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10 results on '"Ibikunle, Oluwanisola"'

1. Skip-WaveNet: A Wavelet based Multi-scale Architecture to Trace Firn Layers in Radar Echograms

Author

Varshney, Debvrat, Yari, Masoud, Ibikunle, Oluwanisola, Li, Jilu, Paden, John, and Rahnemoonfar, Maryam

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Echograms created from airborne radar sensors capture the profile of firn layers present on top of an ice sheet. Accurate tracking of these layers is essential to calculate the snow accumulation rates, which are required to investigate the contribution of polar ice cap melt to sea level rise. However, automatically processing the radar echograms to detect the underlying firn layers is a challenging problem. In our work, we develop wavelet-based multi-scale deep learning architectures for these radar echograms to improve firn layer detection. We show that wavelet based architectures improve the optimal dataset scale (ODS) and optimal image scale (OIS) F-scores by 3.99% and 3.7%, respectively, over the non-wavelet architecture. Further, our proposed Skip-WaveNet architecture generates new wavelets in each iteration, achieves higher generalizability as compared to state-of-the-art firn layer detection networks, and estimates layer depths with a mean absolute error of 3.31 pixels and 94.3% average precision. Such a network can be used by scientists to trace firn layers, calculate the annual snow accumulation rates, estimate the resulting surface mass balance of the ice sheet, and help project global sea level rise.

Published
2023

6. Smart Tracking of Internal Layers of Ice in Radar Data via Multi-Scale Learning

Author

Ibikunle Oluwanisola, Maryam Rahnemoonfar, Lora S. Koenig, Lynn Montgomery, Masoud Yari, and John Paden

Subjects
Artificial neural network, Computer science, business.industry, Deep learning, Missing data, Machine learning, computer.software_genre, law.invention, law, Radar imaging, Artificial intelligence, Noise (video), Radar, Scale (map), Transfer of learning, business, computer
Abstract

Artificial intelligence (AI) techniques have displayed impressive success in many practical fields. Deep neural networks (DNNs) owe their success to the availability of massive labeled data. However, in many real-world problems, even when a large dataset is available, deep learning methods have shown less success, due to causes such as lack of large labeled dataset, presence of noise in data, or missing data. In the present work, we intend to examine the application of deep learning methods on radar data gathered from polar regions. Our goal is to track internal ice layers in radar imagery. In such data, the presence of noise is one of the main obstacles in utilizing popular deep learning methods such as transfer learning. Our experiments show that if the neural network is trained to detect contours of objects in electro-optical imagery, it can only track a low percentage of contours in radar data. Fine-tuning and further training do not provide any better results. However, we will show that selecting the right model and training the model on the radar imagery from the base, is going to yield far better results. We also discuss another possible learning approach that can save us time for data annotation.

Published
2019
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10. Deep multi-scale learning for automatic tracking of internal layers of ice in radar data.

Author

Rahnemoonfar, Maryam, Yari, Masoud, Paden, John, Koenig, Lora, and Ibikunle, Oluwanisola

Subjects
AUTOMATIC tracking, DEEP learning, RADAR, ARTIFICIAL intelligence, REMOTE sensing, RADARSAT satellites
Abstract

In this study, our goal is to track internal ice layers on the Snow Radar data collected by NASA Operation IceBridge. We examine the application of deep learning methods on radar data gathered from polar regions. Artificial intelligence techniques have displayed impressive success in many practical fields. Deep neural networks owe their success to the availability of massive labeled data. However, in many real-world problems, even when a large dataset is available, deep learning methods have shown less success, due to causes such as lack of a large labeled dataset, presence of noise in the data or missing data. In our radar data, the presence of noise is one of the main obstacles in utilizing popular deep learning methods such as transfer learning. Our experiments show that if the neural network is trained to detect contours of objects in electro-optical imagery, it can only track a low percentage of contours in radar data. Fine-tuning and further training do not provide any better results. However, we show that selecting the right model and training it on the radar imagery from the start yields far better results. [ABSTRACT FROM AUTHOR]

Published
2021
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