Causality for Remote Sensing: An Exploratory Study

Causality is one of the most important topics in a Machine Learning (ML) research, and it gives insights beyond the dependency of data points. Causality is a very vital concept also for investigating the dynamic surface of our living planet. However, there are not many attempts for integrating a causal model in Remote Sensing (RS) methodologies. Hence, in this paper, we propose to use patch-based RS images and to represent each patch-based image by a single variable (e.g. entropy). Then we use a Structural Equation Model (SEM) to study their cause-effect relation. Moreover, the SEM is a simple causal model characterized by a Directed Acyclic Graph (DAG). Its nodes are causal variables, and its edges represent causal relationships among causal variables if and only if causal variables are dependent.