Semi-supervised Multiscale Matching for SAR-Optical Image

• URL: http://arxiv.org/abs/2508.07812v1
• Date: Mon, 11 Aug 2025 09:55:39 GMT
• Title: Semi-supervised Multiscale Matching for SAR-Optical Image
• Authors: Jingze Gai, Changchun Li,
• Abstract summary: We propose a semi-supervised multiscale matching for SAR-optical image matching (S2M2-SAR)<n>Specifically, we pseudo-label those unlabeled SAR-optical image pairs with pseudo ground-truth similarity heatmaps.<n>We also introduce a cross-modal feature enhancement module trained using a cross-modality mutual independence loss.
• Score: 5.25009884148204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Driven by the complementary nature of optical and synthetic aperture radar (SAR) images, SAR-optical image matching has garnered significant interest. Most existing SAR-optical image matching methods aim to capture effective matching features by employing the supervision of pixel-level matched correspondences within SAR-optical image pairs, which, however, suffers from time-consuming and complex manual annotation, making it difficult to collect sufficient labeled SAR-optical image pairs. To handle this, we design a semi-supervised SAR-optical image matching pipeline that leverages both scarce labeled and abundant unlabeled image pairs and propose a semi-supervised multiscale matching for SAR-optical image matching (S2M2-SAR). Specifically, we pseudo-label those unlabeled SAR-optical image pairs with pseudo ground-truth similarity heatmaps by combining both deep and shallow level matching results, and train the matching model by employing labeled and pseudo-labeled similarity heatmaps. In addition, we introduce a cross-modal feature enhancement module trained using a cross-modality mutual independence loss, which requires no ground-truth labels. This unsupervised objective promotes the separation of modality-shared and modality-specific features by encouraging statistical independence between them, enabling effective feature disentanglement across optical and SAR modalities. To evaluate the effectiveness of S2M2-SAR, we compare it with existing competitors on benchmark datasets. Experimental results demonstrate that S2M2-SAR not only surpasses existing semi-supervised methods but also achieves performance competitive with fully supervised SOTA methods, demonstrating its efficiency and practical potential.

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