Deep Learning on Airborne Radar Echograms for Tracing Snow Accumulation Layers of the Greenland Ice Sheet
Abstract
:1. Introduction
2. Related Work
2.1. Ice Layer Tracking Techniques
2.1.1. Ice Surface and Bottom Detection
2.1.2. Internal Ice Layer Detection
2.2. Fully Convolutional Networks
3. Dataset
3.1. Characteristics
3.2. Challenges
4. Methodology
4.1. Pre-Processing
4.1.1. Image Cropping
4.1.2. Denoising
4.2. FCN Architectures
4.2.1. UNet
4.2.2. PSPNet
4.2.3. DeepLabv3+
4.3. Tracing of Annual Snow Accumulation Layers
5. Experimental Setup
- Semantic segmentation of EG-2012:- EG’s 2012 data contains more than two thousand images having a variety of features, from those containing only the ice surface, to those having twenty to thirty internal layers. Hence, we use this data subset to find which of the three state-of-the-art FCNs will be useful for ice layer tracking. We keep 260 images for testing and the remaining 2361 for training. We train each network with two different learning rate strategies for 200 epochs each.
- Training on past data and predicting on future data:- For this category of experiments, we train the most generalized network obtained over EG-2012 and train it on the entire EG dataset, i.e., over the years 2009 to 2012. We then predict the ice layers on ‘future years’, 2013–2017, from the SG dataset. Training on past data before 2012, and testing on data post-2012, would give us a sense of applicability of our current method for future datasets, in general. We train on past years of EG and not of SG because the former has a larger, and varied, number of images. Further, we experiment with denoising all the images before feeding them into the FCN in order to improve layer detection. In this category, we train the network for 70 epochs.
- Training and testing on combined dataset:- Here, we use the network which gave the least mean absolute error (described below) over EG-2012 and train it on the entire Snow Radar data we have, i.e., EG and SG combined. We split the dataset into 80% for training and 20% for testing. As this is a large dataset, it would help in nourishing FCN. Here, again, we experiment with training on both regular and denoised images, for 70 epochs.
5.1. Hyperparameters
5.2. Evaluation Metrics
6. Results and Discussion
6.1. Segmentation and Thickness Estimation of EG-2012
6.2. Training on Past Data and Predicting on Future Data
6.3. Training and Testing on Combined Dataset
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Dataset | Region | Timespan (Years) | Number of Images |
---|---|---|---|
EG | Entire Greenland | 2009 to 2012 | 21,445 |
SG | Southeast Greenland | 2009 to 2017 | 2037 |
EG-2012 | Entire Greenland | 2012 | 2621 |
Network-LRS | Train | Test |
---|---|---|
UNet-Poly | 0.755 | 0.714 |
UNet-OneCycle | 0.856 | 0.792 |
PSPNet-Poly | 0.948 | 0.899 |
PSPNet-OneCycle | 0.938 | 0.867 |
DeepLabv3+-Poly | 0.957 | 0.887 |
DeepLabv3+-OneCycle | 0.935 | 0.886 |
Network-LRS | Train | Test |
---|---|---|
UNet-Poly | 0.387 | 0.343 |
UNet-OneCycle | 0.549 | 0.438 |
PSPNet-Poly | 0.737 | 0.650 |
PSPNet-OneCycle | 0.728 | 0.589 |
DeepLabv3+-Poly | 0.734 | 0.590 |
DeepLabv3+-OneCycle | 0.676 | 0.595 |
Network-LRS | Train | Test |
---|---|---|
UNet-Poly | 7.95 | 8.75 |
UNet-OneCycle | 5.22 | 6.17 |
PSPNet-Poly | 2.80 | 3.63 |
PSPNet-OneCycle | 4.03 | 5.62 |
DeepLabv3+-Poly | 2.36 | 3.75 |
DeepLabv3+-OneCycle | 3.08 | 3.59 |
Year | Vertical Pixel Size (cm) | Accuracy (%) | F-Score (%) | MAE (Pixels) |
---|---|---|---|---|
2009 | 1.42 | 87.3 | 82.2 | 1.27 |
2010 | 1.39 | 93.5 | 88.0 | 0.75 |
2011 | 3.19 | 92.1 | 86.5 | 0.54 |
2012 | 2.07 | 90.2 | 85.1 | 1.36 |
Year | Vertical Pixel Size (cm) | Accuracy (%) | F-Score (%) | MAE (Pixels) |
---|---|---|---|---|
2009 | 1.39 | 94.8 | 88.2 | 5.97 |
2010 | 1.37 | 90.7 | 85.1 | 3.26 |
2011 | 1.54 | 87.0 | 82.1 | 2.19 |
2012 | 1.03 | 87.7 | 82.8 | 6.41 |
2013 | 2.11 | 88.5 | 83.4 | 0.98 |
2014 | 1.03 | 89.3 | 84.2 | 2.01 |
2015 | 1.05 | 90.2 | 85.3 | 7.25 |
2016 | 1.05 | 89.6 | 85.1 | 7.28 |
2017 | 0.83 | 89.8 | 85.3 | 5.79 |
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Varshney, D.; Rahnemoonfar, M.; Yari, M.; Paden, J.; Ibikunle, O.; Li, J. Deep Learning on Airborne Radar Echograms for Tracing Snow Accumulation Layers of the Greenland Ice Sheet. Remote Sens. 2021, 13, 2707. https://doi.org/10.3390/rs13142707
Varshney D, Rahnemoonfar M, Yari M, Paden J, Ibikunle O, Li J. Deep Learning on Airborne Radar Echograms for Tracing Snow Accumulation Layers of the Greenland Ice Sheet. Remote Sensing. 2021; 13(14):2707. https://doi.org/10.3390/rs13142707
Chicago/Turabian StyleVarshney, Debvrat, Maryam Rahnemoonfar, Masoud Yari, John Paden, Oluwanisola Ibikunle, and Jilu Li. 2021. "Deep Learning on Airborne Radar Echograms for Tracing Snow Accumulation Layers of the Greenland Ice Sheet" Remote Sensing 13, no. 14: 2707. https://doi.org/10.3390/rs13142707