Recover Glacier Velocity Fields Derived From the SAR Speckle Tracking Technique Using Artificial Neural Network

The speckle tracking technique has demonstrated its great potential in glacier velocity field (GVF) mapping applications. It analyzes the cross correlation between two synthetic aperture radar (SAR) images, which is capable of providing 2-D glacier motion measurements with acceptable accuracy. Since the coherence between a SAR image pair illuminating glacier areas cannot be preserved everywhere in nearly all cases, inevitable no data areas will be presented in SAR speckle tracking products. In this letter, a relatively convenient method is proposed to recover a GVF generated by the speckle tracking technique. This method considers a GVF recovery problem to be a unitary supervised training problem and resolve it via an artificial neural network. A targeted architecture of the network for recovering a GVF is presented. Moreover, the parameters correlative with glacier motion mechanism are proposed to be introduced into the network, which is able to effectively improve the performance of GVF recovery. For the purpose of validation, the proposed method is compared with the well-known Kriging interpolation method based on real speckle tracking products. The experimental results demonstrate that the proposed method can effectively recover a GVF derived from the speckle tracking technique.