Multi-Head Graph Attention Adversarial Autoencoder Network for Unsupervised Change Detection Using Heterogeneous Remote Sensing Images
Heterogeneous remote sensing images, acquired from different sensors, exhibit significant variations in data structure, resolution, and radiometric characteristics. These inherent heterogeneities present substantial challenges for change detection, a task that involves identifying changes in a targe...
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| Published in: | Remote sensing (Basel, Switzerland) Vol. 17; no. 15; p. 2581 |
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| Main Authors: | , , , , |
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| Language: | English |
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| ISSN: | 2072-4292, 2072-4292 |
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| Abstract | Heterogeneous remote sensing images, acquired from different sensors, exhibit significant variations in data structure, resolution, and radiometric characteristics. These inherent heterogeneities present substantial challenges for change detection, a task that involves identifying changes in a target area by analyzing multi-temporal images. To address this issue, we propose the Multi-Head Graph Attention Mechanism (MHGAN), designed to achieve accurate detection of surface changes in heterogeneous remote sensing images. The MHGAN employs a bidirectional adversarial convolutional autoencoder network to reconstruct and perform style transformation of heterogeneous images. Unlike existing unidirectional translation frameworks (e.g., CycleGAN), our approach simultaneously aligns features in both domains through multi-head graph attention and dynamic kernel width estimation, effectively reducing false changes caused by sensor heterogeneity. The network training is constrained by four loss functions: reconstruction loss, code correlation loss, graph attention loss, and adversarial loss, which together guide the alignment of heterogeneous images into a unified data domain. The code correlation loss enforces consistency in feature representations at the encoding layer, while a density-based kernel width estimation method enhances the capture of both local and global changes. The graph attention loss models the relationships between features and images, improving the representation of consistent regions across bitemporal images. Additionally, adversarial loss promotes style consistency within the shared domain. Our bidirectional adversarial convolutional autoencoder simultaneously aligns features across both domains. This bilateral structure mitigates the information loss associated with one-way mappings, enabling more accurate style transformation and reducing false change detections caused by sensor heterogeneity, which represents a key advantage over existing unidirectional methods. Compared with state-of-the-art methods for heterogeneous change detection, the MHGAN demonstrates superior performance in both qualitative and quantitative evaluations across four benchmark heterogeneous remote sensing datasets. |
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| AbstractList | Heterogeneous remote sensing images, acquired from different sensors, exhibit significant variations in data structure, resolution, and radiometric characteristics. These inherent heterogeneities present substantial challenges for change detection, a task that involves identifying changes in a target area by analyzing multi-temporal images. To address this issue, we propose the Multi-Head Graph Attention Mechanism (MHGAN), designed to achieve accurate detection of surface changes in heterogeneous remote sensing images. The MHGAN employs a bidirectional adversarial convolutional autoencoder network to reconstruct and perform style transformation of heterogeneous images. Unlike existing unidirectional translation frameworks (e.g., CycleGAN), our approach simultaneously aligns features in both domains through multi-head graph attention and dynamic kernel width estimation, effectively reducing false changes caused by sensor heterogeneity. The network training is constrained by four loss functions: reconstruction loss, code correlation loss, graph attention loss, and adversarial loss, which together guide the alignment of heterogeneous images into a unified data domain. The code correlation loss enforces consistency in feature representations at the encoding layer, while a density-based kernel width estimation method enhances the capture of both local and global changes. The graph attention loss models the relationships between features and images, improving the representation of consistent regions across bitemporal images. Additionally, adversarial loss promotes style consistency within the shared domain. Our bidirectional adversarial convolutional autoencoder simultaneously aligns features across both domains. This bilateral structure mitigates the information loss associated with one-way mappings, enabling more accurate style transformation and reducing false change detections caused by sensor heterogeneity, which represents a key advantage over existing unidirectional methods. Compared with state-of-the-art methods for heterogeneous change detection, the MHGAN demonstrates superior performance in both qualitative and quantitative evaluations across four benchmark heterogeneous remote sensing datasets. |
| Author | Jia, Meng Lou, Xiangyu Shi, Zhenghao Lu, Xiaofeng Zhao, Zhiqiang |
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| Cites_doi | 10.1109/TGRS.2017.2739800 10.1080/01431161.2018.1448481 10.1109/TIP.2021.3093766 10.1109/IGARSS.2010.5650677 10.1007/978-3-030-30671-7 10.1109/TIP.2017.2784560 10.3390/s8031613 10.1109/JPROC.2015.2462751 10.3390/rs11202377 10.1109/TGRS.2009.2038274 10.1109/TNNLS.2016.2636227 10.1109/LGRS.2018.2868704 10.1016/j.patcog.2020.107598 10.1177/001316448104100307 10.1109/LGRS.2022.3201925 10.3390/rs11091091 10.1109/TNNLS.2022.3172183 10.1016/j.rse.2009.09.012 10.1109/TGRS.2005.857987 10.1109/TGRS.2011.2171493 10.1080/01431168908903939 10.1109/MGRS.2018.2890023 10.1109/LGRS.2019.2892432 10.1109/JSTARS.2019.2916560 10.1109/IGARSS.2015.7326153 10.1109/CVPR.2017.632 10.3390/rs15030621 10.1007/978-3-642-35289-8_3 10.1109/TGRS.2007.893568 10.1109/TIP.2004.838698 |
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| References | Chen (ref_22) 2011; 11 Jia (ref_42) 2022; 19 Nascimento (ref_25) 2005; 38 Luppino (ref_34) 2022; 35 Ghamisi (ref_10) 2019; 7 Luppino (ref_41) 2021; 60 Griffiths (ref_7) 2010; 114 Inglada (ref_1) 2007; 45 ref_14 Liu (ref_15) 2018; 29 ref_35 Singh (ref_21) 1989; 10 Wan (ref_12) 2019; 16 Li (ref_23) 2018; 10 ref_32 ref_39 ref_38 Sun (ref_28) 2021; 109 ref_37 Liu (ref_27) 2021; 60 Liu (ref_36) 2022; 60 Sun (ref_43) 2021; 30 Li (ref_20) 2020; 162 Niu (ref_17) 2019; 16 Wan (ref_13) 2018; 39 Liu (ref_2) 2018; 27 Prasad (ref_9) 2015; 103 Zhao (ref_16) 2017; 55 Radke (ref_4) 2005; 14 Gong (ref_3) 2016; 53 Brunner (ref_6) 2010; 48 Zhou (ref_11) 2008; 8 Zhang (ref_24) 2019; 57 Bovolo (ref_33) 2005; 43 He (ref_30) 2020; 164 Brennan (ref_40) 1981; 41 ref_8 Chen (ref_19) 2018; 9 Bovolo (ref_31) 2012; 50 ref_5 Wang (ref_26) 2017; 9 Sun (ref_29) 2021; 60 Zhang (ref_18) 2019; 12 |
| References_xml | – volume: 55 start-page: 7066 year: 2017 ident: ref_16 article-title: Discriminative feature learning for unsupervised change detection in heterogeneous images based on a coupled neural network publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2017.2739800 – volume: 39 start-page: 3753 year: 2018 ident: ref_13 article-title: Multi-sensor remote sensing image change detection based on sorted histograms publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2018.1448481 – volume: 60 start-page: 1 year: 2021 ident: ref_29 article-title: Structure consistency-based graph for unsupervised change detection with homogeneous and heterogeneous remote sensing images publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 30 start-page: 6277 year: 2021 ident: ref_43 article-title: Iterative robust graph for unsupervised change detection of heterogeneous remote sensing images publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2021.3093766 – volume: 11 start-page: 179 year: 2011 ident: ref_22 article-title: Change detection for remote sensing images: A survey publication-title: Int. J. Image Graph. – ident: ref_8 doi: 10.1109/IGARSS.2010.5650677 – volume: 9 start-page: 997 year: 2018 ident: ref_19 article-title: Robust feature alignment for heterogeneous remote sensing image change detection via cross-domain adversarial learning publication-title: Remote Sens. Lett. – ident: ref_38 doi: 10.1007/978-3-030-30671-7 – volume: 60 start-page: 4700422 year: 2021 ident: ref_41 article-title: Deep image translation with an affinity-based change prior for unsupervised multimodal change detection publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 27 start-page: 1822 year: 2018 ident: ref_2 article-title: Change detection in heterogenous remote sensing images via homogeneous pixel transformation publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2784560 – volume: 8 start-page: 1613 year: 2008 ident: ref_11 article-title: Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data publication-title: Sensors doi: 10.3390/s8031613 – volume: 103 start-page: 1585 year: 2015 ident: ref_9 article-title: Challenges and opportunities of multimodality and data fusion in remote sensing publication-title: Proc. IEEE doi: 10.1109/JPROC.2015.2462751 – ident: ref_14 doi: 10.3390/rs11202377 – volume: 48 start-page: 2403 year: 2010 ident: ref_6 article-title: Earthquake damage assessment of buildings using VHR optical and SAR imagery publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2009.2038274 – volume: 29 start-page: 545 year: 2018 ident: ref_15 article-title: A deep convolutional coupling network for change detection based on heterogeneous optical and radar images publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2016.2636227 – volume: 162 start-page: 55 year: 2020 ident: ref_20 article-title: Context-aware deep learning for complex scene change detection in heterogeneous remote sensing images publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 16 start-page: 45 year: 2019 ident: ref_17 article-title: A conditional adversarial network for change detection in heterogeneous images publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2018.2868704 – volume: 109 start-page: 107598 year: 2021 ident: ref_28 article-title: Nonlocal patch similarity based heterogeneous remote sensing change detection publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2020.107598 – volume: 9 start-page: 1254 year: 2017 ident: ref_26 article-title: Heterogeneous remote sensing image change detection based on transfer learning publication-title: Remote Sens. – volume: 53 start-page: 123 year: 2016 ident: ref_3 article-title: A survey on change detection in synthetic aperture radar imagery publication-title: J. Comput. Res. Dev. – volume: 41 start-page: 687 year: 1981 ident: ref_40 article-title: Coefficient Kappa: Some uses, misuses, and alternatives publication-title: Educ. Psychol. Meas. doi: 10.1177/001316448104100307 – volume: 10 start-page: 831 year: 2018 ident: ref_23 article-title: A review of change detection techniques for remotely sensed imagery publication-title: Remote Sens. – volume: 164 start-page: 43 year: 2020 ident: ref_30 article-title: Change detection in remote sensing imagery using deep learning techniques: A survey publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 19 start-page: 6515705 year: 2022 ident: ref_42 article-title: Bipartite adversarial autoencoders with structural self-similarity for unsupervised heterogeneous remote sensing image change detection publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2022.3201925 – ident: ref_5 doi: 10.3390/rs11091091 – volume: 60 start-page: 4701514 year: 2021 ident: ref_27 article-title: A probabilistic model based on bipartite convolutional neural network for unsupervised change detection publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 60 start-page: 4403413 year: 2022 ident: ref_36 article-title: Unsupervised change detection from heterogeneous data based on image translation publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 35 start-page: 60 year: 2022 ident: ref_34 article-title: Code-aligned autoencoders for unsupervised change detection in multimodal remote sensing images publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2022.3172183 – volume: 114 start-page: 426 year: 2010 ident: ref_7 article-title: Mapping megacity growth with multi-sensor data publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2009.09.012 – volume: 38 start-page: 1709 year: 2005 ident: ref_25 article-title: Change detection in remote sensing images using multiscale morphological processing publication-title: Pattern Recognit. – volume: 43 start-page: 2963 year: 2005 ident: ref_33 article-title: A detail-preserving scale-driven approach to change detection in multitemporal SAR images publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2005.857987 – volume: 57 start-page: 9736 year: 2019 ident: ref_24 article-title: Unsupervised change detection of remote sensing images based on generative adversarial networks publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 50 start-page: 2196 year: 2012 ident: ref_31 article-title: A framework for automatic and unsupervised detection of multiple changes in multitemporal images publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2011.2171493 – volume: 10 start-page: 989 year: 1989 ident: ref_21 article-title: Digital change detection techniques using remotely-sensed data publication-title: Int. J. Remote Sens. doi: 10.1080/01431168908903939 – volume: 7 start-page: 6 year: 2019 ident: ref_10 article-title: Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art publication-title: IEEE Geosci. Remote Sens. Mag. doi: 10.1109/MGRS.2018.2890023 – volume: 16 start-page: 1026 year: 2019 ident: ref_12 article-title: A post-classification comparison method for SAR and optical images change detection publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2019.2892432 – volume: 12 start-page: 1543 year: 2019 ident: ref_18 article-title: Multi-scale convolutional neural network for remote sensing image change detection publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2019.2916560 – ident: ref_32 doi: 10.1109/IGARSS.2015.7326153 – ident: ref_37 doi: 10.1109/CVPR.2017.632 – ident: ref_35 doi: 10.3390/rs15030621 – ident: ref_39 doi: 10.1007/978-3-642-35289-8_3 – volume: 45 start-page: 1432 year: 2007 ident: ref_1 article-title: A new statistical similarity measure for change detection in multitemporal SAR images and its extension to multiscale change analysis publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2007.893568 – volume: 14 start-page: 294 year: 2005 ident: ref_4 article-title: Image change detection algorithms: A systematic survey publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2004.838698 |
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| SubjectTerms | Change detection Classification Data structures Datasets Deep learning domain transformation graph attention Heterogeneity heterogeneous remote sensing images Image acquisition Methods Neural networks Performance evaluation Remote sensing Representations Sensors Similarity measures Transformations (mathematics) unsupervised change detection |
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| Title | Multi-Head Graph Attention Adversarial Autoencoder Network for Unsupervised Change Detection Using Heterogeneous Remote Sensing Images |
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