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

Yuan-hu, Cai; Hu, Shaobin; Lang, Shuyan; Guo, Yajun; Liu, Jiaqi

doi:10.3390/app10072501

Cited by 8 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The slow speed of the human-labeled method does not suit the processing of extensive ice sheet radar data; hence, researchers have sought more convenient layer-finding methods [18][19][20][21][22][23][36][37][38], such as traditional handcrafted feature methods and DL methods. Traditional methods to extract layers from radar sequences rely mainly on the Markov model [21,23].…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Literature [24] further improved the model by adding an Atrous Spatial Pyramid Pooling (ASPP) module to main network structure to control feature resolution. The research in [25] uses ResNet network to segment ice layer, thermal noise, echo free area and bedrock in radargram, getting overall accuracy similar to SVM based traditional machine learning method. Literature [26] used U-Net network and ASPP module to detect ice layer structure, classifying radargram into bedrock, echo free area, ice layer and basal ice, which is somewhat similar to basal units.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Basal Units beneath Antarctic Ice Sheet in Radargram Based on Deep Learning

Wang¹,

Xu²,

Lang³

et al. 2023

AETR

View full text Add to dashboard Cite

Sea level rise, caused by accelerated melting of glaciers in Greenland and Antarctica in recent decades, has become a major concern in scientific, environmental, and political arenas. A comprehensive study of subglacial conditions and processes is particularly important for reliable analysis about their future evolution. Basal units – visibly distinct englacial structures near ice-bedrock interface, could provide substantial insight into subglacial processes, ice-sheet dynamic history and dramatically influence the flow of surrounding ice. In order to enable improved characterization of these features, we develop and apply an algorithm that allows for automatic detection of basal units, therefore, an algorithm based on deep learning is proposed. Compared with traditional method based on manual feature extraction, proposed algorithm can achieve completely automatic recognition of basal units in radargram. The network is mainly composed of ResNet and ASPP module, which can achieve high accuracy in very small dataset. We use radar data collected by Polar Geophysics Group (PGG) of The Earth Institute at Columbia University in Antarctica in 2008-2009 and 2009-2010 for experiments, results confirm the effectiveness and robustness of proposed algorithm. At the same time, a more rapid deep learning method is tried, which uses the lightweight network MobileNet V3 as backbone, obtaining a network structure that can save 81.8 percent of training time and 92.2 percent of processing time with high accuracy. It provides a possibility for rapid network training, application on mobile devices and real-time processing of radargram.

show abstract

Accurate prediction of ice surface and bottom boundary based on multi-scale feature fusion network

Yuan-hu

Wan²,

Hu³

et al. 2022

Appl Intell

View full text Add to dashboard Cite

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

Cited by 8 publications

References 27 publications

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

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

Automatic Detection of Basal Units beneath Antarctic Ice Sheet in Radargram Based on Deep Learning

Accurate prediction of ice surface and bottom boundary based on multi-scale feature fusion network

Contact Info

Product

Resources

About