Deep Learning-based Approaches for Forest Mapping with TanDEM-X Interferometric Data

Abstract

Deep learning models trained in a fully supervised way have shown encouraging capabilities for mapping forests with TanDEM-X interferometric data, being able to generate time-tagged forest maps at large-scale over tropical forests. These maps have been generated at 50 m resolution to reduce the computation burden. In this work, we now aim to exploit the high-resolution capabilities of the TanDEM-X interferometric dataset, processed at only 6 m resolution, for forest mapping purposes. In order to cope with the lack of reliable reference data at such a high resolution, we focus on the investigation of self-supervised learning approaches. The availability of a reference map over Pennsylvania, USA, based on Lidar acquisitions at 1 m resolution, allows us to compare different deep learning approaches. The obtained results show the possibility to extend the proposed self-supervised learning approach over areas where the lack of reference data prevent us from using fully supervised deep learning methods.