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Spatial-Frequency Collaborative Learning Network for Remote Sensing Change Detection.

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Title: Spatial-Frequency Collaborative Learning Network for Remote Sensing Change Detection.
Authors: Wang, Mengmeng1 (AUTHOR), He, Jie2,3 (AUTHOR), Zhou, Chaohu2,3 (AUTHOR), Huang, Diping1,4 (AUTHOR), Qu, Ya1,2,3 (AUTHOR), Zhu, Bai2,4 (AUTHOR), Xie, Qicai2,3 (AUTHOR) 15680906069@163.com
Source: Remote Sensing. Jun2026, Vol. 18 Issue 12, p2031. 22p.
Subjects: Frequency-domain analysis, Change-point problems, Deep learning, Artificial neural networks
Abstract: Highlights: What are the main findings? We introduce a Spatial-Frequency Collaborative Learning Network (SFCLNet) that combines feature representations from both spatial and frequency domains for remote sensing change detection. The proposed SFCLNet achieves competitive performance on three change detection datasets and outperforms several recently published methods. What are the implications of the main findings? Integrating features from both spatial and frequency domains enhances the model's ability to discriminate changed regions. The proposed network provides a useful reference for developing more discriminative change detection methods that leverage spatial-frequency collaborative representations. Recent advances in deep learning have substantially improved remote sensing change detection. However, most existing models still describe bi-temporal differences mainly from the spatial domain, making it difficult to fully capture complementary frequency domain cues in complex scenes. To address this limitation, this paper introduces a Spatial-Frequency Collaborative Learning Network (SFCLNet) for remote sensing change detection. In particular, hierarchical features are extracted from bi-temporal images using a Siamese backbone. A Spatial Domain Feature Fusion (SDFF) module is then designed to enhance local structural variation details by modeling the structural consistency between bi-temporal features. Meanwhile, a Frequency Domain Feature Fusion (FDFF) module is introduced to characterize frequency domain cues by separately modeling phase and amplitude components. Furthermore, a Spatial-Frequency Collaborative Fusion (SFCF) module is developed to obtain more discriminative change feature representations by integrating the fused spatial domain and frequency domain features in a channel-wise competitive way. Finally, the pixel-wise results are predicted using a UNet-based decoder that progressively aggregates the fused multi-level features. Experimental results on Google, LEVIR, and MSRS benchmark datasets show that SFCLNet achieves F1 scores of 88.94%, 91.39%, and 74.97%, respectively, outperforming several recently published methods. These results verify the effectiveness of jointly exploiting the frequency domain and spatial domain for remote sensing change detection. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Spatial-Frequency Collaborative Learning Network for Remote Sensing Change Detection.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Mengmeng%22">Wang, Mengmeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22He%2C+Jie%22">He, Jie</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Chaohu%22">Zhou, Chaohu</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Diping%22">Huang, Diping</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qu%2C+Ya%22">Qu, Ya</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Bai%22">Zhu, Bai</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xie%2C+Qicai%22">Xie, Qicai</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> 15680906069@163.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p2031. 22p.
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  Data: <searchLink fieldCode="DE" term="%22Frequency-domain+analysis%22">Frequency-domain analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Change-point+problems%22">Change-point problems</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? We introduce a Spatial-Frequency Collaborative Learning Network (SFCLNet) that combines feature representations from both spatial and frequency domains for remote sensing change detection. The proposed SFCLNet achieves competitive performance on three change detection datasets and outperforms several recently published methods. What are the implications of the main findings? Integrating features from both spatial and frequency domains enhances the model's ability to discriminate changed regions. The proposed network provides a useful reference for developing more discriminative change detection methods that leverage spatial-frequency collaborative representations. Recent advances in deep learning have substantially improved remote sensing change detection. However, most existing models still describe bi-temporal differences mainly from the spatial domain, making it difficult to fully capture complementary frequency domain cues in complex scenes. To address this limitation, this paper introduces a Spatial-Frequency Collaborative Learning Network (SFCLNet) for remote sensing change detection. In particular, hierarchical features are extracted from bi-temporal images using a Siamese backbone. A Spatial Domain Feature Fusion (SDFF) module is then designed to enhance local structural variation details by modeling the structural consistency between bi-temporal features. Meanwhile, a Frequency Domain Feature Fusion (FDFF) module is introduced to characterize frequency domain cues by separately modeling phase and amplitude components. Furthermore, a Spatial-Frequency Collaborative Fusion (SFCF) module is developed to obtain more discriminative change feature representations by integrating the fused spatial domain and frequency domain features in a channel-wise competitive way. Finally, the pixel-wise results are predicted using a UNet-based decoder that progressively aggregates the fused multi-level features. Experimental results on Google, LEVIR, and MSRS benchmark datasets show that SFCLNet achieves F1 scores of 88.94%, 91.39%, and 74.97%, respectively, outperforming several recently published methods. These results verify the effectiveness of jointly exploiting the frequency domain and spatial domain for remote sensing change detection. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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        Type: general
      – SubjectFull: Change-point problems
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      – SubjectFull: Deep learning
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      – TitleFull: Spatial-Frequency Collaborative Learning Network for Remote Sensing Change Detection.
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              Text: Jun2026
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              Y: 2026
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