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Gated Feature Aggregation for Height Estimation From Single Aerial Images

文献类型：期刊论文

作者Xing, Siyuan2,3; Dong, Qiulei1,2,3; Hu, Zhanyi1,2,3
刊名IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
出版日期2022
卷号19页码:5
ISSN号1545-598X
关键词Estimation Decoding Logic gates Training Feature extraction Testing Encoding Convolutional neural networks (CNNs) gate mechanism height estimation progressive refinement
DOI10.1109/LGRS.2021.3090470
通讯作者Dong, Qiulei(qldong@nlpr.ia.ac.cn)
英文摘要Height estimation from single images, strictly speaking, is an ill-posed problem. However, recently, it is shown that it is both possible and feasible to learn a mapping from image statistics to height information. In spite of recent efforts in this field, how to learn fine-shape preserving features, such as object boundaries and contours, is still an open issue. In this work, we propose a progressive learning network to estimate height information from single aerial images in a coarse-to-fine manner. In particular, a gated feature aggregation module is introduced to effectively combine low-level and high-level features. The proposed method is validated on three public datasets, including the Vaihingen dataset, the Potsdam dataset, and the DFC2019 dataset. Both quantitative and qualitative experimental results demonstrate that the proposed method can achieve more accurate height estimation from single aerial images, especially with better object boundary and contour preserving capability, than four related height estimation methods.
资助项目National Natural Science Foundation of China[61991423] ; National Natural Science Foundation of China[U1805264] ; National Natural Science Foundation of China[61573359] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32050100]
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000733504700001
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/46950]  
专题自动化研究所_模式识别国家重点实验室_机器人视觉团队
通讯作者Dong, Qiulei
作者单位1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Xing, Siyuan,Dong, Qiulei,Hu, Zhanyi. Gated Feature Aggregation for Height Estimation From Single Aerial Images[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2022,19:5.
APA Xing, Siyuan,Dong, Qiulei,&Hu, Zhanyi.(2022).Gated Feature Aggregation for Height Estimation From Single Aerial Images.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,19,5.
MLA Xing, Siyuan,et al."Gated Feature Aggregation for Height Estimation From Single Aerial Images".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19(2022):5.

入库方式： OAI收割

来源：自动化研究所

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