KFDA-based cropland inundation change detection with an automatic method for training sample extraction

Flood is the most frequent disaster in the world, which can do harm to agriculture and threat to food security. Using kernel based supervised classifier to execute change detection for multi-temporal remote sensing data is a common method for flood disaster monitoring and assessment, and kernel Fisher´s discrimination analysis (KFDA) is one of them. Choosing training sample by visual interpretation is an important step, but difficult and wasting time, for the reason that a great amount of the flooded pixels are heterogeneous. In this study, we proposed an automatic sample extraction method, finding pixels in relative homogeneous areas by multiresolution segmentation and zonal standard deviation calculating, and then assigning sample class via clustering or linear discrimination of some specific index. The results showed that overall accuracy could reach 91.57% and the Kappa coefficient was 0.8316. The method we proposed was proved to be efficient.