SCE-Net: Self- and Cross-Enhancement Network for Single-View Height Estimation and Semantic Segmentation

doi:10.3390/rs14092252

	SCE-Net: Self- and Cross-Enhancement Network for Single-View Height Estimation and Semantic Segmentation
	Xing, Siyuan1,2 ; Dong, Qiulei1,2,3 ; Hu, Zhanyi1,2
发表期刊	REMOTE SENSING
	2022-05-01
卷号	14 期号:9 页码:22
通讯作者	Dong, Qiulei(qldong@nlpr.ia.ac.cn)
摘要	Single-view height estimation and semantic segmentation have received increasing attention in recent years and play an important role in the photogrammetry and remote sensing communities. The height information and semantic information of images are correlated, and some recent works have shown that multi-task learning methods can achieve complementation of task-related features and improve the prediction results of the multiple tasks. Although much progress has been made in recent works, how to effectively extract and fuse height features and semantic features is still an open issue. In this paper, a self- and cross-enhancement network (SCE-Net) is proposed to jointly perform height estimation and semantic segmentation on single aerial images. A feature separation-fusion module is constructed to effectively separate and fuse height features and semantic features based on an attention mechanism for feature representation enhancement across tasks. In addition, a height-guided feature distance loss and a semantic-guided feature distance loss are designed based on deep metric learning to achieve task-aware feature representation enhancement. Extensive experiments are conducted on the Vaihingen dataset and the Potsdam dataset to verify the effectiveness of the proposed method. The experimental results demonstrate that the proposed SCE-Net could outperform the state-of-the-art methods and achieve better performance in both height estimation and semantic segmentation.
关键词	height estimation semantic segmentation single aerial image convolutional neural networks multi-task learning deep metric learning
DOI	10.3390/rs14092252
关键词[WOS]	REMOTE-SENSING IMAGES ; OBJECT DETECTION ; AERIAL IMAGES ; DEEP ; RGB ; CLASSIFICATION ; RECONSTRUCTION ; SURFACE
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] ; 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	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000794398800001
出版者	MDPI
引用统计	被引频次：9[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/173211/49390
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 100190, 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. SCE-Net: Self- and Cross-Enhancement Network for Single-View Height Estimation and Semantic Segmentation[J]. REMOTE SENSING,2022,14(9):22.
APA	Xing, Siyuan,Dong, Qiulei,&Hu, Zhanyi.(2022).SCE-Net: Self- and Cross-Enhancement Network for Single-View Height Estimation and Semantic Segmentation.REMOTE SENSING,14(9),22.
MLA	Xing, Siyuan,et al."SCE-Net: Self- and Cross-Enhancement Network for Single-View Height Estimation and Semantic Segmentation".REMOTE SENSING 14.9(2022):22.