P. Manivannan

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Master thesis (1)

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Urban Infrastructure Damage Detection and Mapping Using Sentinel 1

Master thesis (2019) - Padmini Manivannan Manivannan (author) , Paco Lopez Dekker López-Dekker (mentor) , Alle-Jan van der Veen van der Veen (graduation committee member) , Faruk Uysal Uysal (graduation committee member)

Natural or man-made disasters can have a drastic impact on social, economic and environmental aspects of an affected population. Specifically, earthquakes are one of the most potent natural hazards, which cause a disproportionate amount of fatalities, primarily due to a) unexpected building collapses, b) restricted or limited access to basic amenities and c) potential hazards following earthquakes such as landslides, tsunamis etc. It is crucial to have an overview of the infrastructural damage caused following a disaster for search and rescue services to assess the extent of the damage. For the purpose of this research, Sentinel 1 imagery is used to map the building damage in an urban area after a disaster. A combination of parameters such as persistent scatterers, pixel amplitude and phase is used with a timeseries of full-resolution and spatially averaged radar images. Points that are stable in amplitude over a long timeseries, also known as Persistent Scatterers, are extracted from a stack of full-resolution images. The amplitudes of persistent scatterers, along with amplitude and coherence of pixels derived from a stack of spatially-averaged images, are statistically analysed to check the trends of the parameters pre- and post the disaster. A change detection algorithm is applied to this stack in order to localise the areas of building damage. The results are superimposed on Google Earth for easy interpretation using a graded damage scale. The analysis shows that exploiting the persistent scatterer amplitudes in the manner used in this research provides a novel way of locating building damage. This technique can be used effectively in urban areas. Using a combination of pixel amplitudes and coherence along with the persistent scatterers helps correctly find new and unique points of damage for each parameter used. The results were validated using reference Grading and crowd-sourced maps. The results illustrate that the proposed approach can be used for detecting and producing informative maps on infrastructural damage detection in urban areas.