OBIA Flood Delimitation Assisted by Threshold Determination with Principal Component Analysis

Accurate and rapidly mapped flood boundaries are extremely important for emergency management operations and hydraulic flood model calibration. This study presents a methodology for automatic flood delimitation in SAR images. An Object-based Image Analysis (OBIA) is applied to SAR images and to a Digital Terrain Model (DTM), organizing a database for hydraulic flood models calibration. Principal Component Analysis (PCA) is proposed to automate the determination of flood / non-flood decision thresholds. A previous classification, with a visual threshold selection, is performed for a small set of training images. A first PCA detects correlation between the training thresholds, image, and flood parameters; while a second PCA allows the automatic determination of the threshold for the remaining dataset classification. For the quality assessment, averages of 88 percent for properly detected flooded area and of 10 percent for commission error are achieved. It is verified that the algorithm performs well for images acquired during most weather conditions.