Supervised Multi-Task Learning for Tracking Inland Glacier Flows Using Sentinel-1 TOPS Data

Abstract

Multi-swath SAR interferometry is a powerful tool for assessing sub-wavelength changes over large-scale areas. The azimuth variation of the line of sight (LOS) induces phase jumps between adjacent bursts in the interferograms which contain useful information about the motion. In this work, we present a multitask convolutional neural network that simultaneously decouples the interferometric phase due to displacements in the LOS direction from that due to displacements in the along-track direction, and predicts a proxy for the along-track displacement. We show results using a single pair of Sentinel-1 acquisitions over the inland region of Greenland, where glacier flows occur in the winter season within the revisit time.