  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.
