DEEP LEARNING ALGORITHMS FOR RADAR ECHOGRAM LAYER TRACKING

The accelerated melting of ice sheets in the polar regions of the world, specifically in Greenland and Antarctica, due to contemporary climate warming is contributing to global sea level rise. To understand and quantify this phenomenon, airborne radars have been deployed to create echogram images that map snow accumulation patterns in these regions. Using advanced radar systems developed by the Center for Remote Sensing and Integrated Systems (CReSIS), a significant amount (1.5 petabytes) of climate data has been collected. However, the process of extracting ice phenomenology information, such as accumulation rate, from the data is limited. This is because the radar echograms require tracking of the internal layers, a task that is still largely manual and time-consuming. Therefore, there is a need for automated tracking.

Machine learning and deep learning algorithms are well-suited for this problem given their near-human performance on optical images. Moreover, the significant overlap between classical radar signal processing and machine learning techniques suggests that fusion of concepts from both fields can lead to optimized solutions for the problem. However, supervised deep learning algorithms suffer the circular problem of first requiring large amounts of labeled data to train the models which do not exist currently.

In this work, we propose custom algorithms, including supervised, semi-supervised, and self-supervised approaches, to deal with the limited annotated data problem to achieve accurate tracking of radiostratigraphic layers in echograms. Firstly, we propose an iterative multi-class classification algorithm, called “Row Block,” which sequentially tracks internal layers from the top to the bottom of an echogram given the surface location. We aim to use the trained iterative model in an active learning paradigm to progressively increase the labeled dataset. We also investigate various deep learning semantic segmentation algorithms by casting the echogram layer tracking problem as a binary and multiclass classification problem. These require post-processing to create the desired vector-layer annotations, hence, we propose a custom connected-component algorithm as a post-processing routine. Additionally, we propose end-to-end algorithms that avoid the post-processing to directly create annotations as vectors. Furthermore, we propose semi-supervised algorithms using weakly-labeled annotations and unsupervised algorithms that can learn the latent distribution of echogram snow layers while reconstructing echogram images from a sparse embedding representation.

A concurrent objective of this work is to provide the deep learning and science community with a large fully-annotated dataset. To achieve this, we propose synchronizing radar data with outputs from a regional climate model to provide a dataset with overlapping measurements that can enhance the performance of the trained models.