Probabilistic graphical techniques for automated ice-bottom tracking and comparison between state-of-the-art solutions

We present improvements to existing frameworks for automated extraction of ice interfaces applied to two-dimensional and three-dimensional radar echograms of polar ice sheets. These improvements consist of novel image pre-processing steps and empirically-derived cost functions that allow for the integration of further domain-specific knowledge into the models employed. Along with an explanation of our modifications, we demonstrate the results obtained by our proposed models and algorithms, such as a 43% decrease in mean tracking error in the case of three-dimensional imagery. We also present the results obtained by several state-of-the-art ice-interface tracking solutions, and compare all automated results with manually-corrected ground-truth data. Furthermore, we perform a self-assessment of tracking results by analyzing the differences found between the automatically extracted ice-layers in cases where two separate radar measurements have been made at the same location

Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction

Deep learning methods have surpassed the performance of traditional techniques on a wide range of problems in computer vision, but nearly all of this work has studied consumer photos, where precisely correct output is often not critical. It is less clear how well these techniques may apply on structured prediction problems where fine-grained output with high precision is required, such as in scientific imaging domains. Here we consider the problem of segmenting echogram radar data collected from the polar ice sheets, which is challenging because segmentation boundaries are often very weak and there is a high degree of noise. We propose a multi-task spatiotemporal neural network that combines 3D ConvNets and Recurrent Neural Networks (RNNs) to estimate ice surface boundaries from sequences of tomographic radar images. We show that our model outperforms the state-of-the-art on this problem by (1) avoiding the need for hand-tuned parameters, (2) extracting multiple surfaces (ice-air and ice-bed) simultaneously, (3) requiring less non-visual metadata, and (4) being about 6 times faster.Comment: 10 pages, 7 figures, published in WACV 201

West Antarctica snow accumulation trend study (1979-2011) from Snow Radar and ice core profiles

Ice sheets are under threat from increasing air and ocean temperatures. For Antarctica, observed changes are most apparent near the margins; inland the effects of a warming atmosphere and changing circulation patterns are less clear. Snow accumulation to the ice sheet offsets ice losses near the margin, and characterizing ice sheet accumulation rate is necessary for understanding ice sheet mass balance and predicting future sea level rise. Ice penetrating radar systems enable the measurement of ice sheet properties beneath the surface, such as ice thickness and internal layering. This study concentrates on mapping the depth of internal layers, and linking the layers to a chronology that allows snow accumulation rates over particular time periods to be determined. The focus is on one particular ice penetrating radar system: Snow Radar from the Center for Remote Sensing of Ice Sheet (CReSIS). The Snow Radar is a 2-8 GHz ultra-wideband (UWB), frequency-modulated, continuous-wave (FMCW) radar, having a ~5cm vertical resolution. The chronology of Snow Radar detected layers is validated to be annual layers using nearby ice core data and the results of a regional climate model (RACMO2.1/ANT). The measurement error of a manual layer picking procedure, and proximity of ice core density profiles to the Snow Radar data have been examined. The results show that the average error variance in manual picking is as small as 3.0e-4 m, and that it is reasonable to use ice core density profiles in Snow Radar data processing. Using Snow Radar data, a snow accumulation rate time series has been determined along two flight lines over West Antarctica. The spatiotemporal distribution of snow accumulation has been analyzed and possible explanations for such distribution are discussed. No significant trend is found in snow accumulation during the 33-year study period (1979-2011). The snow accumulation spatial distribution has been related to topography and wind, showing that snow accumulation has a negative correlation with elevation and is generally lower on leeward slopes than on the windward slopes

AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network

This work is licensed under a Creative Commons Attribution 4.0 International License.Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network

Turbulent ship wakes and their spatiotemporal extent

Shipping activities occur in almost every part of the global oceans and in intensely trafficked shipping lanes there can be up to one ship passage every ten minutes. All these ships impact the marine environment in different ways through pollution or physical disturbance. This thesis is focused on the turbulent ship wake, a physical disturbance from ships and previously overlooked as an environmental impact. When a ship moves through water, the turbulence induced by the propeller and hull, will create a turbulent wake that remains and expands after the ship passage. The turbulence in the wake will govern the spread of contaminants and affect gas exchange in the wake water, physically perturb plankton, and potentially impact local biogeochemistry through increased vertical mixing.To be able to assess the environmental impact of ship-induced turbulence in areas with intense ship traffic, knowledge of the spatiotemporal extent and development of the turbulent wake is necessary. The aim of this thesis is to increase that knowledge, by conducting in situ observations of turbulent ship wakes, which can be used to estimate the spatiotemporal extent of the turbulent wake. By using a collection of methods, the thesis work has resulted in a first estimate of the spatiotemporal extent of the turbulent ship wake, based on more than 200 field observations of different real-size ships in natural conditions. The observed turbulent wakes showed large variation in their spatiotemporal extent, and further studies are needed to fully disentangle how environmental conditions and vessel specifications affect the intensity and extent of the turbulent wake. The results and experiences gained from the in situ observations, give an indication of the complexity entailed in characterising the development of the turbulent wake, and provide valuable input regarding the relevant parameters and spatiotemporal scales to include in future studies. The work of this thesis constitutes the first step in addressing the knowledge gap regarding the environmental impact of ship-induced turbulence and can be used as a road map for further studies within the field

January 1 - December 31, 2012

This report summarizes training, education, and outreach activities for calendar 2012 of PTI and affiliated organizations, including the School of Informatics and Computing, Office of the Vice President for Information Technology, and Maurer School of Law. Reported activities include those led by PTI Research Centers (Center for Applied Cybersecurity Research, Center for Research in Extreme Scale Technologies, Data to Insight Center, Digital Science Center) and Service and Cyberinfrastructure Centers (Research Technologies Division of University Information Technology Services, National Center for Genome Assembly Support