A Multisource Image Matching Method Based on Contrastive Network With Similarity Weighting
Multisource remote sensing (MRS) image matching can provide more accurate data support for a variety of remote sensing tasks, but the disparities in imaging characteristics among different sensors bring significant challenges for effective image matching. In this article, we propose an image matchin...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10858329/ |
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| Summary: | Multisource remote sensing (MRS) image matching can provide more accurate data support for a variety of remote sensing tasks, but the disparities in imaging characteristics among different sensors bring significant challenges for effective image matching. In this article, we propose an image matching method based on the contrastive network with similarity statistics weighting for MRS registration. First, we proposed a sampling strategy that selects hard negatives based on similarity statistics measures. These hard samples with more valuable information can promote the abundance of multimodal consistency information. Subsequently, the samples are encoded with a self-attention contrastive network for semantic feature embedding, enabling the learning of effective representations in the latent space. In addition, a similarity-weighted loss function is designed to guide the training process, aiming to improve the discrimination ability of the network. Experimental results demonstrate that our proposed method can derive accurate and robust results in MRS matching, exhibiting superior matching performance with respect to other methods. |
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| ISSN: | 1939-1404 2151-1535 |