Multi-Expert Learning Framework with the State Space Model for Optical and SAR Image Registration
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915787629920256 |
|---|---|
| author | Wang, Wei Quan, Dou Huyan, Ning Lv, Chonghua Wang, Shuang Li, Yunan Jiao, Licheng |
| author_facet | Wang, Wei Quan, Dou Huyan, Ning Lv, Chonghua Wang, Shuang Li, Yunan Jiao, Licheng |
| contents | Optical and Synthetic Aperture Radar (SAR) image registration is crucial for multi-modal image fusion and applications. However, several challenges limit the performance of existing deep learning-based methods in cross-modal image registration: (i) significant nonlinear radiometric variations between optical and SAR images affect the shared feature learning and matching; (ii) limited textures in images hinder discriminative feature extraction; (iii) the local receptive field of Convolutional Neural Networks (CNNs) restricts the learning of contextual information, while the Transformer can capture long-range global features but with high computational complexity. To address these issues, this paper proposes a multi-expert learning framework with the State Space Model (ME-SSM) for optical and SAR image registration. Firstly, to improve the registration performance with limited textures, ME-SSM constructs a multi-expert learning framework to capture shared features from multi-modal images. Specifically, it extracts features from various transformations of the input image and employs a learnable soft router to dynamically fuse these features, thereby enriching feature representations and improving registration performance. Secondly, ME-SSM introduces a state space model, Mamba, for feature extraction, which employs a multi-directional cross-scanning strategy to efficiently capture global contextual relationships with linear complexity. ME-SSM can expand the receptive field, enhance image registration accuracy, and avoid incurring high computational costs. Additionally, ME-SSM uses a multi-level feature aggregation (MFA) module to enhance the multi-scale feature fusion and interaction. Extensive experiments have demonstrated the effectiveness and advantages of our proposed ME-SSM on optical and SAR image registration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04397 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Expert Learning Framework with the State Space Model for Optical and SAR Image Registration Wang, Wei Quan, Dou Huyan, Ning Lv, Chonghua Wang, Shuang Li, Yunan Jiao, Licheng Computer Vision and Pattern Recognition Optical and Synthetic Aperture Radar (SAR) image registration is crucial for multi-modal image fusion and applications. However, several challenges limit the performance of existing deep learning-based methods in cross-modal image registration: (i) significant nonlinear radiometric variations between optical and SAR images affect the shared feature learning and matching; (ii) limited textures in images hinder discriminative feature extraction; (iii) the local receptive field of Convolutional Neural Networks (CNNs) restricts the learning of contextual information, while the Transformer can capture long-range global features but with high computational complexity. To address these issues, this paper proposes a multi-expert learning framework with the State Space Model (ME-SSM) for optical and SAR image registration. Firstly, to improve the registration performance with limited textures, ME-SSM constructs a multi-expert learning framework to capture shared features from multi-modal images. Specifically, it extracts features from various transformations of the input image and employs a learnable soft router to dynamically fuse these features, thereby enriching feature representations and improving registration performance. Secondly, ME-SSM introduces a state space model, Mamba, for feature extraction, which employs a multi-directional cross-scanning strategy to efficiently capture global contextual relationships with linear complexity. ME-SSM can expand the receptive field, enhance image registration accuracy, and avoid incurring high computational costs. Additionally, ME-SSM uses a multi-level feature aggregation (MFA) module to enhance the multi-scale feature fusion and interaction. Extensive experiments have demonstrated the effectiveness and advantages of our proposed ME-SSM on optical and SAR image registration. |
| title | Multi-Expert Learning Framework with the State Space Model for Optical and SAR Image Registration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.04397 |