Image Representation Alternatives for the Analysis of Satellite Image Time Series

Kurzfassung

Current satellite images and image time series provide us with detailed information about the state of our planet as well as about our technical infrastructure and human activities. These images allow us to learn more about local, regional, and global phenomena and events, including - if interpreted properly - their causes and effects. In particular, image time series provide specific information about the dynamics of many processes implicitly contained in our images that need to be unearthed and investigated in detail. A traditional approach towards this aim is to start with pixel-level or patch-level data analysis for pixel-based image analysis, followed, if necessary, by subsequent feature extraction, clustering, classification and semantic labelling in order to generate various types of change maps on different representation levels. The classification step can be supported by interactive human intervention, or by automated machine learning strategies to identify higher level objects and their spatial and temporal relationships. The detected relationships can then be formulated as parameterized rule sets that create higher-level descriptor sets of the content of the selected images, and of additional external data such as thematic maps or typical dynamics descriptions. As an innovative extension of this traditional concept, we propose a highly automated approach for application-adapted image content exploration and knowledge extraction. The reason for this strategy is the additional amount and the precision of semantic relationships and details that we can assign to an image time series once we know the final application field and how to embed and access image content within knowledge graphs.