Tsunami-induced Change Detection Using SAR Intensity and Texture Information Based on the Generalized Gaussian Mixture Model

Abstract

The remote sensing technique using SAR data have many advantages when applied to the disaster site due to its wide coverage and all-weather acquisition availability. Although a single-pol (polarimetric) SAR image cannot represent the land surface better than a quad-pol SAR image can, single-pol SAR data are worth using for disaster-induced change detection. In this paper, an automatic change detection method based on a mixture of GGDs (generalized Gaussian distribution) is proposed, and usability of the textural features and intensity is evaluated by using the proposed method. Three ALOS/PALSAR images were used in the experiments, and the study site was Norita City, which was affected by the 2011 Tohoku earthquake. The experiment results showed that the proposed automatic change detection method is practical for disaster sites where the large areas change. The intensity information is useful for detecting disaster-induced changes with a 68.3% g-mean, but the texture information is not. The autocorrelation and correlation show the interesting implication that they tend not to extract agricultural areas in the change detection map. Therefore, the final tsunami-induced change map is produced by the combination of three maps: one is derived from the intensity information and used as an initial map, and the others are derived from the textural information and used as auxiliary data. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

A Similarity Weight-based Method to Detect Damage Induced by a Tsunami

“…Single polarization SAR images have been extensively used for the assessment of damage caused by large-scale disasters (Yonezawa and Takeuchi, 2001;Matsuoka and Yamazaki, 2004;Matsuoka and Yamazaki, 2005;Arciniegas et al, 2007;Bovolo and Bruzzone, 2007;Brunner et al, 2010;Chini et al, 2012;Jung and Kim, 2016). Arciniegas et al (2007) and Chini et al (2012) utilized the complex coherence of the interferometric pair for damage assessment.…”

“…Matsuoka and Yamazaki (2004), Matsuoka and Yamazaki (2005), and Yonezawa and Takeuchi (2001) proposed methods that used intensity changes and intensity correlation to detect damage. Jung and Kim (2016) also used intensity changes and textual features to assess damage. Brunners et al (2010) detected the damaged areas using the similarity between a simulated image and an actual SAR image that was generated by using optical and SAR images.…”