论文标题
基于解释方法的树种分类的遥感图像的弱监督语义分割
Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods
论文作者
论文摘要
在运营林业应用中，收集了大量基于像素的标签培训样品，以耗时且昂贵。为了解决这个问题，在本文中，我们研究了仅使用图像级标签进行弱监督语义分割的深神经网络解释方法的有效性。具体来说，我们考虑了四种方法：i）类激活图（CAM）； ii）基于梯度的凸轮； iii）像素相关模块；和iv）自我增强地图（SEM）。我们使用其分割精度及其计算要求的定量和定性度量彼此比较这些方法。在空中图像档案中获得的实验结果表明：i）考虑的解释技术与识别有弱监督的树种高度相关； ii）SEM优于其他考虑的方法。本文的代码可在https://git.tu-berlin.de/rsim/rs_wsss上公开获取。
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.
