@conference {cMoliner,
	title = {Weakly Supervised Semantic Segmentation for Remote Sensing Hyperspectral Imaging},
	booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020)},
	year = {2020},
	month = {05/2020},
	abstract = {<p>This paper studies the problem of training a semantic segmentation neural network with weak annotations, in order to be applied in aerial vegetation images from Teide National Park. It proposes a Deep Seeded Region Growing system which consists on training a semantic segmentation network from a set of seeds generated by a Support Vector Machine. A region growing algorithm module is applied to the seeds to progressively increase the pixel-level supervision. The proposed method performs better than an SVM, which is one of the most popular segmentation tools in remote sensing image applications.</p>},
	author = {Eloi Moliner and Luis Salgueiro and Ver{\'o}nica Vilaplana}
}
