احمد کشاورز

Sea and ice discrimination and classification in the polar regions from satellite data gained importance in remote sensing and geosciences, essentially because of the undergoing climate change. Synthetic Aperture Radar (SAR) is one of the best instruments in remote sensing for sea-ice discrimination at high spatial resolution (hundreds of m), because it provides images day and night with whatever cloud coverage. Sentinel-1 is a European Space Agency (ESA) satellite mission designed for Earth observation, specifically focused on monitoring changes in the Earth’s surface. Launched in 2014, the Sentinel-1 satellite uses SAR in the C band, which is why the Sentinel-1 data are used in this thesis. The area of interest used in this thesis is a small part of Norway Island in the north of Europe. This area is cold because it is close to the polar region, and most of the time there is ice in the area. The available ice masks are generally at spatial resolution of km, thus not fully suitable to be used with SAR images. This thesis studies the sea and ice discrimination in high resolution with good accuracy. The target in this thesis is to employ unsupervised techniques due to the absence of training data and the difficulty in evaluating such samples. In this thesis two different approaches are in- troduced, the first based on texture feature extraction and the second based on deep learning methods. The discrimination of the ice from sea is with the existence of wind, which make it the challenging conditions. The texture feature extraction approach is based on three texture analysis methods, which are GLCM, MLBP and Tamara, with over-segmentation to reduce the noise effects and FCM for discrimination. The deep learning-based approach is based on the cGAN network, which is trained with sea textures and is able to generate only sea, and the post-processing of anomaly detection. At the end, the results show the proposed ap- proaches have better quality than available ice masks an