SGCM: Semantic and Geometric Consistency for Robust Aerial Image Matching
Aerial image matching remains an ongoing challenge due to the significant geometric distortions and nonlinear radiation distortion. Most current approaches primarily focus on low-level image features, while overlooking the potential of high-level semantic information to guide the matching process, l...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11105454/ |
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| author | Xiangzeng Liu Guanglu Shi Chi Wang Xiaodong Zhang Qiguang Miao |
| author_facet | Xiangzeng Liu Guanglu Shi Chi Wang Xiaodong Zhang Qiguang Miao |
| author_sort | Xiangzeng Liu |
| collection | DOAJ |
| description | Aerial image matching remains an ongoing challenge due to the significant geometric distortions and nonlinear radiation distortion. Most current approaches primarily focus on low-level image features, while overlooking the potential of high-level semantic information to guide the matching process, limiting the robustness and accuracy of these methods in complex scenarios. In this article, we propose a novel coarse-to-fine aerial image matching framework called SGCM, which considers both semantic and geometric consistency in feature representation. Concretely, in the coarse matching stage, the SAM foundation model is leveraged to efficiently extract salient structural regions, providing valuable semantic guidance for subsequently matching procedure. To achieve accurate region matching, we construct the region description graphs that incorporate both semantic categories and geometric attributes information. A graph neural network (GNN) is then employed for feature aggregation and node descriptor updates, leading to more robust region representations. Coarse matching is performed by computing a similarity matrix between node descriptors, which helps effectively mitigate rotational and scaling differences between input images. Consequently, a fine matching strategy combining intraregion point matching and global matching is developed, which significantly improves the robustness of image matching under geometric variations. The performance of our method has been thoroughly evaluated on three challenging aerial image datasets and compared with five state-of-the-art matching methods. Extensive experimental results demonstrate the substantial advantages of SGCM in terms of matching accuracy and efficiency. |
| format | Article |
| id | doaj-art-b3034c5148c74c13a9e8366fa0faf7db |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-b3034c5148c74c13a9e8366fa0faf7db2025-08-20T03:37:01ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118196961970910.1109/JSTARS.2025.359412711105454SGCM: Semantic and Geometric Consistency for Robust Aerial Image MatchingXiangzeng Liu0https://orcid.org/0000-0002-2751-6096Guanglu Shi1https://orcid.org/0009-0008-7185-4763Chi Wang2https://orcid.org/0009-0006-3230-1483Xiaodong Zhang3Qiguang Miao4https://orcid.org/0000-0001-6766-8310School of Computer Science and Technology, Xidian University, Xi’an, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an, ChinaAerial image matching remains an ongoing challenge due to the significant geometric distortions and nonlinear radiation distortion. Most current approaches primarily focus on low-level image features, while overlooking the potential of high-level semantic information to guide the matching process, limiting the robustness and accuracy of these methods in complex scenarios. In this article, we propose a novel coarse-to-fine aerial image matching framework called SGCM, which considers both semantic and geometric consistency in feature representation. Concretely, in the coarse matching stage, the SAM foundation model is leveraged to efficiently extract salient structural regions, providing valuable semantic guidance for subsequently matching procedure. To achieve accurate region matching, we construct the region description graphs that incorporate both semantic categories and geometric attributes information. A graph neural network (GNN) is then employed for feature aggregation and node descriptor updates, leading to more robust region representations. Coarse matching is performed by computing a similarity matrix between node descriptors, which helps effectively mitigate rotational and scaling differences between input images. Consequently, a fine matching strategy combining intraregion point matching and global matching is developed, which significantly improves the robustness of image matching under geometric variations. The performance of our method has been thoroughly evaluated on three challenging aerial image datasets and compared with five state-of-the-art matching methods. Extensive experimental results demonstrate the substantial advantages of SGCM in terms of matching accuracy and efficiency.https://ieeexplore.ieee.org/document/11105454/Aerial image matchinggraph neural network (GNN)SAM modelsemantic and geometric consistency |
| spellingShingle | Xiangzeng Liu Guanglu Shi Chi Wang Xiaodong Zhang Qiguang Miao SGCM: Semantic and Geometric Consistency for Robust Aerial Image Matching IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Aerial image matching graph neural network (GNN) SAM model semantic and geometric consistency |
| title | SGCM: Semantic and Geometric Consistency for Robust Aerial Image Matching |
| title_full | SGCM: Semantic and Geometric Consistency for Robust Aerial Image Matching |
| title_fullStr | SGCM: Semantic and Geometric Consistency for Robust Aerial Image Matching |
| title_full_unstemmed | SGCM: Semantic and Geometric Consistency for Robust Aerial Image Matching |
| title_short | SGCM: Semantic and Geometric Consistency for Robust Aerial Image Matching |
| title_sort | sgcm semantic and geometric consistency for robust aerial image matching |
| topic | Aerial image matching graph neural network (GNN) SAM model semantic and geometric consistency |
| url | https://ieeexplore.ieee.org/document/11105454/ |
| work_keys_str_mv | AT xiangzengliu sgcmsemanticandgeometricconsistencyforrobustaerialimagematching AT guanglushi sgcmsemanticandgeometricconsistencyforrobustaerialimagematching AT chiwang sgcmsemanticandgeometricconsistencyforrobustaerialimagematching AT xiaodongzhang sgcmsemanticandgeometricconsistencyforrobustaerialimagematching AT qiguangmiao sgcmsemanticandgeometricconsistencyforrobustaerialimagematching |