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Open AccessArticle

Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods

1
International Research Institute of Disaster Science, Tohoku University, Aoba 468-1-E301, Aramaki, Aoba-ku, Sendai 980-8572, Japan
2
Graduate School of Engineering, Tohoku University, Aoba 468-1-E301, Aramaki, Aoba-ku, Sendai 980-8572, Japan
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2320; https://doi.org/10.3390/rs11192320
Received: 27 August 2019 / Revised: 26 September 2019 / Accepted: 3 October 2019 / Published: 5 October 2019
Synthetic aperture radar (SAR) images have been used to map flooded areas with great success. Flooded areas are often identified by detecting changes between a pair of images recorded before and after a certain flood. During the 2018 Western Japan Floods, the change detection method generated significant misclassifications for agricultural targets. To evaluate whether such a situation could be repeated in future events, this paper examines and identifies the causes of the misclassifications. We concluded that the errors occurred because of the following. (i) The use of only a single pair of SAR images from before and after the floods. (ii) The unawareness of the dynamics of the backscattering intensity through time in agricultural areas. (iii) The effect of the wavelength on agricultural targets. Furthermore, it is highly probable that such conditions might occur in future events. Our conclusions are supported by a field survey of 35 paddy fields located within the misclassified area and the analysis of Sentinel-1 time series data. In addition, in this paper, we propose a new parameter, which we named “conditional coherence”, that can be of help to overcome the referred issue. The new parameter is based on the physical mechanism of the backscattering on flooded and non-flooded agricultural targets. The performance of the conditional coherence as an input of discriminant functions to identify flooded and non-flooded agricultural targets is reported as well. View Full-Text
Keywords: the 2018 western Japan floods; ALOS-2 SAR imagery; agriculture targets; false detection the 2018 western Japan floods; ALOS-2 SAR imagery; agriculture targets; false detection
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Moya, L.; Endo, Y.; Okada, G.; Koshimura, S.; Mas, E. Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods. Remote Sens. 2019, 11, 2320.

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