| Summary: | Abstract — Polar regions, especially sensitive to small changes in temperature, play a key role in global climate change. Scientists are interested in evaluating the decline in local ice production. We will attempt to classify automatically First Year (FY) ice, Multi Year (MY) ice and Deformed ice using Sidescan sonar images of the Arctic ice-shelf. We use 4-bit data (16 grey levels) ground truth images provided by an expert as a starting point of the study. These images have been obtained using a Sidescan sonar pointing upward. Our methods use first order statistics extracted from local areas of the image. The local histogram is fitted to three pdfs (Rayleigh, Log-normal and Gaussian distributions) whose parameters are extracted. A Chi-square test is used to evaluate the quality of the fit. The parameters are then used to classify the regions. The results obtained show that FY and MY ice follow Rayleigh or Log-normal distributions whereas Deformed ice is more like a Gaussian distribution. We have created a new method to classify ice types selecting the best fitting for each region. A classification of three classes (FY, MY and Deformed ice) is achieved with first order statistics. In future work we will investigate the potential of second order statistics to improve the classification. I.
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