Arctic sea ice motion retrieval from multisource SAR images using a keypoint-free feature tracking algorithm

The rapid changes in Arctic sea ice serve as an important indicator for the global climate system. Remote sensing-based sea ice motion (SIM) monitoring has become a key technological tool in polar environment research. Traditional feature tracking algorithms, such as ORB and A-KAZE, have certain limitations in Synthetic Aperture Radar (SAR) image-based SIM retrieval. We propose a novel deep learning-based feature tracking framework. First, a phase consistency edge enhancement module is used to extract edge structure features from SAR images, improving the uniformity of feature distribution. Next, a geographic location constraint attention mechanism is designed, embedding the physical model of SIM into the Transformer architecture. By establishing a mapping relationship between geographic coordinates and feature space, sparse self-attention and region-focused cross-attention are guided. This significantly enhances the generalization ability of feature representation and improves matching efficiency. The experiments use multi-source SAR data from Sentinel-1, ALOS-2, C-SAR, Envisat ASAR, and RadarSat-2 to construct a test set. Experimental results show that, compared to traditional methods, the proposed algorithm not only greatly reduces computational time but also improves the accuracy of motion vector speed and direction by approximately 50% on multi-source SAR images. The deep learning feature tracking framework developed in this study provides new technical support and research perspectives for Arctic sea ice dynamics.