPRW.2009.5206716
   Google Scholar
• Gandhi, N.J., Shah, V.J., and Kshirsagar, R., 2014. Mean shift technique for image segmentation and modified canny edge detection algorithm for circle detection. In: 2014 International conference on communication and signal processing. Melmaruvathur: IEEE, 246–250. doi:10.1109/ICCSP.2014.6949838
   Google Scholar
• Gonzalez, R.C. and Woods, R.E., 2008. Digital image processing. 3rd ed. Upper Saddle River, NJ: Prentice Hall. doi:10.1049/ep.1978.0474.
   Google Scholar
• Heipke, C., et al., 1997. Evaluation of automatic road extraction. International Archives of Photogrammetry and Remote Sensing, 32 (3 SECT-4W2), 151–156.
   Google Scholar
• Horkaew, P., Puttinaovarat, S., and Khaimook, K., 2015. River boundary delineation from remote sensed imagery based on SVM and relaxation labeling of water index and DSM. Journal of Theoretical and Applied Information Technology, 71 (3), 376–386.
   Google Scholar
• Hu, X. and Tao, V., 2007. Automatic extraction of main road centerlines from high resolution satellite imagery using hierarchical grouping. Photogrammetric Engineering & Remote Sensing, 73 (9), 1049–1056.
   Web of Science ®Google Scholar
• Hyunil, Y. and Handon, J., 2012. An approach to effective land registration based on the satellite photogrammetry: case study in Baharly, Ahal Velayat, Turkmenistan. In: Innovative technology in land administration. Rome, Italy: International Federation of Surveyors (FIG).
   Google Scholar
• IAAO, 2015. Standard on digital cadastral maps and parcel identifiers. Kanas City, MO: International Association of Assessing Officers(IAAO).
   Google Scholar
• Jin, X. and Davis, C.H., 2005. Automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual, and spectral information. EURASIP Journal on Advances in Signal Processing, 2005 (14), 2196–2206. doi:10.1155/ASP.2005.2196.
   Web of Science ®Google Scholar
• Kaftan, J., Bell, A., and Aach, T., 2008. Mean shift segmentation evaluation of optimization techniques. Proc. Computer Vision Theory & Applications, 365–374. Available from: https://jaja.kn.vutbr.cz/~kajan/POV/MeanShiftSegmentationEvaluationofOptimizationTechniques.pdf
   Google Scholar
• Katiyar, S. and Arun, P., 2014. Comparative analysis of common edge detection techniques in context of object extraction. IEEE TGRS, 50 (11), 68–78. Available from: https://arxiv.org/abs/1405.6132
   Google Scholar
• Konecny, G., 2009. Cadastral mapping with earth observation technology. In: Geospatial Technology for Earth Observation. Boston, MA: Springer US, 397–409. doi:10.1007/978-1-4419-0050-0_15
   Google Scholar
• Kumar, M., et al., 2014. Road network extraction from high resolution multispectral satellite imagery based on object oriented techniques. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, II-8, 107–110. doi:10.5194/isprsannals-II-8-107-201410.5194/isprsannals-II-8-107-2014
   Google Scholar
• Liu, H. and Jezek, K.C., 2004. Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods. International Journal of Remote Sensing, 25 (5), 937–958. doi:10.1080/0143116031000139890.
   Web of Science ®Google Scholar
• Liu, W., et al., 2007. Image smoothing based on the mean shift algorithm. In: 2007 IEEE international conference on control and automation. Vol. 1, Guangzhou: IEEE, 1349–1353. doi:10.1109/ICCA.2007.4376580
   Google Scholar
• Mena, J.B., 2003. State of the art on automatic road extraction for GIS update: a novel classification. Pattern Recognition Letters, 24 (16), 3037–3058. doi:10.1016/S0167-8655(03)00164-8.
   Web of Science ®Google Scholar
• Navratil, G., 2011. Cadastral boundaries: benefits of complexity. URISA Journal, 23 (1), 19–27. Available from: https://www.scopus.com/inward/record.url?eid=2-s2.0-84855891788&partnerID=tZOtx3y1
   Google Scholar
• Nixon, M.S. and Aguado, A.S., 2012. Low-level feature extraction (including edge detection). In: Feature extraction & image processing for computer vision. Elsevier, 137–216. doi:10.1016/B978-0-12-396549-3.00004-5
   Google Scholar
• Parida, P.K., Sanabada, M.K., and Tripathi, S., 2014. Cadastral resurvey using high resolution satellite ortho image - challenges: a case study in Odisha, India. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-8 (8), 1165–1170. doi:10.5194/isprsarchives-XL-8-1165-201410.5194/isprsarchives-XL-8-1165-2014
   Google Scholar
• Qian, Y., Lu, G., and Chen, Z., 2000. Automatic extraction of valley boundary from dems in the hilly Loess Plateau. Chinese Geographical Science, 10 (3), 238–244. doi:10.1007/s11769-000-0007-8.
   Google Scholar
• Rao, S.S., et al., 2014. Assessing usefulness of high-resolution satellite imagery (HRSI) for re-survey of cadastral maps. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, II-8, 133–143. doi:10.5194/isprsannals-II-8-133-201410.5194/isprsannals-II-8-133-2014
   Google Scholar
• Solomon, A.H., 2005. Bridging the land rights demarcation gap in Ethiopia: usefulness of high resolution satellite image (HRSI) data. Vienna: University of Natural Resources and Applied Life Sciences.
   Google Scholar
• Tveite, H., 1999. An accuracy assessment method for geographical line data sets based on buffering. International Journal of Geographical Information Science, 13 (1), 27–47. doi:10.1080/136588199241445.
   Web of Science ®Google Scholar
• Ventura, A.D., Rampini, A., and Schettini, R., 1990. Image registration by recognition of corresponding structures. IEEE Transactions on Geoscience and Remote Sensing, 28 (3), 305–314. doi:10.1109/36.54357.
   Web of Science ®Google Scholar
• Zevenbergen, J., 2009. Land administration: to see the change from day to day. Enschede: International Institute for Geo-Information Science and Earth Observation.
   Google Scholar
• Zevenbergen, J. and Bennett, R., 2015. The visible boundary: more than just a line between coordinates. Available from: https://www.geotechrwanda2015.com/wp-content/uploads/2015/12/59_Jaap-Zevenbergen_bennett.pdf
   Google Scholar
• Zheng, L., Zhang, J., and Wang, Q., 2009. Mean-shift-based color segmentation of images containing green vegetation. Computers and Electronics in Agriculture, 65 (1), 93–98. doi:10.1016/j.compag.2008.08.002.
   Web of Science ®Google Scholar