A semi-automatic approach for estimating near surface internal layers from snow radar imagery

Mitchell, Jerome E.; Crandall, David J.; Fox, Geoffrey; Paden, John

doi:10.1109/igarss.2013.6723737

Cited by 15 publications

(9 citation statements)

References 12 publications

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“…In addition to these works, which deal with the segmentation of the ice sheet subsurface into different regions, there are a few works focused on understanding the ice stratigraphy, which is useful for ice flow modeling [12] or the isochronous characterization of the ice [13]. These objectives have motivated many efforts for developing automated methods (e.g., [14]- [16]). Other methods focus on the detection of water at the ice/bedrock interface.…”

Section: Rs Data Processing and Related Work In Glaciologymentioning

confidence: 99%

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A System for the Automatic Classification of Ice Sheet Subsurface Targets in Radar Sounder Data

Ilisei

Bruzzone

2015

IEEE Trans. Geosci. Remote Sensing

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Exhaustive investigations of the ice sheet subsurface can be carried out by analyzing the information contained in the huge archives of radargrams acquired by dedicated radar sounder (RS) instruments. The analysis can be done by using properly designed automatic techniques for a quantitative, objective, and reliable extraction of information from radargrams. Unfortunately, the definition and development of such automatic techniques have only been marginally addressed in the literature. In this paper, we propose a novel and efficient system for the automatic classification of ice subsurface targets present in radargrams. The core of the system is represented by the extraction of a set of features for target discrimination. The features are based on both the specific statistical properties of the RS signal and the spatial distribution of the ice subsurface targets. Such features are then provided as input to an automatic classifier based on support vector machine. Experimental results obtained on two real-world data sets acquired by airborne-mounted RSs in large regions of Antarctica confirm the robustness and effectiveness of the proposed classification system.

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Section: Rs Data Processing and Related Work In Glaciologymentioning

confidence: 99%

“…surf a is automatically detected for each trace of the radargram as the position of the maximum return along the trace. This is a fast and simple approach that has been employed in other works for the analysis of airborne acquisitions (e.g., [16]). Thus…”

Section: ) Range Position Of the Subsurface Targetsmentioning

confidence: 99%

A System for the Automatic Classification of Ice Sheet Subsurface Targets in Radar Sounder Data

Ilisei

Bruzzone

2015

IEEE Trans. Geosci. Remote Sensing

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“…Therefore, it is a great challenge to extract useful information from echograms accurately and efficiently. The manual labeled method, which is commonly used to mark important ice layer information [14][15][16][17][18], is a highly time-consuming and tedious task. Thus, researchers began to explore semi-automatic and automatic methods to quickly and accurately extract ice layer locations [18][19][20][21][22][23][24][25][26].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Attention Multi-Scale Network for Automatic Layer Extraction of Ice Radar Topological Sequences

et al. 2021

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Analyzing the surface and bedrock locations in radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume and how they may contribute to global climate change. However, the traditional handcrafted methods cannot quickly provide quantitative, objective and reliable extraction of information from radargrams. Most traditional handcrafted methods, designed to detect ice-surface and ice-bed layers from ice sheet radargrams, require complex human involvement and are difficult to apply to large datasets, while deep learning methods can obtain better results in a generalized way. In this study, an end-to-end multi-scale attention network (MsANet) is proposed to realize the estimation and reconstruction of layers in sequences of ice sheet radar tomographic images. First, we use an improved 3D convolutional network, C3D-M, whose first full connection layer is replaced by a convolution unit to better maintain the spatial relativity of ice layer features, as the backbone. Then, an adjustable multi-scale module uses different scale filters to learn scale information to enhance the feature extraction capabilities of the network. Finally, an attention module extended to 3D space removes a redundant bottleneck unit to better fuse and refine ice layer features. Radar sequential images collected by the Center of Remote Sensing of Ice Sheets in 2014 are used as training and testing data. Compared with state-of-the-art deep learning methods, the MsANet shows a 10% reduction (2.14 pixels) on the measurement of average mean absolute column-wise error for detecting the ice-surface and ice-bottom layers, runs faster and uses approximately 12 million fewer parameters.

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“…While several recent papers have proposed automated techniques for segmenting layer boundaries in ice [5,8,12,13,17,23,25,26,35], none have approached the accuracy of even an undergraduate student annotator [24], much less an expert. However, these techniques have all relied on traditional image processing and computer vision techniques, like edge detection, pixel template models, active contour models, etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-task Spatiotemporal Neural Networks for Structured Surface Reconstruction

Cheng

Paden

et al. 2018

2018 IEEE Winter Conference on Applications of Computer Vision (WACV)

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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.

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A semi-automatic approach for estimating near surface internal layers from snow radar imagery

Cited by 15 publications

References 12 publications

A System for the Automatic Classification of Ice Sheet Subsurface Targets in Radar Sounder Data

A System for the Automatic Classification of Ice Sheet Subsurface Targets in Radar Sounder Data

Attention Multi-Scale Network for Automatic Layer Extraction of Ice Radar Topological Sequences

Multi-task Spatiotemporal Neural Networks for Structured Surface Reconstruction

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