An automatic approach to map refreezing ice in radar sounder data

• Published in: Image and Signal Processing for Remote Sensing XXV
• Main Authors: Donini, Elena, Thakur, Sanchari, Bovolo, Francesca, Bruzzone, Lorenzo
• Format: Conference Object
• Language: English
• Published: 2019
• Subjects: Greenland; Center for Remote Sensing of Ice Sheets (CReSIS); North Greenland
• Online Access: http://hdl.handle.net/11582/319933; https://doi.org/10.1117/12.2533169; https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11155/2533169/An-automatic-approach-to-map-refreezing-ice-in-radar-sounder/10.1117/12.2533169.short?SSO=1

Radar sounders mounted on airborne platforms have acquired data of the subsurface of the Earth's icy areas over the last decades. These data, called radargrams, contain information on the dielectric discontinuities in the ice-sheets, and thus on the buried geological structures and the related processes. Conventionally, these structures have been characterized and mapped by visually inspecting the radargrams. However, visual inspection is subjective and time-consuming and can lead to misinterpretations. Recently, state-of-the-art automatic techniques are proposed to map the position of the bedrock, the ice layering, and the noise in the radargram. However, there are no automatic techniques for mapping the basal refreezing, which is an important ice target that controls the rate of sea-ward ow of the ice-sheets. This paper proposes an automatic method to map the refreezing ice in radargrams. We model the refreezing ice considering its geophysical and radiometric properties. Then, we design a set of features considering this model to perform a classification of the radargrams into four classes, i.e., ice layering, echo-free zone (EFZ) and thermal noise, bedrock, and the refreezing ice. We applied the proposed method to radargrams acquired in the north Greenland by Multichannel Coherent Radar Depth Sounder (MCoRDS3), a radar sounder designed by the Center for Remote Sensing of Ice Sheets (CReSIS). The results indicate a good overall accuracy. The accuracy of refreezing ice is high, while that of the other classes is comparable with the state-of-the-art techniques. The results indicate the effectiveness of the proposed features in mapping the refreezing ice.