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Joint Feature Network for Bridge Segmentation in Remote Sensing Images
Jian Cai; Lei Ma; Feimo Li; Yiping Yang
2018
Conference NameIGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium
Pages2515-2518
Conference Date2018-07-22
Conference PlaceValencia, Spain
Abstract

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.

KeywordConvolutional Neural Networks Pixelwise Classification Remote Sensing Images Semantic Segmentation
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23592
Collection空天信息研究中心
Corresponding AuthorLei Ma
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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