The identification of flooded areas over Earth Observation (EO) satellite images has paved the way to monitor damaged areas and take effective actions. Classifying all pixels of a satellite image as a flooded area or not allows for creating maps which are then used by civil protection agencies and first responders. In this work, a method, firstly implemented for Emergency Management Service (e.g. Copernicus), will be applied for the first time to the detection of changes of surface water bodies, based on water volumes data available from the Moroccan Demonstration Area of the H2020 MOSES (Managing crOp water Saving with Enterprise Services) Project.
The novel method is based on the combination of Mahalanobis Distance-based classification for flood mask creation and morphological post-processing for flood mask correction. The classification is performed by using four-dimensional classification features derived directly from the image pixels (namely R, G, B and Near-Infrared channel). The mask correction consists of three steps.
The first step relies on the assumption that if the percentage of pixels classified as flooded in an image is very small, and then probably these pixels are misclassified samples. So if the percentage of flooded pixels in an image is less than a preset threshold, they are set to non-flooded (the threshold has been empirically set to 5% after examining classification results of a training set).
The second post-processing step aims at eliminating small flooded areas (groups of a few pixels, usually 1 to 10, classified as flooded in non-flooded areas), which are potentially false positives, by applying connected-component analysis: the algorithm counts the number of pixels in such areas and in case it is less than a threshold (10 pixels) marks them as non-flooded.
Finally the third post-processing step, aims at eliminating small non-flooded areas inside flooded area, which are probably false negatives, by applying image dilation and erosion.
Experiments have been performed on satellite images, collected from PlanetLabs  and have been provided in the context of MediaEval2017 - Multimedia Satellite Task (http://www.multimediaeval.org/mediaeval2018). In this work we also examine the performance of the method in the context of water body detection over irrigated agricultural areas.
The current technique is going to be extended in the future by considering synthetic aperture radar (SAR) data fusion and additional features in the context of H2020-EOPEN (opEn interOperable Platform for unified access and analysis of Earth observation data).