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Identifying the locations of ice surface and bottom boundary in the radar imagery enables the calculation of ice sheet thickness, which is one of important inputs for ice-sheet modelling and global climate research. Therefore, accurate predictions of the boundaries can contribute to improve the accuracy of global climate analysis and sea level prediction. However, an accurate boundary detection in radar sounder data collected from the polar ice sheet has still been a challenge because the boundaries of the ice layer are usually very weak and noisy, and subglacial topography is highly variable. In recent years, the deep learning methods have surpassed the performances of traditional technology and helped to overcome a series of problems, including image boundary segmentation and target detection. This paper proposes a multi-scale feature fusion network (MFFN) for boundary detection of ice sheet radar echograms, where the ground truth supervises the output of the network at different stages, rather than the output of the last layer of the network. Also, a multi-scale convolution module (MCM) is introduced to learn the rich multi-scale representation of each network stage from shallow to deep, which uses convolution with different dilation rates to obtain multi-scale features. Furthermore, an improved loss function makes the proposed MFFN more effective to solve the sample imbalance problem of boundary detection, and further improves the accuracy of boundary detection. The proposed method is verified experimentally using the radar echograms from 2009 provided by the Center of Remote Sensing of Ice Sheets (CReSIS) that are used as training and test data. In the experiments, the proposed MFFN not only achieves state-of-the-art boundary detection accuracy on the test set but also improves the visual effect by generating fine boundaries. [ABSTRACT FROM AUTHOR]

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]

In order to determine a great capacity of ice thickness assessments in a short time on-site, we have designed a real-time processor by using the modern commercial DSP chip named TMS320C6678. It is essential to develop an appropriate real-time imaging algorithm adapted to this real-time processor. In this paper, we propose a novel real-time imaging algorithm named the range-Doppler algorithm (RDA) integrated with the shift-and-correlate (SAC) algorithm. The proposed method extends the standard RDA to be appropriate for numerous media while adding the SAC algorithm to realize azimuth FM rate estimation to obtain accurate echograms of interior reflecting horizons (IRHs) and bedrock of the ice sheets at different depths. Initially, theoretical study is performed to the introduced method. Next, the hardware architecture of the designed real-time embedded system is presented, as well as the implementation of the proposed method on this processor. As a final point, the method is applied to the replication point targets and high-resolution ice-sounding radar information in order to verify its correctness of ice layers picturing. Through the presented echograms of ice sheets and the comparisons with both preceding methods in both main features, we can establish that the designed real-time processor accompany with the proposed RDA integrated with the SAC algorithm is quite reliable to generate the high-resolution ice-sounding images deprived of lowering the capability to reduce the azimuth clutter. [ABSTRACT FROM AUTHOR]

This paper presents the ice thickness, fine resolution internal reflecting horizons (IRHs), and distinct bottom topography measurements of Chinese Kunlun Station and Grove Mountains, Antarctica, derived from sounding these glaciers with a high-resolution radar. To enable the development of next-generation ice-sheet models, we need information on IRHs, bottom topography, and basal conditions. To this end, we performed measurements with the progressively improved ice-sounding radar system, currently known as the high-resolution ice-sounding radar developed by the Key Laboratory of Electromagnetic Radiation and Sensing Technology of Institute of Electronics, Chinese Academy of Sciences, Beijing, China. We processed the collected data using focused synthetic aperture radar (SAR) algorithm named the modified range migration algorithm using curvelets and the modified nonlinear chirp scaling algorithm to improve radar sensitivity and reduce along-track surface clutter. Representative results from selected transects indicate that we successfully sounded 3-km-thick ice with a fine resolution of 0.75 m. In this paper, we provide a brief description of the radar system, discuss the focused SAR processing algorithms, and provide sample results to demonstrate the successful sounding of the ice sheet in Antarctica. [ABSTRACT FROM AUTHOR]

We develop and then demonstrate a modified planar subarray processing algorithm based on the inverse scaled Fourier transform applied to very high frequency ice-sounding data that produces swath measurements of ice sheet surface topography, ice thickness, and radar reflectivity of both internal reflecting horizons and bedrock of the ice sheet. It is a real-time ice-sounding imaging method. First, theory analysis has been carried on the proposed algorithm. Then, we give the particular realizing steps to implement this algorithm. Finally, we apply this algorithm to the simulation point targets and real data collected during the 29th Chinese Antarctic Research Expedition to prove its validity of imaging of ice sheets. Furthermore, compared with two previous algorithms in two major aspects—the power of azimuth clutter reduction and calculating time—the proposed algorithm could considerably reduce the imaging time to meet the requirement of real-time imaging of ice-sounding data without degrading the ability of azimuth clutter reduction via C+MPI language on a parallel computer system. [ABSTRACT FROM PUBLISHER]

We develop and demonstrate a new method to address the surface clutter problem in imaging of ice sheets by airborne radar sounder. This new method utilizes the modified range migration algorithm based on inverse scaled Fourier transform (ISFT) to reduce along-track clutter and the beamforming using curvelets to reduce cross-track clutter. First, theory analysis has been carried on to the proposed method. Then, we give the particular realizing steps to implement this method. At last, we apply this method to the simulation point targets and MCoRDS data to prove its validity of imaging of ice sheets. Furthermore, we show the advantages of the proposed method through comparison with two methods in four major aspects—the power of along-track clutter reduction, the power of cross-track clutter reduction, the equivalent number of looks (ENLs), and the calculating time. [ABSTRACT FROM PUBLISHER]

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]