Semi- and weak-supervised learning for Norwegian tree species detection

Abstract

Tree species mapping of Norwegian production forests is a time-consuming process as forest associations largely rely on manual interpretation of earth observation data. Deep learning based image segmentation techniques have the potential to improve automated tree species classification, but a major challenge is the limited quality and availability of training data. Semi-supervised techniques could alleviate the need for training label and weak supervision enables handling coarse-grained and noisy labels. In this study, we evaluated the added value of semi-supervised deep learning methods in a weakly supervised setting. Specifically, consistency training and pseudo-labeling are applied for tree species classification from aerial ortho imagery in Norway. The techniques are generic and relevant for the wider earth observation domain, especially for other land cover segmentation tasks. The results show that consistency training gives a significant performance increase. Pseudo-labeling on the other hand does not, potentially this is due to varying convergence speeds for different classes causing confirmation bias or a partial violation of the cluster assumption.