A Circular Approach to Multi-Class Change Detection in Multitemporal Sentinel-1 SAR Image Time Series

This paper presents a multitemporal technique for multi-class Change Detection (CD) between pairs of images of a satellite image time series. Changes between different pair of images within a time series must be consistent with each other since images acquired over the same scene are causally related with one another. The temporal consistency of the pixel status can be used to formulate a principle that constrains the CD results within the series to be mutually consistent. This principle coincides with the conservative property of the change variable and it allows the unsupervised validation of changes detected between arbitrary image pairs. Thus, all images in the series, rather than a single couple, are used in the pair-wise CD. The proposed technique was applied to a dataset of dual-polarized terrain-corrected SAR images acquired by Sentinel-1. Experimental results show the validity of the proposed multitemporal approach in improving the CD results.