In the following, we describe highly-automated image analysis approaches that help us classify satellite images, and allow us to monitor dynamical changes in image time series. We concentrated on flooding events within the Danube Delta as seen by the European Sentinel-1 and Sentinel-2 satellites, and describe systematic processing approaches to extract pre-defined categories from the image data (being either Synthetic Aperture Radar or multispectral images). One basic tool to monitor dynamical changes is to analyze and compare the compressibility of image patches using their Normalized Compression Distances. These distances can be converted into similarity matrices providing reliable maps of surface changes. The accuracy of these change maps was quantified for several typical test cases. In addition, we analyzed the performance of an alternative active learning approach, where Gabor filters and Weber local descriptors were used to extract features from image patches that were classified and semantically annotated. Then one can perform data analytics and generate maps based on the extracted semantic annotations; again, we used several representative test cases for benchmarking.