Extracting impervious surfaces from full polarimetric SAR images in different urban areas

References

• Ainsworth, T., D. Schuler, and J.-S. Lee. 2008. “Polarimetric SAR Characterization of Man-made Structures in Urban Areas Using Normalized Circular-pol Correlation Coefficients.” Remote Sensing of Environment 112: 2876–2885. doi:10.1016/j.rse.2008.02.005.
   Web of Science ®Google Scholar
• Arnold JR, C. L., and C. J. Gibbons. 1996. “Impervious Surface Coverage: The Emergence of a Key Environmental Indicator.” Journal of the American Planning Association 62: 243–258. doi:10.1080/01944369608975688.
   Web of Science ®Google Scholar
• Benedetti, A., M. Picchiani, and F. Del Frate. 2018. “Sentinel-1 and Sentinel-2 Data Fusion for Urban Change Detection.” IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, 1962–1965. IEEE. doi: 10.1109/IGARSS.2018.8517586.
   Google Scholar
• Brabec, E., S. Schulte, and P. L. Richards. 2002. “Impervious Surfaces and Water Quality: A Review of Current Literature and Its Implications for Watershed Planning.” Journal of Planning Literature 16: 499–514. doi:10.1177/088541202400903563.
   Web of Science ®Google Scholar
• Dekker, R. J. 2003. “Texture Analysis and Classification of ERS SAR Images for Map Updating of Urban Areas in the Netherlands.” IEEE Transactions on Geoscience Remote Sensing 41: 1950–1958. doi:10.1109/TGRS.2003.814628.
   Web of Science ®Google Scholar
• Dong, Y., B. Forster, and C. Ticehurst. 1997. “Radar Backscatter Analysis for Urban Environments.” International Journal of Remote Sensing 18: 1351–1364. doi:10.1080/014311697218467.
   Web of Science ®Google Scholar
• Espey JR, W. H., C. W. Morgan, and F. D. Masch. 1966. “Study of Some Effects of Urbanization on Storm Runoff from a Small Watershed.” Texas Water Development Board.
   Google Scholar
• Frost, V. S., J. A. Stiles, K. S. Shanmugan, and J. C. Holtzman. 1982. “A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise.” IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-4: 157–166. doi:10.1109/TPAMI.1982.4767223.
   Web of Science ®Google Scholar
• Gamba, P., and M. Aldrighi. 2012. “SAR Data Classification of Urban Areas by Means of Segmentation Techniques and Ancillary Optical Data.” IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing 5: 1140–1148. doi:10.1109/JSTARS.2012.2195774.
   Web of Science ®Google Scholar
• Guo, H., H. Yang, Z. Sun, X. Li, and C. Wang. 2014. “Synergistic Use of Optical and PolSAR Imagery for Urban Impervious Surface Estimation.” Photogrammetric Engineering and Remote Sensing 80: 91–102. doi:10.14358/PERS.80.1.91.
   Web of Science ®Google Scholar
• Haralick, R. M., and K. Shanmugam. 1973. “Textural Features for Image Classification.” IEEE Transactions on System, Man, Cybernetics: Systems SMC-3: 610–621. doi:10.1109/TSMC.1973.4309314.
   Web of Science ®Google Scholar
• Heumann, B. W. 2011. “An Object-based Classification of Mangroves Using a Hybrid Decision tree—Support Vector Machine Approach.” Remote Sensing of Environment 3: 2440–2460. doi:10.3390/rs3112440.
   Web of Science ®Google Scholar
• Huang, C., L. Davis, and J. Townshend. 2002. “An Assessment of Support Vector Machines for Land Cover Classification.” International Journal of Remote Sensing 23: 725–749. doi:10.1080/01431160110040323.
   Web of Science ®Google Scholar
• Jensen, J. R., and K. Lulla. 1987. “Introductory Digital Image Processing: A Remote Sensing Perspective.” Geocarto International 2: 65. doi:10.1080/10106048709354084.
   Google Scholar
• Jiang, L., M. Liao, H. Lin, and L. Yang. 2009. “Synergistic Use of Optical and InSAR Data for Urban Impervious Surface Mapping: A Case Study in Hong Kong.” International Journal of Remote Sensing 30: 2781–2796. doi:10.1080/01431160802555838.
   Web of Science ®Google Scholar
• Ju, J., S. Gopal, and E. D. Kolaczyk. 2005. “On the Choice of Spatial and Categorical Scale in Remote Sensing Land Cover Classification.” Remote Sensing of Environment 96: 62–77. doi:10.1016/j.rse.2005.01.016.
   Web of Science ®Google Scholar
• Leinenkugel, P., T. Esch, and C. Kuenzer. 2011. “Settlement Detection and Impervious Surface Estimation in the Mekong Delta Using Optical and SAR Remote Sensing Data.” Remote Sensing of Environment 115: 3007–3019. doi:10.1016/j.rse.2011.06.004.
   Web of Science ®Google Scholar
• Lin, Y., H. Zhang, G. Li, T. Wang, L. Wan, and H. Lin. 2019. “Improving Impervious Surface Extraction with Shadow-Based Sparse Representation from Optical, SAR, and LiDAR Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12: 2417–2428. doi:10.1109/JSTARS.2019.2907744.
   PubMed Web of Science ®Google Scholar
• Majd, M. S., E. Simonetto, and L. Polidori. 2012. “Maximum Likelihood Classification of Single High Resolution Polarimetric SAR Images in Urban Areas.” Photogrammetrie-Fernerkundung-Geoinformation 2012 (4): 395–407. doi:10.1127/1432-8364/20/0126.
   Google Scholar
• Marceau, D. J. 1999. “The Scale Issue in the Social and Natural Sciences.” Canadian Journal of Remote Sensing 25: 347–356. doi:10.1080/07038992.1999.10874734.
   Google Scholar
• Pellizzeri, T. M. 2003. “Classification of Polarimetric SAR Images of Suburban Areas Using Joint Annealed Segmentation and “H/A/α” Polarimetric Decomposition.” ISPRS Journal of Photogrammetry and Remote Sensing 58: 55–70. doi:10.1016/S0924-2716(03)00017-0.
   Web of Science ®Google Scholar
• Puissant, A., J. Hirsch, and C. Weber. 2005. “The Utility of Texture Analysis to Improve Per‐pixel Classification for High to Very High Spatial Resolution Imagery.” International Journal of Remote Sensing 26: 733–745. doi:10.1080/01431160512331316838.
   Web of Science ®Google Scholar
• Schueler, T. 1994. “The Importance of Imperviousness.” Watershed Protection Techniques 1: 100–101.
   Google Scholar
• Shimada, M., O. Isoguch, T. Tadono, R. Higuchi, and K. Isono. 2007. “PALSAR CALVAL Summary and Update 2007.” 2007 IEEE International Geoscience Remote Sensing Symposium, 3593–3596. IEEE. doi:10.1109/IGARSS.2007.4423622.
   Google Scholar
• Stasolla, M., and P. Gamba. 2008. “Spatial Indexes for the Extraction of Formal and Informal Human Settlements from High-resolution SAR Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 1 (2): 98–106. doi:10.1109/JSTARS.2008.921099.
   Web of Science ®Google Scholar
• Statistical Centre of Iran. 2016. “Population and Housing Censuses.” [Online]. Accessed 1 June 2019. https://www.amar.org.ir/english/Population-and-Housing-Censuses/
   Google Scholar
• Tison, C., J. M. Nicolas, F. Tupin, and H. Maître. 2004. “A New Statistical Model for Markovian Classification of Urban Areas in High-resolution SAR Images.” IEEE Transactions on Geoscience and Remote Sensing 42 (10): 2046–2057. doi:10.1109/TGRS.2004.834630.
   Web of Science ®Google Scholar
• Vapnik, V. 2013. The Nature of Statistical Learning Theory. New york: Springer Science and Business Media.
   Google Scholar
• Voisin, A., V. A. Krylov, G. Moser, S. B. Serpico, and J. Zerubia. 2012. “Classification of Very High Resolution SAR Images of Urban Areas Using Copulas and Texture in a Hierarchical Markov Random Field Model.” IEEE Geoscience Remote Sensing Letters 10: 96–100. doi:10.1109/LGRS.2012.2193869.
   Web of Science ®Google Scholar
• Wei, C., and T. Blaschke. 2018. “Pixel-Wise Vs. Object-based Impervious Surface Analysis from Remote Sensing: Correlations with Land Surface Temperature and Population Density.” Urban Science 2: 2. doi:10.3390/urbansci2010002.
   Google Scholar
• Weng, Q., D. Lu, and B. Liang. 2006. “Urban Surface Biophysical Descriptors and Land Surface Temperature Variations.” Photogrammetric Engineering and Remote Sensing 72: 1275–1286. doi:10.14358/PERS.72.11.1275.
   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: 311–332. doi:10.1016/0034-4257(87)90015-0.
   Web of Science ®Google Scholar
• Xu, R., H. Zhang, and H. Lin. 2017. “Urban Impervious Surfaces Estimation from Optical and SAR Imagery: A Comprehensive Comparison.” IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing 10: 4010–4021. doi:10.1109/JSTARS.2017.2706747.
   Web of Science ®Google Scholar
• Yanan, K. O. N. G., S. U. N. Genyun, A. ZHANG, and H. HUANG. 2018. “Synergistic Use of Optical and SAR Data with Multiple Kernel Learning for Impervious Surface Mapping.” 2018 Fifth International Workshop on Earth Observation and Remote Sensing Applications (EORSA), 1–4. IEEE. doi: 10.1109/EORSA.2018.8598552.
   Google Scholar
• Yang, F., B. Matsushita, and T. Fukushima. 2010. “A Pre-screened and Normalized Multiple Endmember Spectral Mixture Analysis for Mapping Impervious Surface Area in Lake Kasumigaura Basin, Japan.” ISPRS Journal of Photogrammetry and Remote Sensing 65: 479–490. doi:10.1016/j.isprsjprs.2010.06.004.
   Web of Science ®Google Scholar
• Yang, X. 2011. “Parameterizing Support Vector Machines for Land Cover Classification.” Photogrammetric Engineering and Remote Sensing 77: 27–37. doi:10.14358/PERS.77.1.27.
   Web of Science ®Google Scholar
• Yuan, F., and M. E. Bauer. 2007. “Comparison of Impervious Surface Area and Normalized Difference Vegetation Index as Indicators of Surface Urban Heat Island Effects in Landsat Imagery.” Remote Sensing of Environment 106: 375–386. doi:10.1016/j.rse.2006.09.003.
   Web of Science ®Google Scholar
• Zhang, H., H. Lin, Y. Li, Y. Zhang, and C. Fang. 2016. “Mapping Urban Impervious Surface with Dual-polarimetric SAR Data: An Improved Method.” Landscape and Urban Planning 151: 55–63. doi:10.1016/j.landurbplan.2016.03.009.
   Web of Science ®Google Scholar
• Zhang, H., H. Lin, and Y. Wang. 2018. “A New Scheme for Urban Impervious Surface Classification from SAR Images.” ISPRS Journal of Photogrammetry and Remote Sensing 139: 103–118. doi:10.1016/j.isprsjprs.2018.03.007.
   Web of Science ®Google Scholar
• Zhang, H., L. Wan, T. Wang, Y. Lin, H. Lin, and Z. Zheng. 2019. “Impervious Surface Estimation from Optical and Polarimetric SAR Data Using Small-Patched Deep Convolutional Networks: A Comparative Study.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. doi:10.1109/JSTARS.2019.2915277.
   PubMed Web of Science ®Google Scholar
• Zhang, H., Y. Zhang, and H. Lin. 2012. “A Comparison Study of Impervious Surfaces Estimation Using Optical and SAR Remote Sensing Images.” International Journal of Applied Earth Observation and Geoinformation 18: 148–156. doi:10.1016/j.jag.2011.12.015.
   Web of Science ®Google Scholar
• Zhu, Z., C. E. Woodcock, J. Rogan, and J. Kellndorfer. 2012. “Assessment of Spectral, Polarimetric, Temporal, and Spatial Dimensions for Urban and Peri-urban Land Cover Classification Using Landsat and SAR Data.” Remote Sensing of Environment 117: 72–82. doi:10.1016/j.rse.2011.07.020.
   Web of Science ®Google Scholar