# Weakly Supervised Semantic Segmentation of Satellite Images

## TL;DR

This paper explores using image-level annotations for semantic segmentation of satellite images, employing weak supervision techniques to generate pixel-level labels and adapt AffinityNet for direct segmentation, achieving competitive results.

## Contribution

It introduces an adaptation of AffinityNet for direct semantic segmentation from weak labels and compares generated labels with original data, demonstrating comparable performance to fully-supervised methods.

## Key findings

- Generated labels yield similar segmentation performance as original labels.
- AffinityNet and Random Walk achieve results close to fully-supervised approaches.
- Weak supervision reduces annotation costs while maintaining high segmentation quality.

## Abstract

When one wants to train a neural network to perform semantic segmentation, creating pixel-level annotations for each of the images in the database is a tedious task. If he works with aerial or satellite images, which are usually very large, it is even worse. With that in mind, we investigate how to use image-level annotations in order to perform semantic segmentation. Image-level annotations are much less expensive to acquire than pixel-level annotations, but we lose a lot of information for the training of the model. From the annotations of the images, the model must find by itself how to classify the different regions of the image. In this work, we use the method proposed by Anh and Kwak [1] to produce pixel-level annotation from image level annotation. We compare the overall quality of our generated dataset with the original dataset. In addition, we propose an adaptation of the AffinityNet that allows us to directly perform a semantic segmentation. Our results show that the generated labels lead to the same performances for the training of several segmentation networks. Also, the quality of semantic segmentation performed directly by the AffinityNet and the Random Walk is close to the one of the best fully-supervised approaches.