MACHINE LEARNING TECHNIQUES FOR KNOWLEDGE EXTRACTION FROM SATELLITE IMAGES: APPLICATION TO SPECIFIC AREA TYPES
Keywords: Image classification, Image compression, Active learning, Latent Dirichlet Allocation (LDA), Convolutional Neural Networks (CNNs), Sentinel-1, Sentinel-2
Abstract. When we want to extract knowledge form satellite images, several well-known image classification and analysis techniques can be concatenated or combined to gain a more detailed target understanding. In our case, we concentrated on specific extended target areas such as polar ice-covered surfaces, forests shrouded by fire plumes, flooded areas, and shorelines. These image types can be described by characteristic features and statistical relationships. Here, we demonstrate that both multispectral (optical) as well as SAR (Synthetic Aperture Radar) images can be used for knowledge extraction. The free availability of image data provided by the European Sentinel-1 and Sentinel-2 satellites allowed us to conduct a series of experiments that verified our classification approaches. This could already be verified in our recent work by quantitative quality tests.