Impacts of Color and Texture Distortions on Earth Observation Data in Deep Learning

arXiv:2403.04385v1 Announce Type: cross
Abstract: Land cover classification and change detection are two important applications of remote sensing and Earth observation (EO) that have benefited greatly from the advances of deep learning. Convolutional and transformer-based U-net models are the state-of-the-art architectures for these tasks, and their performances have been boosted by an increased availability of large-scale annotated EO datasets. However, the influence of different visual characteristics of the input EO data on a model's predictions is not well understood. In …

abstract advances applications architectures art arxiv change classification color cs.cv cs.lg data deep learning detection earth earth observation impacts observation performances sensing state tasks texture transformer type