Multimodal Feature Fusion Network with Text Difference Enhancement for Remote Sensing Change Detection
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911138281684992 |
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| author | Zhou, Yijun Zhai, Yikui Ying, Zilu Xian, Tingfeng Zhou, Wenlve Zhou, Zhiheng Tian, Xiaolin Jia, Xudong Zhang, Hongsheng Chen, C. L. Philip |
| author_facet | Zhou, Yijun Zhai, Yikui Ying, Zilu Xian, Tingfeng Zhou, Wenlve Zhou, Zhiheng Tian, Xiaolin Jia, Xudong Zhang, Hongsheng Chen, C. L. Philip |
| contents | Although deep learning has advanced remote sensing change detection (RSCD), most methods rely solely on image modality, limiting feature representation, change pattern modeling, and generalization especially under illumination and noise disturbances. To address this, we propose MMChange, a multimodal RSCD method that combines image and text modalities to enhance accuracy and robustness. An Image Feature Refinement (IFR) module is introduced to highlight key regions and suppress environmental noise. To overcome the semantic limitations of image features, we employ a vision language model (VLM) to generate semantic descriptions of bitemporal images. A Textual Difference Enhancement (TDE) module then captures fine grained semantic shifts, guiding the model toward meaningful changes. To bridge the heterogeneity between modalities, we design an Image Text Feature Fusion (ITFF) module that enables deep cross modal integration. Extensive experiments on LEVIRCD, WHUCD, and SYSUCD demonstrate that MMChange consistently surpasses state of the art methods across multiple metrics, validating its effectiveness for multimodal RSCD. Code is available at: https://github.com/yikuizhai/MMChange. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03961 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multimodal Feature Fusion Network with Text Difference Enhancement for Remote Sensing Change Detection Zhou, Yijun Zhai, Yikui Ying, Zilu Xian, Tingfeng Zhou, Wenlve Zhou, Zhiheng Tian, Xiaolin Jia, Xudong Zhang, Hongsheng Chen, C. L. Philip Computer Vision and Pattern Recognition Artificial Intelligence Although deep learning has advanced remote sensing change detection (RSCD), most methods rely solely on image modality, limiting feature representation, change pattern modeling, and generalization especially under illumination and noise disturbances. To address this, we propose MMChange, a multimodal RSCD method that combines image and text modalities to enhance accuracy and robustness. An Image Feature Refinement (IFR) module is introduced to highlight key regions and suppress environmental noise. To overcome the semantic limitations of image features, we employ a vision language model (VLM) to generate semantic descriptions of bitemporal images. A Textual Difference Enhancement (TDE) module then captures fine grained semantic shifts, guiding the model toward meaningful changes. To bridge the heterogeneity between modalities, we design an Image Text Feature Fusion (ITFF) module that enables deep cross modal integration. Extensive experiments on LEVIRCD, WHUCD, and SYSUCD demonstrate that MMChange consistently surpasses state of the art methods across multiple metrics, validating its effectiveness for multimodal RSCD. Code is available at: https://github.com/yikuizhai/MMChange. |
| title | Multimodal Feature Fusion Network with Text Difference Enhancement for Remote Sensing Change Detection |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2509.03961 |