Robust measurement of glacier surface motion from multiscale speckle tracking using local constraints

A grown importance in long-term operational glacier monitoring has emerged, mainly due to the connection of glacier recession to climate changes. Up to now, mainly two types of methods have been used for the estimation of glacier flow velocities: Image matching and differential interferometry (DInSAR). Although the principal potential of DInSAR for glacier velocity estimation has been shown in several case studies, its successful application is often limited by phase noise, described by the coherence. Additionally, the glacier velocity is often too large to be analysed by means of DInSAR since this method can be too sensitive to correctly track the large displacements occurring during a typical data acquisition interval of one month. SAR amplitude images are not limited by phase stability problems like in DInSAR and can reliably be acquired on a regular basis. In this work, a novel algorithm for computing the velocity field and motion parameters from a sequence of SAR amplitude images are presented. The algorithm is based on the vector relaxation combined with standardized cross- covariance matrix information and cross-correlation techniques. The cross-correlation is used to indicate the candidate motion vectors for each pixel. After this step, by a relaxation operation local smoothness constraints are introduced into the estimated flow pattern, leading to a more homogeneous velocity estimation. In order to handle fast motion and reduce the mismatches, the mentioned algorithms are applied in different scales and linked using anisotropic diffusion equation in case of multiscale cross-correlation. This significantly improves the reliability of the motion detection in the presence of noise, inherent in case of SAR data.