Object-Level Semantic Change Interpretation for Multi-band Remotely Sensed Imagery

We propose a method for object-level change interpretation which we apply to remotely-sensed multispectral imagery. The method employs a model-based change description determined by a previously-developed change understanding system [3] in which object change is expressed by estimated model parameters. In this work, object change is interpreted using a physical description of the model parameter changes and the evaluation of object change model probabilities and error objects. Experiments are performed on multispectral ASTER imagery to analyze the proposed change interpretation mechanisms.