Urban change detection in SAR images by interactive learning

This paper focuses on finding changes in an urban environment (new or demolished buildings, activity monitoring) using Synthetic Aperture Radar (SAR) imagery. We propose a novel approach to characterize changes between two registered images. First, “what is a change” is learned interactively using user-provided examples in order to adapt the detection to the query context. Second, we propose the Change-Index Histogram of Oriented Gradients (CI-HOG), a new change descriptor that captures local statistics of change indices. We assess our system on TerraSAR-X data captured over challenging locations.