SAR image texture tracking using a pointwise graph-based model for glacier displacement measurement

This paper investigates the problem of glacier flow estimation using Synthetic Aperture Radar (SAR) image data. Our motivation is to exploit a weighted graph model constructed from characteristic points (i.e. keypoints) to measure the displacement vectors located at their positions. In fact, characteristic points are capable of capturing the image's radiometric and contextual information. Then, by encoding their interaction and inter-connection, a graph model is able to characterize both intensity and geometry information from the image content, which is relevant for texture tracking task. In this work, we employ a graph-based similarity measure to track the local texture information around each keypoint in order to figure out its correspondence from the other image and calculate the associated displacement. The proposed approach is tested and evaluated using high resolution TerraSAR-X images acquired from the Argentiere Glacier located in the French Alps. Our preliminary experimental results show the algorithm's capacity to provide a fast and reliable estimation of glacier flows, especially over highly textured and structured regions.