Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods. (arXiv:2201.07495v1 [cs.CV])

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 …

arxiv classification cv images remote segmentation semantic sensing tree