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The spatial correlation of ice-sounding data can be used to trace internal isochronic layers and synchronize the age–depth relationship between different ice core sites, which is difficult using existing imaging methods. In this study, we propose a new algorithm to address the opportunity that applying spatial correlation to ice-sheet imaging offers. The algorithm is a spatial-correlation-based ice-sounding imaging method using fast back projection (FBP) that successfully improves the spatial correlation of imaging results with high efficiency. We give the specific steps to implement the algorithm and apply it to simulate both point targets and ice-sounding radar data to demonstrate its validity in imaging ice sheets. Furthermore, compared with two previous methods, the proposed algorithm improves the spatial correlation without degrading the ability of signal-to-noise ratio (SNR) improvement and processing efficiency. [ABSTRACT FROM AUTHOR]

During the last few decades, bed-elevation profiles from radar sounders have been used to quantify bed roughness. Various methods have been employed, such as the 'two-parameter' technique that considers vertical and slope irregularities in topography, but they struggle to incorporate roughness at multiple spatial scales leading to a breakdown in their depiction of bed roughness where the relief is most complex. In this article, we describe a new algorithm, analogous to wavelet transformations, to quantify the bed roughness at multiple scales. The 'Self-Adaptive Two-Parameter' system calculates the roughness of a bed profile using a frequency-domain method, allowing the extraction of three characteristic factors: (1) slope, (2) skewness and (3) coefficient of variation. The multi-scale roughness is derived by weighted-summing of these frequency-related factors. We use idealized bed elevations to initially validate the algorithm, and then actual bed-elevation data are used to compare the new roughness index with other methods. We show the new technique is an effective tool for quantifying bed roughness from radar data, paving the way for improved continental-wide depictions of bed roughness and incorporation of this information into ice flow models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]