Urban Classification Using Multi-temporal Sentinel-1 Data Based On Coherence Characteristics
Main Article Content
Abstract
The paper presents the method of urban classification using the coherence characteristics of pairs of SAR images observed at different times. Two scenes of Sentinel-1A VV and VH polarized on January 16, 2020, and January 28, 2020, in some central districts of Hanoi city were used experimentally in this study. The primary data processing steps included: (1) Creating the coherence image by using a pair of SAR interference images; (2) Processing coherence image by computing multi-look and geometric correction to UTM coordinate system; (3) Classification of the coherence image to urban/non-urban areas threshold method. The results showed that the urban extracted from the VH polarization image was better than the VV polarization image. The overall accuracy of classification achieved for VV and VH polarized images were 89% and 93%. Using SAR image pairs to classify urban areas that were not affected by weather conditions, showed good efficiency in managing and monitoring urban space in Vietnam cities.
Keywords:
Sentinel-1, coherence, urban areas, SAR image
References
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[2] C. Sun, Z. Wu, Z. Lv, N. Yao, J. Wei, Quantifying different types of urban growth and the change dynamic in Guangzhou using multi-temporal remote sensing data, International Journal of Applied Earth Observation and Geoinformation, 21 (2013) 409-417. https://doi.org/10.1016/j.jag.2011.12.012
[3] A. R. As-syakur, W. S. Adnyana, W. Arthana, W. Nuarsa, Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area, Remote Sensing, 4 (2012), 2957-2970. https://doi:10.3390/rs4102957.
[4] C. He, P. Shi, D. Xie, Y. Zhao, Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach, Remote Sensing Letters, 1 (2010) 213-221. https://doi.org/10.1080/01431161.2010.481681.
[5] H. Xu, A new index for delineating built‐up land features in satellite imagery, International Journal of Remote Sensing, 29 (2008), 4269-4276, http://dx.doi.org/10.1080/01431160802039957
[6] Y. Zha, J. Gao, S. Ni, Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, International Journal of Remote Sensing, 24 (2003) 583-594. http://dx.doi.org/10.1080/01431160304987.
[7] H. Zhao, X. Chen, Use of Normalized Difference Bareness Index in Quickly Mapping Bare Areas from TM/ETM+, International Geoscience and Remote Sensing Symposium (IGARSS) 3 (2005) 1666 – 1668, http://10.1109/IGARSS.2005.1526319.
[8] C. Corbane, G. Lemoine, M. Pesaresi, T. Kemper, F. Sabo, S. Ferri, V. Syrris, Enhanced automatic detection of human settlements using Sentinel-1 interferometric coherence, International Journal of Remote Sensing, 39 (2017) 842-853. https://doi.org/10.1080/01431161.2017.1392642.
[9] F. Vicente-Guijalba, J. Duro, C. Notarnicola, A. Jacob, R. Sonnenschein, J.J. Mallorquí, C. López-Martínez, J.M. Lopez-Sanchez, Assessing hypertemporal Sentinel-1 coherence maps for land cover monitoring, In Proceedings of the 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), IEEE, Belgium, 2017, https://doi: 10.1109/Multi-Temp.2017.8035240.
[10] L. Bruzzone, M. Marconcini, U. Wegmuller, A. Wiesmann, An Advanced System for the Automatic Classification of Multitemporal SAR Images, IEEE Transactions on Geoscience and Remote Sensing, 42 (2004) 1321–1334, https://doi: 10.1109/TGRS.2004.826821.
[11] M. Chini, R. Pelich, R. Hostache, P. Matgen, Built-up areas mapping at global scale based on adapative parametric thresholding of Sentinel-1 intensity & coherence time series, In Proceedings of the 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), IEEE, Belgium, 2017, https://doi: 10.1109/Multi-Temp.2017.8035258.
[12] P. Washaya, T. Balz, B. Mohamadi, Coherence Change-Detection with Sentinel-1 for Natural and Anthropogenic Disaster Monitoring in Urban Areas, Remote Sensing, 10 (2018) 1-22. https://doi.org/10.3390/rs10071026.
[13] T.L. Hung, Urban Bare Land Classification Using NDBaI Index Based on Combination of Sentinel 2 MSI and Landsat 8 Multiresolution Images, VNU Journal of Science: Earth and Environmental Sciences, 36 (2020) 68-78 (in Vietnamese). https://doi.org/10.25073/2588-1094/vnuees.4537.
[14] N.H.K. Linh, Automatic creation of urban land distribution maps using IBI index from Landsat TM image: Case study in Hue city, Thua Thien Hue Province, GIS conference, (2011) 205-212 (in Vietnamese).
[15] N.T. Hien, Evaluate the accuracy of extracting construction land and bare land in urban areas from remote sensing images by index images, experiment in Hanoi, Master thesis, Hanoi University of Natural Resources and Environment, Hanoi, 2018 (in Vietnamese).
[16] N.B. Duy, Studying on the Interferometry SAR (InSAR) technique for Digital Elevation Model (DEM) generation using Open source Software NEST and SNAPHU, Can Tho University Journal of Science, 36 (2015) 77-87 (in Vietnamese).
[17] D.V. Khac, N.C. Kien, D.M. Tam, Applying RADAR interference method to determine land subsidence in the urban center of Hanoi city, Journal of Science and Technology in Civil Engineering, 2 (2015) 61-68 (in Vietnamese).
[18] L.V. Trung, H.T.M. Dinh, Measuring ground subsidence in Ho Chi Minh city using differential InSAR techniques, Science and Technology Development Journal, 11 (2008) 121-130 (in Vietnamese).
[19] K. Clauss, M. Ottinger, P. Leinenkugel, C. Kuenzer, Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data, International Journal of Applied Earth Observation and Geoinformation, 73 (2018) 574-585. https://doi.org/10.1016/j.jag.2018.07.022.
[20] H. P. Phung, L. D. Nguyen, N. H. Thong, L. T. Thuy, A. A. Apan, Monitoring rice growth status in the Mekong Delta, Vietnam using multitemporal Sentinel-1 data, Journal of Applied Remote Sensing, 14 (2020) 1-23. https://doi.org/10.1117/1.JRS Sentinel-1.
[21] L.M. Hang, V.V. Truong, N.D. Duong, T.A. Tuan, Mapping land cover using multi-temporal sentinel-1a data: A case study in Hanoi, Vietnam Journal of Earth Sciences, 39 (2017) 345-359. https://doi.org/10.15625/0866-7187/39/4/10730.
[22] R. Bamler, P. Hartl, Synthetic Aperture Radar Interferometry, Inverse Problems, 14 (1998) 1-54.
[23] A. Ferretti, A. Monti-Guarnieri, C. Prati, F. Rocca, InSAR Principles: Guidelines for SAR Interferometry Processing and Interpretation, TM-19, ESA Publications, The Netherlands, 2007.
[24] I.H. Woodhouse, Introduction to Microwave Remote Sensing, Taylor and Francis, USA, 2005.
[25] C. Lopez-Martinez, X. Fabregas, E. Pottier, A new Alternative for SAR Imagery Coherence Estimation, In Proceedings of the 5th European Conference on Synthetic Aperture Radar (EUSAR’04), Germany, 2004.
[26] B. Kampes, S. Usai, In Doris: The delft object-oriented radar interferometric software, In Proceedings of the 2nd International Symposium on Operationalization of Remote Sensing, Deft University of Technology, The Netherlands, 1999.
[27] U. Wegmuller, C.L. Werner, SAR Interferometric Signatures of Forest, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 33 (1995) 1153-1161. https://doi.org/10.1109/36.469479.
[28] S. Usai, An Analysis of the Interferometric Characteristics of Anthropogenic Features, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 38 (2000), 1192-1197, https://doi.org/10.1109/36.843050.
[29] N. Otsu, A threshold selection method from gray-level histograms, IEEE Transactions on systems, man, and cybernetics, 9 (1979) 62–66. https://doi.org/10.1109/TSMC.1979.4310076
[30] L. K. Huang, M. J. J. Wang, Image thresholding by minimizing the measures of fuzziness, Pattern recognition, 28 (1995) 41-51. https://doi.org/10.1016/0031-3203(94)E0043-K