1 research outputs found
Classification of sea ice types for the East part of Greenland waters using SENTINEL 1 data
Ships navigate in Greenland waters all year round. Cruises to Greenland due to tourism
and educational purposes have increased the last decade. Hence, it is essential for ships
that navigate through Sea Ice in winter to use reliable and accurate information on sea ice
conditions. An accurate classification of Sea Ice types is an ongoing problem. Many
classification algorithms depend only on the SAR image intensity for discriminating the
sea ice types. Different Sea Ice types exhibit similar backscatter signature which makes
the algorithm unable to correctly classify them.
In this study, two dual-polarization SENTINEL-1 images with a spatial resolution of 40 x
40m acquired over the East part of Greenland in February and May of 2016. Support
Vector Machine (SVM) classifier was used to perform the classification. In order to
improve the discrimination of ice types, texture analysis was performed using Grey Level
Co-occurrence Matrix (GLCM) algorithm. Ten GLCM texture features were calculated.
The analysis revealed that the most informative texture features for the sea ice
classification are Energy, mean, dissimilarity for HV polarization and Angular Second
Moment, variance and energy for HH polarization.
The classification results for the SAR images acquired during winter and spring period
were compared against the sea ice charts produced by DMI. A good agreement between
the classification results and validation data is shown. The results show that the overall
classification accuracy for both SAR images amount to 91%. The most hazardous for ships
navigation sea ice types (old ice and deformed first year ice) have been successfully
discriminated