Joint Feature Network for Bridge Segmentation in Remote Sensing Images | |
Jian Cai; Lei Ma; Feimo Li; Yiping Yang | |
2018 | |
会议名称 | IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium |
页码 | 2515-2518 |
会议日期 | 2018-07-22 |
会议地点 | Valencia, Spain |
摘要 | This paper proposes a novel convolutional neural network architecture for semantic segmentation of bridges with various scales in optical remote sensing images. In the context of R-SI analysis on objects with irregular shapes, it is necessary to get dense, pixelwise classification maps. To address the issue, a new network architecture for producing refined shapes is required instead of image categorization labels. In our end-to-end framework, a ResNet is used as a backbone model to extract semantic features, then a cascaded top-down path is added to fuse these features as different scales. Joint features are obtained by stacking different layers of feature maps. Experiments show our proposed architecture has the ability to combine rich multi-scale contextual information to produce semantic segmentation maps with high accuracy. |
关键词 | Convolutional Neural Networks Pixelwise Classification Remote Sensing Images Semantic Segmentation |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23592 |
专题 | 空天信息研究中心 |
通讯作者 | Lei Ma |
作者单位 | 中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Jian Cai,Lei Ma,Feimo Li,et al. Joint Feature Network for Bridge Segmentation in Remote Sensing Images[C],2018:2515-2518. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
bridge_detection.pdf(1312KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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