Integrating object-based image analysis and geographic information systems for waterbodies delineation on synthetic aperture radar data

References

• Belgiu M, Csillik O. 2018. Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote Sens Environ. 204:509–523.
   Web of Science ®Google Scholar
• Blaschke T, Burnett C, Pekkarinen A. 2004. Image Segmentation Methods for Object-based Analysis and Classification. In: Jong SMD, Meer FDV, editors. Remote sensing image analysis: including the spatial domain. Dordrecht: Springer; p. 211–236. https://doi.org/10.1007/1-4020-2560-2_12
   Google Scholar
• Blaschke T, Hay GJ, Kelly M, Lang S, Hofmann P, Addink E, Queiroz Feitosa R, van der Meer F, van der Werff H, van Coillie F, et al. 2014. Geographic object-based image analysis – towards a new paradigm. ISPRS J Photogramm Remote Sens. 87(100):180–191.
   PubMedGoogle Scholar
• Brivio PA, Colombo R, Maggi M, Tomasoni R. 2002. Integration of remote sensing data and GIS for accurate mapping of flooded areas. Int J Remote Sens. 23(3):429–441.
   Web of Science ®Google Scholar
• Canada.ca. 2015. Radar Polarimetry [Internet]; [accessed 2021 Feb 22]. https://www.nrcan.gc.ca/maps-tools-publications/satellite-imagery-air-photos/remote-sensing-tutorials/microwave-remote-sensing/radar-polarimetry/9275.
   Google Scholar
• Chehata N, Orny C, Boukir S, Guyon D, Wigneron JP. 2014. Object-based change detection in wind storm-damaged forest using high-resolution multispectral images. Int J Remote Sens. 35(13):4758–4777.
   Web of Science ®Google Scholar
• Cheng G, Han J. 2016. A survey on object detection in optical remote sensing images. ISPRS J Photogramm Remote Sens. 117:11–28.
   Web of Science ®Google Scholar
• Derivaux S, Forestier G, Wemmert C, Lefèvre S. 2010. Supervised image segmentation using watershed transform, fuzzy classification and evolutionary computation. Pattern Recog Lett. 31(15):2364–2374.
   Web of Science ®Google Scholar
• ESA. 2020a. SNAP [Internet]; [accessed: 2021 Jan 18]. https://step.esa.int/main/toolboxes/snap/.
   Google Scholar
• ESA. 2020b. Definitions [Internet]; [accessed 2021 Feb 22]. https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/definitions.
   Google Scholar
• ESA. 2020c. Polarimetry [Internet]; [accessed 2021 Feb 22]. https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-overview/polarimetry.
   Google Scholar
• Filipponi F. 2019. Sentinel-1 GRD Preprocessing Workflow. Proceedings. 18(1):11.
   Google Scholar
• Hay GJ, Castilla G. 2006. Object-based image analysis: strengths, weaknesses, opportunities and threats (SWOT). In: Lang S, Blaschke T, Schöpfer E, editors. Proceedings of 1st International Conference on Object-Based Image Analysis – Bridging Remote Sensing and GIS. Austria: Salzburg University; p. 4–5.
   Google Scholar
• Huang F, Chen Y, Li L, Zhou J, Tao J, Tan X, Fan G. 2019. Implementation of the parallel mean shift-based image segmentation algorithm on a GPU cluster. Int J Digital Earth. 12(3):328–353.
   Web of Science ®Google Scholar
• Huang W, DeVries B, Huang C, Lang M, Jones J, Creed I, Carroll M. 2018. Automated extraction of surface water extent from sentinel-1 data. Remote Sens. 10(5):797.
   Google Scholar
• Ji L, Zhang L, Wylie B. 2009. Analysis of dynamic thresholds for the normalized difference water index. Photogramm Eng Remote Sens. 75(11):1307–1317.
   Web of Science ®Google Scholar
• Jiang Z, Qi J, Su S, Zhang Z, Wu J. 2012. Water body delineation using index composition and HIS transformation. Int J Remote Sens. 33(11):3402–3421.
   Web of Science ®Google Scholar
• Kussul N, Shelestov A, Skakun S. 2011. Flood Monitoring from SAR Data. In: Kogan F, Powell A, Fedorov O, editors. Use of satellite and in-situ data to improve sustainability, NATO science for peace and security series C: environmental security. Dordrecht, the Netherlands: Springer; p. 19–29. https://doi.org/10.1007/978-90-481-9618-0_3
   Google Scholar
• Lang S. 2008. Object-based image analysis for remote sensing applications: modeling reality – dealing with complexity. In: Blaschke T, Lang S, Hay GJ, editors. Object-based image analysis, lecture notes in geoinformation and cartography. Heidelberg, Berlin: Springer; p. 3–27. https://doi.org/10.1007/978-3-540-77058-9_1
   Google Scholar
• Mahdianpari M, Salehi B, Mohammadimanesh F, Motagh M. 2017. Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery. ISPRS J Photogramm Remote Sens. 130:13–31.
   Web of Science ®Google Scholar
• Matgen P, Hostache R, Schumann G, Pfister L, Hoffmann L, Savenije HHG. 2011. Towards an automated SAR-based flood monitoring system: lessons learned from two case studies. Phys Chem Earth Parts A/B/C. 36(7–8):241–252.
   Web of Science ®Google Scholar
• McFeeters SK. 1996. The use of the normalized difference water index (NDWI) in the delineation of open water features. Int J Remote Sens. 17(7):1425–1432.
   Web of Science ®Google Scholar
• Michel J, Youssefi D, Grizonnet M. 2015. Stable mean-shift algorithm and its application to the segmentation of arbitrarily large remote sensing images. IEEE Trans Geosci Remote Sens. 53(2):952–964.
   Web of Science ®Google Scholar
• Moskal LM, Styers DM, Halabisky M. 2011. Monitoring urban tree cover using object-based image analysis and public domain remotely sensed data. Remote Sens. 3(10):2243–2262.
   Google Scholar
• Navulur K. 2006. Multispectral image analysis using the object-oriented paradigm. Boca Raton, FL: CRC Press.
   Google Scholar
• Nussbaum S, Menz G. 2008. Object-based image analysis and treaty verification: new approaches in remote sensing – applied to nuclear facilities in Iran. New York, NY: Springer. https://doi.org/10.1007/978-1-4020-6961-1
   Google Scholar
• Olofsson P, Foody GM, Herold M, Stehman SV, Woodcock CE, Wulder MA. 2014. Good practices for estimating area and assessing accuracy of land change. Remote Sens Environ. 148:42–57.
   Web of Science ®Google Scholar
• Orfeo Toolbox. 2020. Open Source processing of remote sensing images [Internet]; [accessed 2021 Jan 18]. https://www.orfeo-toolbox.org/.
   Google Scholar
• Pradhan B, Sameen MI, Kalantar B. 2017. Optimized rule-based flood mapping technique using multitemporal RADARSAT-2 images in the tropical region. IEEE J Sel Top Appl Earth Observations Remote Sens. 10(7):3190–3199.
   Web of Science ®Google Scholar
• Pulvirenti L, Chini M, Pierdicca N, Guerriero L, Ferrazzoli P. 2011. Flood monitoring using multi-temporal COSMO-SkyMed data: image segmentation and signature interpretation. Remote Sens Environ. 115(4):990–1002.
   Web of Science ®Google Scholar
• QGIS. 2020. A free and open source geographic information system [Internet]; [accessed 2021 Jan 18]. https://qgis.org/en/site/.
   Google Scholar
• Qin X, Yang J, Li P, Sun W. 2019. Research on water body extraction from Gaofen-3 imagery based on polarimetric decomposition and machine learning. Paper presented at IGARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan. IEEE. p. 6903–6906.
   Google Scholar
• Ramsar Sites Information Service. 1998. Lake Vistonis, Porto Lagos., Lake Ismaris & adjoining lagoons [Internet]; [accessed 2021 Jan 18]. https://rsis.ramsar.org/ris/55.
   Google Scholar
• Remote Sensing for Forest Cover Change Detection. 2016. Module 3: Introduction to QGIS and Land Cover Classification [Internet]; [accessed 2021 Jan 18]. https://servirglobal.net/Portals/0/Documents/Articles/ChangeDetectionTraining/Module3_LC_Classification_Accuracy_Assessment.pdf.
   Google Scholar
• Smith LC. 1997. Satellite remote sensing of river inundation area, stage, and discharge: a review. Hydrol Procs. 11(10):1427–1439.
   Web of Science ®Google Scholar
• Soti V, Tran A, Bailly J-S, Puech C, Seen DL, Bégué A. 2009. Assessing optical earth observation systems for mapping and monitoring temporary ponds in arid areas. Int J Appl Earth Obs Geoinf. 11(5):344–351.
   Web of Science ®Google Scholar
• Sun F, Sun W, Chen J, Gong P. 2012. Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery. Int J Remote Sens. 33(21):6854–6875.
   Web of Science ®Google Scholar
• Thenkabail PS, editor. 2015. Remotely sensed data characterization, classification, and accuracies. Boca Raton, Florida: CRC Press. https://doi.org/10.1201/b19294
   Google Scholar
• Tholey N, Clandillon S, De Fraipont P. 1997. The contribution of spaceborne SAR and optical data in monitoring flood events: examples in northern and southern France. Hydrol Process. 11(10):1409–1413.
   Web of Science ®Google Scholar
• Töyrä J, Pietroniro A, Martz LW. 2001. Multisensor hydrologic assessment of a freshwater wetland. Remote Sens Environ. 75(2):162–173.
   Web of Science ®Google Scholar
• Tso B, Mather P. 2009. Classification methods for remotely sensed data. 2nd ed. Boca Raton, FL: CRC Press.
   Google Scholar
• Tzotsos A, Argialas D. 2008. Support vector machine classification for object-based image analysis. In: Blaschke T, Lang S, Hay GJ, editors. Object-based image analysis, lecture notes in geoinformation and cartography. Heidelberg, Berlin: Springer; p. 663–677.
   Google Scholar
• Xu H. 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens. 27(14):3025–3033.
   Web of Science ®Google Scholar
• Zhang H, Li Q, Liu J, Du X, Dong T, McNairn H, Champagne C, Liu M, Shang J. 2018. Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier. Geocarto Int. 33(10):1017–1035.
   Web of Science ®Google Scholar
• Zhong L, Hu L, Zhou H. 2019. Deep learning based multi-temporal crop classification. Remote Sens Environ. 221:430–443.
   Web of Science ®Google Scholar
• Zhou X, Liu X, Zhang Z. 2019. Automatic Extraction of Lakes on the Qinghai-Tibet Plateau from Sentinel-1 SAR Images. Paper presented at the 2019 SAR in Big Data Era (BIGSARDATA), Beijing, China. IEEE; p. 1–4.
   Google Scholar