Segmentation of Clouds in Satellite Images
Abstract: The usage of 3D modelling is increasing fast, both for civilian and military areas, such as navigation, targeting and urban planning. When creating a 3D model from satellite images, clouds canbe problematic. Thus, automatic detection ofclouds inthe imagesis ofgreat use. This master thesis was carried out at Vricon, who produces 3D models of the earth from satellite images.This thesis aimed to investigate if Support Vector Machines could classify pixels into cloud or non-cloud, with a combination of texture and color as features. To solve the stated goal, the task was divided into several subproblems, where the ﬁrst part was to extract features from the images. Then the images were preprocessed before fed to the classiﬁer. After that, the classiﬁer was trained, and ﬁnally evaluated.The two methods that gave the best results in this thesis had approximately 95 % correctly classiﬁed pixels. This result is better than the existing cloud segmentation method at Vricon, for the tested terrain and cloud types.
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