A Hierarchical Split-Based Approach for Parametric Thresholding of SAR Images: Flood Inundation as a Test Case

• Published in: IEEE transactions on geoscience and remote sensing Vol. 55; no. 12; pp. 6975 - 6988
• Main Authors: Chini, Marco, Hostache, Renaud, Giustarini, Laura, Matgen, Patrick
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Backscatter; Capacity; Change detection (CD); Data acquisition; Data processing; Distribution; Distribution functions; flood; Flood mapping; Floods; Floodwater; hierarchical split-based approach (HSBA); Histograms; Hydrologic data; Image classification; Imagery; Mathematical models; Methods; Parameterization; parametric thresholding; Radar; Radar imaging; Rivers; SAR (radar); Spatial resolution; Speckle; Synthetic aperture radar; synthetic aperture radar (SAR); Tiles
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2017.2737664

Parametric thresholding algorithms applied to synthetic aperture radar (SAR) imagery typically require the estimation of two distribution functions, i.e., one representing the target class and one its background. They are eventually used for selecting the threshold that allows binarizing the image in an optimal way. In this context, one of the main difficulties in parameterizing these functions originates from the fact that the target class often represents only a small fraction of the image. Under such circumstances, the histogram of the image values is often not obviously bimodal and it becomes difficult, if not impossible, to accurately parameterize distribution functions. Here we introduce a hierarchical split-based approach that searches for tiles of variable size allowing the parameterization of the distributions of two classes. The method is integrated into a flood-mapping algorithm in order to evaluate its capacity for parameterizing distribution functions attributed to floodwater and changes caused by floods. We analyzed a data set acquired during a flood event along the Severn River (U.K.) in 2007. It is composed of moderate (ENVISAT-WS) and high (TerraSAR-X)-resolution SAR images. The obtained classification accuracies as well as the similarity of performance levels to a benchmark obtained with an established method based on the manual selection of tiles indicate the validity of the new method.