A new image processing method for discriminating internal layers from radio echo sounding data of ice sheets via a combined robust principal component analysis and total variation approach

Discriminating internal layers by radio echo sounding is important in analyzing the thickness and ice deposits in the Antarctic ice sheet. The signal processing method of synthesis aperture radar （SAR） has been widely used for improving the signal to noise ratio （SNR） and discriminating internal layers by radio echo sounding data of ice sheets. This method is not efficient when we use edge detection operators to obtain accurate information of the layers, especially the ice-bed interface. This paper presents a new image processing method via a combined robust principal component analysis-total variation （RPCA-TV） ap- proach for discriminating internal layers of ice sheets by radio echo sounding data. The RPCA-based method is adopted to project the high-dimensional observations to low-dimensional subspace structure to accelerate the operation of the TV-based method, which is used to discriminate the internal layers. The efficiency of the presented method has been tested on simulation data and the dataset of the Institute of Electronics, Chinese Academy of Sciences, collected during CHINARE 28. The results show that the new method is more efficient than the previous method in discriminating internal layers of ice sheets by radio echo sounding data.