Forest Mapping with TanDEM-X InSAR Data and Self-Supervised Learning

Abstract

Deep learning methods, used in a fully-supervised learning way, have shown good capabilities for mapping forests with TanDEM-X interferometric data, being able to generate timetagged forest maps at large-scale over tropical forests. All these maps have been generated at 50 m resolution to reduce the computation burden. In this work, we now aim to exploit the full-resolution capabilities of the TanDEM-X interferometric dataset, processed at 6 m resolution. In order to cope with the lack of reliable reference data at such 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, allowed us to compare different deep learning approaches. First promising 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.