Cross-Correlation Stacking for Robust Offset Tracking Using SAR Image Time-Series

Abstract

Offset tracking is widely applied for measuring ground surface displacements from remote sensing data. Displacements are determined by the offset where two image templates match best. The match can be evaluated with normalized cross correlation (NCC), in which the height and location of the NCC peak represent the matching quality and the corresponding offset. Attaining robust degrees Met estimations requires an unambiguous tracking of the peak in the NCC noise floor. To improve offset estimations, we propose a cross-correlation stacking method that can significantly suppress the noise floor of NCC. Instead of deriving offsets from each pairwise NCC, we stack a series of consecutive pairwise NCCs and determine the offset after averaging the NCC stack. Thereby, tracking benefits from the redundant information in multiple NCCs and is more robust to noise. We assessed the method by measuring the flow velocity of the Great Aletsch Glacier in Switzerland using image time series collected by the synthetic aperture radar satellites TanDEM-X and Sentinel-1 A. Using relatively small templates of 48 x 48 pixels combined with a stack of seven pairwise NCCs of TanDEM-X images, we obtain velocity fields whose spatial coverage are almost equivalent to the coverage of velocity fields obtained with templates of 96 x 96 pixels applied on a single image pair. Similar improvements in spatial coverage are observed for Sentinel-1 A. The results demonstrate that the stacking method can greatly enhance both the spatial resolution and the coverage of the obtained velocity fields.