Content
Remote sensing images are a rich information source for monitoring the Earth surface, e.g., for climate change analysis, urban area studies, forestry applications, risk and damage assessment, water quality assessment, crop monitoring. Due to the recent developments in both passive multispectral and hyperspectral sensors, and synthetic aperture radar active instruments, the role of this technology is becoming more and more important for understanding the dynamics of our planet. To efficiently process/analyze the data, remote sensing has evolved into a multidisciplinary field, where machine learning algorithms play an important role nowadays. In this seminar, students will review the current state of the art in the field of machine learning applied to remote sensing image analysis in the framework of different Earth observation applications. The general topics include but are not limited to: hashing methods, data augmentation, active learning, continual learning, federated learning, multimodal learning, compressed domain analysis, explanation methods and understanding memorization with applications to remote sensing image analysis.
