  The collection of a high number of pixel-based labeled training samples for
tree species identification is time consuming and costly in operational
forestry applications. To address this problem, in this paper we investigate
the effectiveness of explanation methods for deep neural networks in performing
weakly supervised semantic segmentation using only image-level labels.
Specifically, we consider four methods:i) class activation maps (CAM); ii)
gradient-based CAM; iii) pixel correlation module; and iv) self-enhancing maps
(SEM). We compare these methods with each other using both quantitative and
qualitative measures of their segmentation accuracy, as well as their
computational requirements. Experimental results obtained on an aerial image
archive show that:i) considered explanation techniques are highly relevant for
the identification of tree species with weak supervision; and ii) the SEM
outperforms the other considered methods. The code for this paper is publicly
available at https://git.tu-berlin.de/rsim/rs_wsss.
