MORPHOLOGICAL HIT-OR-MISS TRANSFORM BASED APPROACH FOR BUILDING DAMAGE ESTIMATION FROM VHR AIRBORNE IMAGERY IN 2011 PACIFIC COAST OF TOHOKU EARTHQUAKE AND TSUNAMI
- 1Department of Urban and Environmental Engineering, Graduate School of Engineering, Kyoto University, Japan
- 2Department of Electrical Engineering, Tokyo University of Science, Tokyo, Japan
- 3Graduate School of Global Environmental Studies, Kyoto University, Japan
- 4Asia Disaster Reduction Center, Kobe, Japan
Keywords: Mathematical Morphological Operators, Hit-or-Miss Transform, Natural Hazard, VHR Airborne Images, Building Extraction
Abstract. The very high resolution (VHR) airborne images offer the opportunity to recognize features such as road, vegetation, buildings and other kind of infrastructures. The advantage of remote sensing and its applications made it possible to extract damaged, undamaged building and vulnerability assessment of wide urban areas due to a natural disaster. In this paper, we focus on an automatic building detection method which is helpful to optimizing, recognizing, rescuing, recovery and management tasks in the event of a disaster. Objective of this study is to develop techniques for tsunami damaged building extraction, based on very high resolution (VHR) airborne images acquired before and after the 2011 East coastline of Japan among Tohoku area and to carry out a damage assessment of building and vulnerable area mapping. This paper presents a methodology and results of evaluating damaged buildings detection algorithm using an object recognition task based on Mathematical Morphological (MM) operators for Very High Resolution (VHR) remotely sensed airborne images. The proposed approach involves several advanced morphological operators among which an adaptive hit-or-miss transform with varying size and shape of the structuring elements. VHR airborne images consisting of pre and post 2011 Pacific coast of Tohoku earthquake and Tsunami site of the Ishinomaki, Miyagi area in Japan were used. The extracted results of building were compared with ground truth data giving 76% and 88% in accuracy before and after the Tsunami event.