Automatic Ice Surface and Bottom Boundaries Estimation in Radar Imagery Based on Level-Set Approach

Rahnemoonfar, Maryam; Fox, Geoffrey; Yari, Masoud; Paden, John

doi:10.1109/tgrs.2017.2702200

Cited by 28 publications

(16 citation statements)

References 21 publications

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“…Another critical area of innovation in radioglaciological data processing and analysis is automatic methods for radargram image interpretation. These include algorithms for layer tracking, bed and surface mapping and basal feature categorization (Sime and others, 2011; Crandall and others, 2012; Ferro and Bruzzone, 2012; Ilisei and Bruzzone, 2015; Panton and Karlsson, 2015; Carrer and Bruzzone, 2016; Rahnemoonfar and others, 2017; Berger and others, 2018; Donini and others, 2019). Success of these approaches is a prerequisite to be able to cope efficiently with the data volume of future surveys and effectively exploit their information content.…”

Section: Processingmentioning

confidence: 99%

Five decades of radioglaciology

et al. 2020

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Radar sounding is a powerful geophysical approach for characterizing the subsurface conditions of terrestrial and planetary ice masses at local to global scales. As a result, a wide array of orbital, airborne, ground-based, and in situ instruments, platforms and data analysis approaches for radioglaciology have been developed, applied or proposed. Terrestrially, airborne radar sounding has been used in glaciology to observe ice thickness, basal topography and englacial layers for five decades. More recently, radar sounding data have also been exploited to estimate the extent and configuration of subglacial water, the geometry of subglacial bedforms and the subglacial and englacial thermal states of ice sheets. Planetary radar sounders have observed, or are planned to observe, the subsurfaces and near-surfaces of Mars, Earth's Moon, comets and the icy moons of Jupiter. In this review paper, and the thematic issue of the Annals of Glaciology on ‘Five decades of radioglaciology’ to which it belongs, we present recent advances in the fields of radar systems, missions, signal processing, data analysis, modeling and scientific interpretation. Our review presents progress in these fields since the last radio-glaciological Annals of Glaciology issue of 2014, the context of their history and future prospects.

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Section: Processingmentioning

confidence: 99%

Five decades of radioglaciology

et al. 2020

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“…Newer work in this field utilizes image processing, computer vision, and deep learning techniques to automatically or semi-automatically determine ice surface and bottom boundaries from echograms [1][2][3][4][5][6][7]. Gifford et al [1] employs both the edge-based and active contour methodologies to automate the task of locating polar ice and bedrock layers from airborne radar data acquired over Greenland and Antarctica.…”

Section: Introductionmentioning

confidence: 99%

“…Mitchell et al [2] uses a level-set technique for estimating bedrock and surface layers, but find it problematic because of the need to reinitialize the curve manually for each radar image. Therefore, Rahnemoonfar et al [3] introduces the distance regularization term, which essentially maintains the regularity of the level-set and leads to a stable numerical procedure without the need for reinitialization. Recently, Rahnemoonfar et al [4] proposes a novel approach that automatically detects the complex topology of the ice surface and bottom boundaries based on the charged particle concept.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery

Yuan-hu

Lang

et al. 2020

Applied Sciences

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Sea level rise, caused by the accelerated melting of glaciers in Greenland and Antarctica in recent decades, has become a major concern in the scientific, environmental, and political arenas. A comprehensive study of the properties of the ice subsurface targets is particularly important for a reliable analysis of their future evolution. Newer deep learning techniques greatly outperform the traditional techniques based on hand-crafted feature engineering. Therefore, we propose an efficient end-to-end network for the automatic classification of ice sheet subsurface targets in radar imagery. Our network uses bilateral filtering to reduce noise and consists of ResNet module, improved Atrous Spatial Pyramid Pooling (ASPP) module, and decoder module. With radar images provided by the Center of Remote Sensing of Ice Sheets (CReSIS) from 2009 to 2011 as our training and testing data, experimental results confirm the robustness and effectiveness of the proposed network in radargram.

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“…Several semi-automated and automated methods exist for layer finding and estimating ice thickness in radar images [1][2][3][4][5][6]. Crandall et al [1] used probabilistic graphical models for detecting the ice layer boundary in echogram images from Greenland and Antarctica.…”

Section: Introductionmentioning

confidence: 99%

“…However, for every single image, the user needs to re-initialize the curve manually and as a result, the method is quite slow and was applied only to a small dataset. This problem was fixed in [4,5], where authors introduced a distance regularization term in the level set approach to maintain the regularity of level set intrinsically. Therefore, it does not need any manual re-initialization and was automatically applied to a large dataset.…”

Section: Introductionmentioning

confidence: 99%

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

Rahnemoonfar

Johnson

Paden

2019

Sensors

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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.

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Automatic Ice Surface and Bottom Boundaries Estimation in Radar Imagery Based on Level-Set Approach

Cited by 28 publications

References 21 publications

Five decades of radioglaciology

Five decades of radioglaciology

End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery

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

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