This project will employ a PhD student who shall develop a suite of algorithms capable of detecting changes in both pairs and longer time series of co-located remote sensing images captured by different sensors or with different acquisition modes. Traditional algorithms for change detection in images typically assume that the images are perfectly co-registered and co-calibrated, such that no change corresponds to zero difference or unit ratio between the compared pixels. This assumption is in most practical applications unrealistic due to variations in viewing geometry and environmental conditions, phenological evolution of vegetation, and other factors that cannot be perfectly compensated for. We proposed an alternative strategy: A pattern of typical temporal evolution is established by use of distance measures that quantify the statistical similarity of sensor measurements. These can be used to contrast single pixels or groups of pixels along both the spatial and the temporal dimension. Clusters of pixels that behave similarly in terms of temporal evolution define a thematic ground cover class with no change. Changes will be detected as divergence and bifurcation within such a cluster, as the spatial configurations or temporal profiles will deviate from the main pattern when changes occur. The distance-based approach will be implemented with kernel methods from recent award winning work on machine learning. The design will rely on a judicious combination of: (i) parametric models that capture prior knowledge based on a physical understanding of the sensors, with (ii) nonparametric models that allow the required flexibility to combine heterogeneous sources of information and to model the sampling distributions of the test statistics. The PhD student will be trained in collaboration with the University of Genoa, Italy, allowing research visits to and co-tutoring by leading experts and authors of seminal work on the topic.
Project leader: Stian Normann Anfinsen
Institution: FAKULTET FOR NATURVITENSKAP OG TEKNOLOGI