A regression modelling approach for optimizing segmentation scale parameters to extract buildings of different sizes

References

• Baatz, M., and A. SchäPe. 2000. “Multiresolution Segmentation: An Optimisation Approach for High Quality Multi-Scale Image Segmentation.” In Angewandte Geographische Informationsverarbeitung XII. Beiträge zum AGIT-Symposium Salzburg 2000, 12–23. Karlsruhe: Herbert Wichmann Verlag. 6 May 2013.
   Google Scholar
• Belgiu, M., and L. Drǎguţ. 2014. “Comparing Supervised and Unsupervised Multiresolution Segmentation Approaches for Extracting Buildings from Very High Resolution Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 96: 67–75. doi:10.1016/j.isprsjprs.2014.07.002.
   PubMed Web of Science ®Google Scholar
• Benz, U. C., P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Heynen. 2004. “Multi-Resolution, Object-Oriented Fuzzy Analysis of Remote Sensing Data for GIS-ready Information.” ISPRS Journal of Photogrammetry and Remote Sensing 58 (3–4): 239–258. doi:10.1016/j.isprsjprs.2003.10.002.
   Web of Science ®Google Scholar
• Blaschke, T. 2010. “Object Based Image Analysis for Remote Sensing.” ISPRS Journal of Photogrammetry and Remote Sensing 65 (1): 2–16. doi:10.1016/j.isprsjprs.2009.06.004.
   Web of Science ®Google Scholar
• Blaschke, T., G. J. Hay, M. Kelly, S. Lang, P. Hofmann, E. Addink, R. Q. Feitosa, 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
• Breiman, L. 1984. “Classification and Regression Trees.” The Wadsworth Statisticsprobability Series 19: 368.
   Google Scholar
• Clinton, N., A. Holt, J. Scarborough, L. Yan, and P. Gong. 2010. “Accuracy Assessment Measures for Object-Based Image Segmentation Goodness.” Photogrammetric Engineering & Remote Sensing 76 (3): 289–299. doi:10.14358/PERS.76.3.289.
   Web of Science ®Google Scholar
• Drǎguţ, L., D. Tiede, and S. R. Levick. 2010. “ESP: A Tool to Estimate Scale Parameter for Multiresolution Image Segmentation of Remotely Sensed Data.” International Journal of Geographical Information Science 24 (6): 859–871. doi:10.1080/13658810903174803.
   Web of Science ®Google Scholar
• Drăguţ, L., O. Csillik, C. Eisank, and D. Tiede. 2014. “Automated Parameterisation for Multi-Scale Image Segmentation on Multiple Layers.” ISPRS Journal of Photogrammetry and Remote Sensing 88: 119–127. doi:10.1016/j.isprsjprs.2013.11.018.
   PubMed Web of Science ®Google Scholar
• Drucker, H., C. J. C. Burges, L. Kaufman, A. Smola, and V. Vapnik. 1997. “Support Vector Regression Machines.” Advances in Neural Information Processing Systems 9 (x): 155–161.
   Google Scholar
• Duro, D. C., S. E. Franklin, and M. G. Dubé. 2012. “A Comparison of Pixel-Based and Object-Based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using SPOT-5 HRG Imagery.” Remote Sensing of Environment 118: 259–272. doi:10.1016/j.rse.2011.11.020.
   Web of Science ®Google Scholar
• Gholoobi, M., and L. Kumar. 2015. “Using Object-Based Hierarchical Classification to Extract Land Use Land Cover Classes from High-Resolution Satellite Imagery in a Complex Urban Area.” Journal of Applied Remote Sensing 9 (1): 096052. doi:10.1117/1.JRS.9.096052.
   Web of Science ®Google Scholar
• Grybas, H., L. Melendy, and R. G. Congalton. 2017. “A Comparison of Unsupervised Segmentation Parameter Optimization Approaches Using Moderate- and High-Resolution Imagery.” GIScience & Remote Sensing 54 (4): 515–533. doi:10.1080/15481603.2017.1287238.
   Web of Science ®Google Scholar
• Johnson, B., M. Bragais, I. Endo, D. Magcale-Macandog, and P. Macandog. 2015. “Image Segmentation Parameter Optimization considering Within- and Between-Segment Heterogeneity at Multiple Scale Levels: Test Case for Mapping Residential Areas Using Landsat Imagery.” ISPRS International Journal of Geo-Information 4 (4): 2292–2305. doi:10.3390/ijgi4042292.
   Web of Science ®Google Scholar
• Johnson, B., R. Tateishi, and N. Hoan. 2012. “Satellite Image Pansharpening Using a Hybrid Approach for Object-Based Image Analysis.” ISPRS International Journal of Geo-Information 1 (3): 228–241. doi:10.3390/ijgi1030228.
   Web of Science ®Google Scholar
• Johnson, B., and Z. Xie. 2011. “Unsupervised Image Segmentation Evaluation and Refinement Using a Multi-Scale Approach.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (4): 473–483. doi:10.1016/j.isprsjprs.2011.02.006.
   Web of Science ®Google Scholar
• Kim, M., and M. Madden. 2006. “Determination of Optimal Scale Parameter for Alliance Level Forest Classification of Multispectral Ikonos Images”. In Proceedings of the 1st International Conference on Object-based Image Analysis, Salzburg, Austria.
   Google Scholar
• Kim, M., M. Madden, and T. Warner. 2008. “Estimation of Optimal Image Object Size for the Segmentation of Forest Stands with Multispectral IKONOS Imagery.” In Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, edited by T. Blaschke, S. Lang, and G. J. Hay, 291–307. Berlin, Heidelberg: Springer Berlin Heidelberg.
   Google Scholar
• Möller, M., L. Lymburner, and M. Volk. 2007. “The Comparison Index: A Tool for Assessing the Accuracy of Image Segmentation.” International Journal of Applied Earth Observation and Geoinformation 9 (3): 311–321. doi:10.1016/j.jag.2006.10.002.
   Web of Science ®Google Scholar
• Myint, S. W., P. Gober, A. Brazel, S. Grossman-Clarke, and Q. Weng. 2011. “Per-Pixel vs. Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery.” Remote Sensing of Environment 115 (5): 1145–1161. doi:10.1016/j.rse.2010.12.017.
   Web of Science ®Google Scholar
• Smith, A. 2010. “Image Segmentation Scale Parameter Optimization and Land Cover Classification Using the Random Forest Algorithm.” Journal of Spatial Science 55 (1): 69–79. doi:10.1080/14498596.2010.487851.
   Web of Science ®Google Scholar
• Tian, J., and D.-M. Chen. 2007. “Optimization in Multi-Scale Segmentation of High-Resolution Satellite Images for Artificial Feature Recognition.” International Journal of Remote Sensing 28 (20): 4625–4644. doi:10.1080/01431160701241746.
   Web of Science ®Google Scholar
• Tong, H., T. Maxwell, Y. Zhang, and V. Dey. 2012. “A Supervised and Fuzzy-Based Approach to Determine Optimal Multi-Resolution Image Segmentation Parameters.” Photogrammetric Engineering & Remote Sensing 78 (10): 1029–1044. doi:10.14358/PERS.78.10.1029.
   Web of Science ®Google Scholar
• Whiteside, T. G., G. S. Boggs, and S. W. Maier. 2011. “Comparing Object-Based and Pixel-Based Classifications for Mapping Savannas.” International Journal of Applied Earth Observation and Geoinformation 13 (6): 884–893. doi:10.1016/j.jag.2011.06.008.
   Web of Science ®Google Scholar
• Witharana, C., and D. L. Civco. 2014. “Optimizing Multi-Resolution Segmentation Scale Using Empirical Methods: Exploring the Sensitivity of the Supervised Discrepancy Measure Euclidean Distance 2 (ED2).” ISPRS Journal of Photogrammetry and Remote Sensing 87: 108–121. doi:10.1016/j.isprsjprs.2013.11.006.
   Web of Science ®Google Scholar
• Witharana, C., and H. Lynch. 2016. “An Object-Based Image Analysis Approach for Detecting Penguin Guano in Very High Spatial Resolution Satellite Images.” Remote Sensing 8 (5): 375. doi:10.3390/rs8050375.
   Web of Science ®Google Scholar
• Woodcock, C. E., and A. H. Strahler. 1987. “The Factor of Scale in Remote Sensing.” Remote Sensing of Environment 21 (3): 311–332. doi:10.1016/0034-4257(87)90015-0.
   Web of Science ®Google Scholar
• Yan, G., J. F. Mas, B. H. P. Maathuis, Z. Xiangmin, and P. M. Van Dijk. 2006. “Comparison of Pixel-Based and Object-Oriented Image Classification Approaches—A Case Study in a Coal Fire Area, Wuda, Inner Mongolia, China.” International Journal of Remote Sensing 27 (18): 4039–4055. doi:10.1080/01431160600702632.
   Web of Science ®Google Scholar
• Zhang, X., X. Feng, P. Xiao, G. He, and L. Zhu. 2015. “Segmentation Quality Evaluation Using Region-Based Precision and Recall Measures for Remote Sensing Images.” ISPRS Journal of Photogrammetry and Remote Sensing 102: 73–84. doi:10.1016/j.isprsjprs.2015.01.009.
   Web of Science ®Google Scholar