Two articles:
- A Multiscale Joint Deep Neural Network for Glacier Contour Extraction |
- Machine Learning Based Imputation of Mountain Snowpack Depth within an Operational LiDAR Sampling Framework in Southwest Alberta
A Multiscale Joint Deep Neural Network for Glacier Contour Extraction
Rapid and accurate acquisition of glacier regional changes is of great significance to the study of glaciers. Among all satellite images, Synthetic Aperture Radar (SAR) data has a great advantage in monitoring the glaciers in harsh weather conditions. Conventionally, glacier boundaries are manually delineated on images. However, this is a time-consuming process, especially in the batch process of large-area data. In this paper, we propose a Multiscale Joint Deep Neural Network (MJ-DNN) for large-scale glaciers contour extraction using single-polarimetric SAR intensity images. Based on U-Net, the proposed method has been improved in three aspects. First, Atrous Separable Convolution is used instead of convolution with the down-sampling part. Second, we propose a multiscale joint convolution layer to obtain information at multiple scales. Third, we deepen the network with the residual connection structure for higher-level features. At the final layer, Continue reading …
Guest Editors
Jinzhou Liu, Li Fang, Huifang Shen & Shudong Zhou
tandfonline.com/doi/full/10.1080/07038992.2021.1986810
Machine Learning Based Imputation of Mountain Snowpack Depth within an Operational LiDAR Sampling Framework in Southwest Alberta
Airborne LiDAR can support high resolution watershed-scale snow depth mapping that provides the spatial coverage necessary to inform water supply forecasts for mountainous headwaters. This research utilized LIDAR and machine learning to evaluate snow depth drivers and to assess the feasibility of sampling datasets for the spatial imputation of snow depth at the watershed-scale under mid-winter and melt onset conditions. We present a Random Forest based method of extrapolating LiDAR snow depth model values from two flight lines, with insights for future operational use. Models of watershed-scale snow depth developed from spatially constrained flightline training samples correlated with more spatially widespread LiDAR snow depth data but were outperformed by models generated from training data sampled across the entire watershed. Random Forest simulations produced R2 values ranging from 0.41 to 0.61 and RMSE values from 0.7 m to 1.0 m (p < 0.05). By evaluating the performance of snow depth drivers within each simulation Continue reading …
Guest Editors
Kelsey Cartwright, Craig Mahoney & Chris Hopkinson
tandfonline.com/doi/full/10.1080/07038992.2021.1988540
