Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods
This work addresses the high cost of labeling in forestry applications, but it is incremental as it compares existing explanation methods without introducing a new paradigm.
The paper tackled the problem of costly pixel-level labeling for tree species classification in remote sensing by investigating explanation methods for weakly supervised semantic segmentation using only image-level labels, finding that self-enhancing maps outperformed other methods on an aerial image dataset.
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.