Method for detection of leads from Sentinel-1 SAR images

• Published in: Annals of Glaciology
• Main Authors: Murashkin, Dmitrii, Spreen, Gunnar, Huntemann, Marcus, Dierking, Wolfgang Fritz Otto
• Format: Article in Journal/Newspaper
• Language: English
• Published: Cambridge University Press (CUP) 2018
• Subjects: VDP::Mathematics and natural science: 400::Geosciences: 450::Quaternary geology; glaciology: 465; VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Kvartærgeologi; glasiologi: 465; ice/atmosphere interactions; ice/ocean interactions; remote sensing; sea ice; sea-ice dynamics; Arctic; Annals of Glaciology; Sea ice
• Online Access: https://hdl.handle.net/10037/15023; https://doi.org/10.1017/aog.2018.6

Source at https://doi.org/10.1017/aog.2018.6 . The presence of leads with open water or thin ice is an important feature of the Arctic sea ice cover. Leads regulate the heat, gas and moisture fluxes between the ocean and atmosphere and are areas of high ice growth rates during periods of freezing conditions. Here, an algorithm providing an automatic lead detection based on synthetic aperture radar images is described that can be applied to a wide range of Sentinel-1 scenes. By using both the HH and the HV channels instead of single co-polarised observations the algorithm is able to classify more leads correctly. The lead classification algorithm is based on polarimetric features and textural features derived from the grey-level co-occurrence matrix. The Random Forest classifier is used to investigate the importance of the individual features for lead detection. The precision–recall curve representing the quality of the classification is used to define threshold for a binary lead/sea ice classification. The algorithm is able to produce a lead classification with more that 90% precision with 60% of all leads classified. The precision can be increased by the cost of the amount of leads detected. Results are evaluated based on comparisons with Sentinel-2 optical satellite data.