An adaptive parcel-based technique robust to registration noise for change detection in multitemporal VHR images

This paper presents a parcel-based multiscale technique robust to registration noise for unsupervised change detection in multitemporal very high geometrical resolution images. The proposed technique is based on the analysis of the statistical behavior of registration noise present in multitemporal images at different scales. In particular, the method exploits a differential analysis of the direction distributions of spectral change vectors (SCVs) computed at different resolution levels in the polar domain. The multiscale analysis permits to separate sectors associated with true changes from sectors associated with residual registration noise. In order to improve the change-detection accuracy, the presented approach exploits the spatial-contextual information contained in the neighborhood of each pixel by defining multitemporal "parcels" (i.e. small homogeneous regions shared by both original images). Change detection is achieved by applying a specific comparison algorithm to each pixe...