Joint Frequency-Spatial Domain Network for Remote Sensing Optical Image Change Detection

doi:10.1109/TGRS.2022.3196040

IOCAS-IR

	Joint Frequency-Spatial Domain Network for Remote Sensing Optical Image Change Detection
	Zhou, Yuan 1; Feng, Yanjie 1; Huo, Shuwei 1; Li, Xiaofeng 2
	2022
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	60 页码:14
通讯作者	Li, Xiaofeng([email protected])
摘要	Change detection for remote sensing images involves detecting regional surface changes of interest between two images taken of the same geographical area but at different times. In image processing, the spatial domain uses grayscale values to describe an image. The frequency is directly related to the spatial change rate, so the frequency domain can be intuitively associated with patterns of intensity variations in the image. These two domains provide different perspectives for image interpretation. Most existing deep-learning-based methods formulate change detection as a pixel-wise binary classification problem and utilize various strategies to extract information in the spatial domain. However, they rarely pay attention to the rich information in the frequency domain. To address this problem, we propose an end-to-end joint frequency-spatial domain network (JFSDNet) to implement remote sensing optical image change detection. Specifically, we introduce frequency information into the change detection to supplement the loss of image details caused by downsampling. In addition, we employ a frequency selection module to adaptively discriminate and choose frequency clues by reducing the complexity of the frequency features. The JFSDNet is applied to two publicly available datasets: the change-detection dataset (CDD) dataset and the LEarning VIsion and Remote sensing Change Detection (LEVIR-CD) dataset. Compared with other methods, both visual interpretation and quantitative assessment confirmed that our proposed method achieved a favorable performance.
关键词	Feature extraction Frequency-domain analysis Remote sensing Optical sensors Optical imaging Frequency domain analysis Optical fiber networks Change detection deep learning frequency domain neural network optical image
DOI	10.1109/TGRS.2022.3196040
收录类别	SCI
语种	英语
资助项目	Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao)[2022QNLM050301-2]; National Natural Science Foundation of China[U2006211]; National Natural Science Foundation of China[62171320]; National Key Research and Development Program of China[2020YFC1523200]; Major Projects of the National Natural Science Foundation of China[42090044]; Chinese Academy of Science Program[Y9KY04101L]
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:000843314100026
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/179914
专题	中国科学院海洋研究所
通讯作者	Li, Xiaofeng
作者单位	1.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Shandong, Peoples R China
通讯作者单位	中国科学院海洋研究所
推荐引用方式 GB/T 7714	Zhou, Yuan,Feng, Yanjie,Huo, Shuwei,et al. Joint Frequency-Spatial Domain Network for Remote Sensing Optical Image Change Detection[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:14.
APA	Zhou, Yuan,Feng, Yanjie,Huo, Shuwei,&Li, Xiaofeng.(2022).Joint Frequency-Spatial Domain Network for Remote Sensing Optical Image Change Detection.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,14.
MLA	Zhou, Yuan,et al."Joint Frequency-Spatial Domain Network for Remote Sensing Optical Image Change Detection".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):14.