Gated Feature Aggregation for Height Estimation From Single Aerial Images

doi:10.1109/LGRS.2021.3090470

	Gated Feature Aggregation for Height Estimation From Single Aerial Images
	Xing, Siyuan 1,2; Dong, Qiulei1,2,3 ; Hu, Zhanyi1,2,3
发表期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN	1545-598X
	2022
卷号	19 页码:5
通讯作者	Dong, Qiulei(qldong@nlpr.ia.ac.cn)
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.
关键词	Estimation Decoding Logic gates Training Feature extraction Testing Encoding Convolutional neural networks (CNNs) gate mechanism height estimation progressive refinement
DOI	10.1109/LGRS.2021.3090470
收录类别	SCI
语种	英语
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]
项目资助者	National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000733504700001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	图像视频处理与分析
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/46951
专题	多模态人工智能系统全国重点实验室_机器人视觉
通讯作者	Dong, Qiulei
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
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.