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

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 19; p. 2320
• Main Authors: Moya, Luis, Endo, Yukio, Okada, Genki, Koshimura, Shunichi, Mas, Erick
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2019
• Subjects: agriculture targets; alos-2 sar imagery; Backscattering; Change detection; Coherence; Disasters; Discriminant analysis; false detection; Flood mapping; Flooded areas; Floods; Machine learning; Parameters; Radar imaging; Remote sensing; Rice fields; Synthetic aperture radar; the 2018 western japan floods; Urban areas; Vegetation; Japan; Chao Phraya River
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11192320

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.