  In the last few years, deep learning (DL) has been successfully and massively
employed in computer vision for discriminative tasks, such as image
classification or object detection. This kind of problems are core to many
remote sensing (RS) applications as well, though with domain-specific
peculiarities. Therefore, there is a growing interest on the use of DL methods
for RS tasks. Here, we consider the forest/non-forest classification problem
with TanDEM-X data, and test two state-of-the-art DL models, suitably adapting
them to the specific task. Our experiments confirm the great potential of DL
methods for RS applications.
