| Summary: | Earth Observation is the gathering of information about planet Earth’s system via Remote Sensing technologies for monitoring land cover types and their changes. Through the years, image classification techniques have been widely studied and employed to extract useful information from Earth Observation data such as satellite imagery. One of the most attractive use cases is the monitoring of polar regions, that recently observed some dramatic changes due to global warming. Indeed drifting ice caps and icebergs represent threats to ship activities and navigation in polar areas, and the risk of collision with land-derived ice highlights the need to design a robust and automatic Sea-Ice classification for delivering up-to- date and accurate information. To achieve this goal, satellite data such as Sentinel-1 Synthetic Aperture Radar images from the European Union’s Copernicus program can be given in input to a Deep Learning classifier based on Convolutional Neural Networks capable of giving the content categorization of such images as output. For the task at hand, the availability of labeled data is generally scarce, there- fore the problem of learning with limited labeled data must be faced. There- fore, this work aims at leveraging the broader pool of unlabeled satellite data available to open up new classification solutions. This thesis proposes a Semi-Supervised Learning approach based on Generative Adversarial Networks. Such an architecture takes in input both labeled and unlabeled data and outputs the classification results exploiting the knowledge retrieved from both the data sources. Its classification performance is evaluated and it is later compared with the Supervised Learning approach and the Transfer Learning approach based on pre-trained networks. This work empirically proves that the Semi-Supervised Generative Adversarial Networks approach outperforms the Supervised Learning method, improving its Overall Accuracy by at least 5% in configurations with less than 100 training labeled samples available in ...
|
|---|