Deep Learning Solutions for TanDEM-X-based Forest Classification
This work addresses forest classification for remote sensing applications, but it is incremental as it applies existing methods to a new dataset.
The paper tackled forest/non-forest classification using TanDEM-X data by testing two state-of-the-art deep learning models adapted to the task, confirming their great potential for remote sensing applications without providing specific numerical results.
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.