Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7, 209-216, 2014
https://doi.org/10.5194/isprsarchives-XL-7-209-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
19 Sep 2014
Rapid Disaster Analysis based on Remote Sensing: A Case Study about the Tohoku Tsunami Disaster 2011
C.H. Yang1, U. Soergel1, Ch. Lanaras2, E. Baltsavias2, K. Cho3, F. Remondino4, and H. Wakabayashi5 1Technische Universität Darmstadt, Institute of Geodesy, Darmstadt, Germany
2ETH Zurich, Institute of Geodesy and Photogrammetry, Zurich, Switzerland
3Tokai University, School of Information and Science & Technology, Kanagawa 259-1292, Japan
4Bruno Kessler Foundation, 3D Optical Metrology unit, via Sommarive 18, 38123, Trento, Italy
5Nihon University, College of Engineering, I Computer Science, Koriyama, Fukushima, Japan
Keywords: Disaster Analysis, SAR, Optical Images, Image Co-registration, Change Detection, Image Matching, Curvelet Filtering, Morphological Approach Abstract. In this study, we present first results of RAPIDMAP, a project funded by European Union in a framework aiming to foster the cooperation of European countries with Japan in R&D. The main objective of RAPIDMAP is to construct a Decision Support System (DSS) based on remote sensing data and WebGIS technologies, where users can easily access real-time information assisting with disaster analysis. In this paper, we present a case study of the Tohoku Tsunami Disaster 2011. We address two approaches namely change detection based on SAR data and co-registration of optical and SAR satellite images. With respect to SAR data, our efforts are subdivided into three parts: (1) initial coarse change detection for entire area, (2) flood area detection, and (3) linearfeature change detection. The investigations are based on pre- and post-event TerraSAR-X images. In (1), two pre- and post-event TerraSAR-X images are accurately co-registered and radiometrically calibrated. Data are fused in a false-color image that provides a quick and rough overview of potential changes, which is useful for initial decision making and identifying areas worthwhile to be analysed further in more depth. However, a bunch of inevitable false alarms appear within the scene caused by speckle, temporal decorrelation, co-registration inaccuracy and so on. In (2), the post-event TerraSAR-X data are used to extract the flood area by using thresholding and morphological approaches. The validated result indicates that using SAR data combining with suitable morphological approaches is a quick and effective way to detect flood area. Except for usage of SAR data, the false-color image composed of optical images are also used to detect flood area for further exploration in this part. In (3), Curvelet filtering is applied in the difference image of pre- and post-event TerraSAR-X images not only to suppress false alarms of irregular-features, but also to enhance the change signals of linear-features (e.g. buildings) in settlements. Afterwards, thresholding is exploited to extract the linear-feature changes. In rapid mapping of disasters various sensors are often employed, including optical and SAR, since they provide complementary information. Such data needs to be analyzed in an integrated fashion and the results from each dataset should be integrated in a GIS with a common coordinate reference system. Thus, if no orthoimages can be generated, the images should be co-registered employing matching of common features. We present results of co-registration between optical (FORMOSAT-2) and TerraSAR-X images based on different matching methods, and also techniques for detecting and eliminating matching errors.
Conference paper (PDF, 3405 KB)


Citation: Yang, C. H., Soergel, U., Lanaras, Ch., Baltsavias, E., Cho, K., Remondino, F., and Wakabayashi, H.: Rapid Disaster Analysis based on Remote Sensing: A Case Study about the Tohoku Tsunami Disaster 2011, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7, 209-216, https://doi.org/10.5194/isprsarchives-XL-7-209-2014, 2014.

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