# Deep Learning Solutions for TanDEM-X-based Forest Classification

## TL;DR

This paper explores the application of deep learning models to classify forests using TanDEM-X remote sensing data, demonstrating the potential of DL in remote sensing tasks.

## Contribution

It adapts and tests two state-of-the-art deep learning models for forest classification with TanDEM-X data, highlighting their effectiveness.

## Key findings

- Deep learning models show high potential for remote sensing classification tasks.
- The adapted models perform well on TanDEM-X forest classification.
- Results confirm the suitability of DL methods for RS applications.

## Abstract

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.