TY - GEN
T1 - Deep Ice Layer Tracking and Thickness Estimation using Fully Convolutional Networks
AU - Varshney, Debvrat
AU - Rahnemoonfar, Maryam
AU - Yari, Masoud
AU - Paden, John
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/10
Y1 - 2020/12/10
N2 - Global warming is rapidly reducing glaciers and ice sheets across the world. Real time assessment of this reduction is required so as to monitor its global climatic impact. In this paper, we introduce a novel way of estimating the thickness of each internal ice layer using Snow Radar images and Fully Convolutional Networks. The estimated thickness can be used to understand snow accumulation each year. To understand the depth and structure of each internal ice layer, we perform multiclass semantic segmentation on radar images, which hasn't been performed before. As the radar images lack good training labels, we carry out a pre-processing technique to get a clean set of labels. After detecting each ice layer uniquely, we calculate its thickness and compare it with the processed ground truth. This is the first time that each ice layer is detected separately and its thickness calculated through automated techniques. Through this procedure we were able to estimate the ice-layer thicknesses within a Mean Absolute Error of approximately 3.6 pixels. Such a Deep Learning based method can be used with ever-increasing datasets to make accurate assessments for cryospheric studies.
AB - Global warming is rapidly reducing glaciers and ice sheets across the world. Real time assessment of this reduction is required so as to monitor its global climatic impact. In this paper, we introduce a novel way of estimating the thickness of each internal ice layer using Snow Radar images and Fully Convolutional Networks. The estimated thickness can be used to understand snow accumulation each year. To understand the depth and structure of each internal ice layer, we perform multiclass semantic segmentation on radar images, which hasn't been performed before. As the radar images lack good training labels, we carry out a pre-processing technique to get a clean set of labels. After detecting each ice layer uniquely, we calculate its thickness and compare it with the processed ground truth. This is the first time that each ice layer is detected separately and its thickness calculated through automated techniques. Through this procedure we were able to estimate the ice-layer thicknesses within a Mean Absolute Error of approximately 3.6 pixels. Such a Deep Learning based method can be used with ever-increasing datasets to make accurate assessments for cryospheric studies.
KW - Fully Convolutional Networks
KW - Ice Layer Thickness
KW - Radargrams
KW - Semantic Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85103857284&partnerID=8YFLogxK
U2 - 10.1109/BigData50022.2020.9378070
DO - 10.1109/BigData50022.2020.9378070
M3 - Conference contribution
AN - SCOPUS:85103857284
T3 - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
SP - 3943
EP - 3952
BT - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
A2 - Wu, Xintao
A2 - Jermaine, Chris
A2 - Xiong, Li
A2 - Hu, Xiaohua Tony
A2 - Kotevska, Olivera
A2 - Lu, Siyuan
A2 - Xu, Weijia
A2 - Aluru, Srinivas
A2 - Zhai, Chengxiang
A2 - Al-Masri, Eyhab
A2 - Chen, Zhiyuan
A2 - Saltz, Jeff
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Big Data, Big Data 2020
Y2 - 10 December 2020 through 13 December 2020
ER -