Difference Enhancement and Spatial-Spectral Nonlocal Network for Change Detection in VHR Remote Sensing Images
The popular Siamese convolutional neural networks (CNNs) for remote sensing (RS) image change detection (CD) often suffer from two problems. First, they either ignore the original information of bitemporal images or insufficiently utilize the difference information between bitemporal images, which l...
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| Published in: | IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 13 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0196-2892, 1558-0644 |
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| Abstract | The popular Siamese convolutional neural networks (CNNs) for remote sensing (RS) image change detection (CD) often suffer from two problems. First, they either ignore the original information of bitemporal images or insufficiently utilize the difference information between bitemporal images, which leads to the low tightness of the changed objects. Second, Siamese CNNs always employ dual-branch encoders for CD, which increases computational cost. To address the above issues, this article proposes a network based on difference enhancement and spatial-spectral nonlocal (DESSN) for CD in very-high-resolution (VHR) images. This article makes threefold contributions. First, we design a difference enhancement (DE) module that can effectively learn the difference representation between foreground and background to reduce the impact of irrelevant changes on the detection results. Second, we present a spatial-spectral nonlocal (SSN) module that is different from vanilla nonlocal because multiscale spatial global features are incorporated to model the large-scale variation of objects during CD. The module can be used to strengthen the edge integrity and internal tightness of changed objects. Third, the asymmetric double convolution with Ghost (ADCG) module is exploited instead of standard convolution. The ADCG can not only refine the edge information of the changed objects, since horizontal and vertical convolutional kernels have good contour preservation advantages, but also greatly reduce the computational complexity of the proposed model. The experiments on two public VHR CD datasets demonstrate that the proposed network can provide higher detection accuracy and requires smaller memory usage than state-of-the-art networks. |
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| AbstractList | The popular Siamese convolutional neural networks (CNNs) for remote sensing (RS) image change detection (CD) often suffer from two problems. First, they either ignore the original information of bitemporal images or insufficiently utilize the difference information between bitemporal images, which leads to the low tightness of the changed objects. Second, Siamese CNNs always employ dual-branch encoders for CD, which increases computational cost. To address the above issues, this article proposes a network based on difference enhancement and spatial–spectral nonlocal (DESSN) for CD in very-high-resolution (VHR) images. This article makes threefold contributions. First, we design a difference enhancement (DE) module that can effectively learn the difference representation between foreground and background to reduce the impact of irrelevant changes on the detection results. Second, we present a spatial–spectral nonlocal (SSN) module that is different from vanilla nonlocal because multiscale spatial global features are incorporated to model the large-scale variation of objects during CD. The module can be used to strengthen the edge integrity and internal tightness of changed objects. Third, the asymmetric double convolution with Ghost (ADCG) module is exploited instead of standard convolution. The ADCG can not only refine the edge information of the changed objects, since horizontal and vertical convolutional kernels have good contour preservation advantages, but also greatly reduce the computational complexity of the proposed model. The experiments on two public VHR CD datasets demonstrate that the proposed network can provide higher detection accuracy and requires smaller memory usage than state-of-the-art networks. |
| Author | Wang, Xingwu Xue, Dinghua Nandi, Asoke K. Ning, Hailong Wang, Qi Lei, Tao Wang, Jie |
| Author_xml | – sequence: 1 givenname: Tao orcidid: 0000-0002-2104-9298 surname: Lei fullname: Lei, Tao email: leitao@sust.edu.cn organization: Shaanxi Joint Laboratory of Artificial Intelligence and the School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an, China – sequence: 2 givenname: Jie surname: Wang fullname: Wang, Jie email: wcjsust@163.com organization: School of Electronical and Control Engineering, Shaanxi University of Science and Technology, Xi'an, China – sequence: 3 givenname: Hailong orcidid: 0000-0001-8375-1181 surname: Ning fullname: Ning, Hailong email: ninghailong93@gmail.com organization: School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, China – sequence: 4 givenname: Xingwu surname: Wang fullname: Wang, Xingwu email: wangxwu1949@163.com organization: Shaanxi Joint Laboratory of Artificial Intelligence and the School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an, China – sequence: 5 givenname: Dinghua surname: Xue fullname: Xue, Dinghua email: 903438920@qq.com organization: School of Electronical and Control Engineering, Shaanxi University of Science and Technology, Xi'an, China – sequence: 6 givenname: Qi orcidid: 0000-0002-7028-4956 surname: Wang fullname: Wang, Qi email: crabwq@gmail.com organization: School of Computer Science and the School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an, China – sequence: 7 givenname: Asoke K. orcidid: 0000-0001-6248-2875 surname: Nandi fullname: Nandi, Asoke K. email: asoke.nandi@brunel.ac.uk organization: Department of Electronic and Electrical Engineering, Brunel University London, Uxbridge, U.K |
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| Snippet | The popular Siamese convolutional neural networks (CNNs) for remote sensing (RS) image change detection (CD) often suffer from two problems. First, they either... |
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| SubjectTerms | Artificial neural networks Change detection Change detection (CD) Clustering algorithms Coders Computer applications Computing costs Convolution Detection difference enhancement (DE) module Feature extraction Image edge detection Image enhancement Image resolution Information processing Modules Neural networks Remote sensing Robustness Siamese convolutional neural networks (CNNs) spatial-spectral nonlocal (SSN) module Spectra Task analysis Tightness |
| Title | Difference Enhancement and Spatial-Spectral Nonlocal Network for Change Detection in VHR Remote Sensing Images |
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