期刊
REMOTE SENSING
卷 12, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/rs12061049
关键词
building detection; weakly supervised learning; superpixel; semantic segmentation; deep learning
类别
资金
- National Natural Science Foundation of China [41671357]
- Scientific Research Fund of Hunan Provincial Education Department [16K093]
- Fundamental Research Funds for the Central Universities of Central South University [1053320183827]
The lack of pixel-level labeling limits the practicality of deep learning-based building semantic segmentation. Weakly supervised semantic segmentation based on image-level labeling results in incomplete object regions and missing boundary information. This paper proposes a weakly supervised semantic segmentation method for building detection. The proposed method takes the image-level label as supervision information in a classification network that combines superpixel pooling and multi-scale feature fusion structures. The main advantage of the proposed strategy is its ability to improve the intactness and boundary accuracy of a detected building. Our method achieves impressive results on two 2D semantic labeling datasets, which outperform some competing weakly supervised methods and are close to the result of the fully supervised method.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据