Dual Fine-Grained network with frequency Transformer for change detection on remote sensing images
Change detection is a fundamental yet challenging task in remote sensing, crucial for monitoring urban expansion, land use changes, and environmental dynamics. However, compared with common color images, objects in remote sensing images exhibit minimal interclass variation and significant intraclass...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225000408 |
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| author | Zhen Li Zhenxin Zhang Mengmeng Li Liqiang Zhang Xueli Peng Rixing He Leidong Shi |
| author_facet | Zhen Li Zhenxin Zhang Mengmeng Li Liqiang Zhang Xueli Peng Rixing He Leidong Shi |
| author_sort | Zhen Li |
| collection | DOAJ |
| description | Change detection is a fundamental yet challenging task in remote sensing, crucial for monitoring urban expansion, land use changes, and environmental dynamics. However, compared with common color images, objects in remote sensing images exhibit minimal interclass variation and significant intraclass variation in the spectral dimension, with obvious scale inconsistency in the spatial dimension. Change detection complexity presents significant challenges, including differentiating similar objects, accounting for scale variations, and identifying pseudo changes. This research introduces a dual fine-grained network with a frequency Transformer (named as FTransDF-Net) to address the above issues. Specifically, for small-scale and approximate spectral ground objects, the network employs an encoder-decoder architecture consisting of dual fine-grained gated (DFG) modules. This enables the extraction and fusion of fine-grained level information in dual dimensions of features, facilitating a comprehensive analysis of their differences and correlations. As a result, a dynamic fusion representation of salient information is achieved. Additionally, we develop a lightweight frequency transformer (LFT) with minimal parameters for detecting large-scale ground objects that undergo significant changes over time. This is achieved by incorporating a frequency attention (FA) module, which utilizes Fourier transform to model long-range dependencies and combines global adaptive attentive features with multi-level fine-grained features. Our comparative experiments across four publicly available datasets demonstrate that FTransDF-Net reaches advanced results. Importantly, it outperforms the leading comparison method by 1.23% and 2.46% regarding IoU metrics concerning CDD and DSIFN, respectively. Furthermore, efficacy for each module is substantiated through ablation experiments. The code is accessible on https://github.com/LeeThrzz/FTrans-DF-Net. |
| format | Article |
| id | doaj-art-89f85295b7fd4bc7acd59d89f0d398ff |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-89f85295b7fd4bc7acd59d89f0d398ff2025-02-10T04:34:17ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-02-01136104393Dual Fine-Grained network with frequency Transformer for change detection on remote sensing imagesZhen Li0Zhenxin Zhang1Mengmeng Li2Liqiang Zhang3Xueli Peng4Rixing He5Leidong Shi6Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China; College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, ChinaKey Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China; College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China; Corresponding author at: Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China.Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, ChinaState Key Laboratory of Remote Sensing Science, Department of Geographical Science, Beijing Normal University, Beijing 100875, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China; College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, ChinaKey Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China; College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, ChinaChange detection is a fundamental yet challenging task in remote sensing, crucial for monitoring urban expansion, land use changes, and environmental dynamics. However, compared with common color images, objects in remote sensing images exhibit minimal interclass variation and significant intraclass variation in the spectral dimension, with obvious scale inconsistency in the spatial dimension. Change detection complexity presents significant challenges, including differentiating similar objects, accounting for scale variations, and identifying pseudo changes. This research introduces a dual fine-grained network with a frequency Transformer (named as FTransDF-Net) to address the above issues. Specifically, for small-scale and approximate spectral ground objects, the network employs an encoder-decoder architecture consisting of dual fine-grained gated (DFG) modules. This enables the extraction and fusion of fine-grained level information in dual dimensions of features, facilitating a comprehensive analysis of their differences and correlations. As a result, a dynamic fusion representation of salient information is achieved. Additionally, we develop a lightweight frequency transformer (LFT) with minimal parameters for detecting large-scale ground objects that undergo significant changes over time. This is achieved by incorporating a frequency attention (FA) module, which utilizes Fourier transform to model long-range dependencies and combines global adaptive attentive features with multi-level fine-grained features. Our comparative experiments across four publicly available datasets demonstrate that FTransDF-Net reaches advanced results. Importantly, it outperforms the leading comparison method by 1.23% and 2.46% regarding IoU metrics concerning CDD and DSIFN, respectively. Furthermore, efficacy for each module is substantiated through ablation experiments. The code is accessible on https://github.com/LeeThrzz/FTrans-DF-Net.http://www.sciencedirect.com/science/article/pii/S1569843225000408Remote sensingChange detectionDual fine-grainedFrequency transformer |
| spellingShingle | Zhen Li Zhenxin Zhang Mengmeng Li Liqiang Zhang Xueli Peng Rixing He Leidong Shi Dual Fine-Grained network with frequency Transformer for change detection on remote sensing images International Journal of Applied Earth Observations and Geoinformation Remote sensing Change detection Dual fine-grained Frequency transformer |
| title | Dual Fine-Grained network with frequency Transformer for change detection on remote sensing images |
| title_full | Dual Fine-Grained network with frequency Transformer for change detection on remote sensing images |
| title_fullStr | Dual Fine-Grained network with frequency Transformer for change detection on remote sensing images |
| title_full_unstemmed | Dual Fine-Grained network with frequency Transformer for change detection on remote sensing images |
| title_short | Dual Fine-Grained network with frequency Transformer for change detection on remote sensing images |
| title_sort | dual fine grained network with frequency transformer for change detection on remote sensing images |
| topic | Remote sensing Change detection Dual fine-grained Frequency transformer |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225000408 |
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