Estimation of Natural Hazard Damages through the Fusion of Change Maps Obtained from Optical and Radar Earth Observations

Abstract

The Earth’s land-cover is exposed to several types of environmental change, caused by either human activities or natural disasters. On 11 March 2011, an earthquake occurred about 130 km off the east coast of Sendai City in Japan. This earthquake was followed by a huge tsunami, which caused devastating damages over wide areas in the eastern coastline of Japan. Due to the occurrence of natural disasters across the world, there is a strong need to develop an automated algorithm for the fast and accurate extraction of changing landscapes within the affected areas. Such techniques can accelerate the process of strategic planning and primary services to move people into shelters and to carry out damage assessments as well as risk management during a crisis. Therefore, a variety of change detection (CD) techniques has been previously developed, based on various requirements and conditions. However, the selection of the most suitable method for change detection is not easy in practice. To the best of our knowledge, there is no existing CD approach that is both optimal and applicable in the cases of using a variety of optical and radar remote sensing images. To resolve these problems, an automated CD method based on a support vector data description (SVDD) classifier is proposed. This method uses the information contents of radar and optical data simultaneously by decision-level fusing of the change maps obtained from these data. For evaluating the efficiency of the proposed method and extract the damaged areas, the 2011 Sendai tsunami was considered. Various optical and radar remote sensing images from before and after the 2011 Sendai tsunami acquired by IKONOS and Radarsat-2 were used. The results confirmed the fundamental role and potential of using both optical and radar data for natural hazard damage detection applications.