A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images

Change detection in high resolution remote sensing images is crucial to the understanding of land surface changes. As traditional change detection methods are not suitable for the task considering the challenges brought by the fine image details and complex texture features conveyed in high resoluti...

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Bibliographic Details
Published in:	ISPRS journal of photogrammetry and remote sensing Vol. 166; pp. 183 - 200
Main Authors:	Zhang, Chenxiao, Yue, Peng, Tapete, Deodato, Jiang, Liangcun, Shangguan, Boyi, Huang, Li, Liu, Guangchao
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.08.2020
Subjects:	Change detection China cities data collection Deep supervision network High resolution remote sensing image Image difference discrimination Image fusion Internet remote sensing texture China Deep supervision network Change detection High resolution remote sensing image Image difference discrimination Image fusion
ISSN:	0924-2716, 1872-8235
Online Access:	Get full text
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Abstract	Change detection in high resolution remote sensing images is crucial to the understanding of land surface changes. As traditional change detection methods are not suitable for the task considering the challenges brought by the fine image details and complex texture features conveyed in high resolution images, a number of deep learning-based change detection methods have been proposed to improve the change detection performance. Although the state-of-the-art deep feature based methods outperform all the other deep learning-based change detection methods, networks in the existing deep feature based methods are mostly modified from architectures that are originally proposed for single-image semantic segmentation. Transferring these networks for change detection task still poses some key issues. In this paper, we propose a deeply supervised image fusion network (IFN) for change detection in high resolution bi-temporal remote sensing images. Specifically, highly representative deep features of bi-temporal images are firstly extracted through a fully convolutional two-stream architecture. Then, the extracted deep features are fed into a deeply supervised difference discrimination network (DDN) for change detection. To improve boundary completeness and internal compactness of objects in the output change maps, multi-level deep features of raw images are fused with image difference features by means of attention modules for change map reconstruction. DDN is further enhanced by directly introducing change map losses to intermediate layers in the network, and the whole network is trained in an end-to-end manner. IFN is applied to a publicly available dataset, as well as a challenging dataset consisting of multi-source bi-temporal images from Google Earth covering different cities in China. Both visual interpretation and quantitative assessment confirm that IFN outperforms four benchmark methods derived from the literature, by returning changed areas with complete boundaries and high internal compactness compared to the state-of-the-art methods.
AbstractList	Change detection in high resolution remote sensing images is crucial to the understanding of land surface changes. As traditional change detection methods are not suitable for the task considering the challenges brought by the fine image details and complex texture features conveyed in high resolution images, a number of deep learning-based change detection methods have been proposed to improve the change detection performance. Although the state-of-the-art deep feature based methods outperform all the other deep learning-based change detection methods, networks in the existing deep feature based methods are mostly modified from architectures that are originally proposed for single-image semantic segmentation. Transferring these networks for change detection task still poses some key issues. In this paper, we propose a deeply supervised image fusion network (IFN) for change detection in high resolution bi-temporal remote sensing images. Specifically, highly representative deep features of bi-temporal images are firstly extracted through a fully convolutional two-stream architecture. Then, the extracted deep features are fed into a deeply supervised difference discrimination network (DDN) for change detection. To improve boundary completeness and internal compactness of objects in the output change maps, multi-level deep features of raw images are fused with image difference features by means of attention modules for change map reconstruction. DDN is further enhanced by directly introducing change map losses to intermediate layers in the network, and the whole network is trained in an end-to-end manner. IFN is applied to a publicly available dataset, as well as a challenging dataset consisting of multi-source bi-temporal images from Google Earth covering different cities in China. Both visual interpretation and quantitative assessment confirm that IFN outperforms four benchmark methods derived from the literature, by returning changed areas with complete boundaries and high internal compactness compared to the state-of-the-art methods.
Author	Liu, Guangchao Zhang, Chenxiao Jiang, Liangcun Huang, Li Shangguan, Boyi Yue, Peng Tapete, Deodato
Author_xml	– sequence: 1 givenname: Chenxiao surname: Zhang fullname: Zhang, Chenxiao organization: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430079, China – sequence: 2 givenname: Peng orcidid: 0000-0003-3006-4542 surname: Yue fullname: Yue, Peng email: pyue@whu.edu.cn organization: School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430079, China – sequence: 3 givenname: Deodato orcidid: 0000-0002-7242-4473 surname: Tapete fullname: Tapete, Deodato organization: Italian Space Agency (ASI), Via del Politecnico snc, 00133, Rome, Italy – sequence: 4 givenname: Liangcun surname: Jiang fullname: Jiang, Liangcun organization: School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430079, China – sequence: 5 givenname: Boyi surname: Shangguan fullname: Shangguan, Boyi organization: School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430079, China – sequence: 6 givenname: Li surname: Huang fullname: Huang, Li organization: School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430079, China – sequence: 7 givenname: Guangchao surname: Liu fullname: Liu, Guangchao organization: School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430079, China
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Snippet	Change detection in high resolution remote sensing images is crucial to the understanding of land surface changes. As traditional change detection methods are...
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SubjectTerms	Change detection China cities data collection Deep supervision network High resolution remote sensing image Image difference discrimination Image fusion Internet remote sensing texture
Title	A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images
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