An Automatic Method for Subglacial Lake Detection in Ice Sheet Radar Sounder Data

• Published in: IEEE Transactions on Geoscience and Remote Sensing
• Main Authors: Ilisei A. -M., Khodadadzadeh M., Ferro A., Bruzzone L.
• Other Authors: Ilisei, A. -M., Khodadadzadeh, M., Ferro, A., Bruzzone, L.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2019
• Subjects: Automatic detection; ice sheet; radar sounder (RS); remote sensing; subglacial lakes (SLs); East Antarctica; Siple; Siple Coast; Antarc*; Antarctica; Ice Sheet
• Online Access: http://hdl.handle.net/11572/250909; https://doi.org/10.1109/TGRS.2018.2882911

During the past decades, radar sounder (RS) instruments have been effectively used to detect subglacial lakes (SLs). SLs appear as flat, smooth, and bright reflectors in RS radargrams. The visual interpretation has been the main approach to SL detection in radargrams. However, this approach is subjective and inappropriate for processing large amounts of radargrams. While the analysis of RS data for understanding the subglacial hydrology has recently received increased attention, the literature on the development of automatic methods specifically designed for SL detection is still limited. In order to fill this gap, in this paper, we propose a novel automatic technique for SL detection. The technique is made up of two steps: 1) feature extraction and 2) automatic detection. In the first step, we define and extract three families of features for discriminating between the lake and nonlake radar reflections. The features model locally the basal topography, the shape of the basal reflected waveforms, and the statistical properties of the basal signal. In the second step, we provide the features as input to a support vector machine classifier to perform the automatic SL detection. The proposed technique has been applied to radargrams acquired over two large regions in East Antarctica and Siple Coast. The obtained results, which are validated both quantitatively and qualitatively, confirm the robustness of the features and their capabilities to effectively characterize SLs. Moreover, they prove the potentiality of the method to process large amounts of radargrams and update the current SL inventory.