Abstract
Abstract. A new Sentinel-1 image-based sea ice classification algorithm using a machine-learning-based model trained in a semi-automated manner is proposed to support daily ice charting. Previous studies mostly rely on manual work in selecting training and validation data. We show that the readily available ice charts from the operational ice services can reduce the amount of manual work in preparation of large amounts of training/testing data. Furthermore, they can feed highly reliable data to the trainer by indirectly exploiting the best ability of the sea ice experts working at the operational ice services. The proposed scheme has two phases: training and operational. Both phases start from the removal of thermal, scalloping, and textural noise from Sentinel-1 data and calculation of grey level co-occurrence matrix and Haralick texture features in a sliding window. In the training phase, the weekly ice charts are reprojected into the SAR image geometry. A random forest classifier is trained with the texture features on input and labels from the rasterized ice charts on output. Then, the trained classifier is directly applied to the texture features from Sentinel-1 images operationally. Test results from the two datasets spanning winter (January–March) and summer (June–August) seasons acquired over the Fram Strait and the Barents Sea showed that the classifier is capable of retrieving three generalized cover types (open water, mixed first-year ice, old ice) with overall accuracies of 87 % and 67 % in winter and summer seasons, respectively. For the summer season, the classifier failed in distinguishing mixed first-year ice from old ice with accuracy of only 12 %; however, it performed rather like an ice–water discriminator with high accuracy of 98 % as the misclassification between the mixed first-year ice and old ice was between them. The accuracy for five cover types (open water, new ice, young ice, first-year ice, old ice) in the winter season was 60 %. The errors are attributed both to incorrect manual classification on the ice charts and to the semi-automated algorithm. Finally, we demonstrate the potential for near-real-time service of the ice map using daily mosaicked Sentinel-1 images.
Highlights
Wide swath SAR observation from several spaceborne SAR missions (RADARSAT-1, 1995–2013; Envisat ASAR, 2002– 2012; ALOS-1 PALSAR, 2006–2011; RADARSAT-2, 2007present; Sentinel-1, 2014–present) played an important role in studying global ocean and ice-covered polar regions
The crosspolarization is known to be more sensitive to the difference in scattering from sea ice and open water than the copolarization (Scheuchl et al, 2004), and the combination of HH and HV polarizations has been widely used for ice edge detection and ice type classification
A new semi-automated SAR-based sea ice type classification scheme was proposed in this study
Summary
Wide swath SAR observation from several spaceborne SAR missions (RADARSAT-1, 1995–2013; Envisat ASAR, 2002– 2012; ALOS-1 PALSAR, 2006–2011; RADARSAT-2, 2007present; Sentinel-1, 2014–present) played an important role in studying global ocean and ice-covered polar regions. The crosspolarization is known to be more sensitive to the difference in scattering from sea ice and open water than the copolarization (Scheuchl et al, 2004), and the combination of HH and HV polarizations has been widely used for ice edge detection and ice type classification (a nice overview is given in the paper by Zakhvatkina et al, 2019). Park et al.: Classification of sea ice types in Sentinel-1 SAR images teristics from Sentinel-1 TOPSAR, and the use of Sentinel-1 for the same purpose is very limited in literature. The main drawback of applying existing algorithms to Sentinel-1 TOPSAR data is the relatively high level of thermal noise contamination and its propagation to image textures
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