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It is widely applied in the registration of these two image types, leading to enhanced environmental perception and improved efficiency in perception computation. However, the traditional estimation methods are frequently challenged by insufficient feature points and the low similarity in features when dealing with these images, which results in poor performance. Deep-learning-based methods have attempted to address these issues by leveraging strong deep feature extraction capabilities but often overlook the importance of precisely guided feature matching in regression networks. Consequently, exactly acquiring feature correlations between multi-modal images remains a complex task. In this study, we propose a feature correlation transformer method, devised to offer explicit guidance for feature matching for the task of homography estimation between infrared and visible images. First, we propose a feature patch, which is used as a basic unit for correlation computation, thus effectively coping with modal differences in infrared and visible images. Additionally, we propose a novel cross-image attention mechanism to identify correlations between varied modal images, thus transforming the multi-source images homography estimation problem into a single-source images problem by achieving source-to-target image mapping in the feature dimension. Lastly, we propose a feature correlation loss (FCL) to induce the network into learning a distinctive target feature map, further enhancing source-to-target image mapping. To validate the effectiveness of the newly proposed components, we conducted extensive experiments to demonstrate the superiority of our method compared with existing methods in both quantitative and qualitative aspects.<\/jats:p>","DOI":"10.3390\/rs15143535","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:39:41Z","timestamp":1689295181000},"page":"3535","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Infrared and Visible Image Homography Estimation Based on Feature Correlation Transformers for Enhanced 6G Space\u2013Air\u2013Ground Integrated Network Perception"],"prefix":"10.3390","volume":"15","author":[{"given":"Xingyi","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China"}]},{"given":"Yinhui","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China"}]},{"given":"Qiang","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China"}]},{"given":"Yun","family":"Rui","sequence":"additional","affiliation":[{"name":"School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China"}]},{"given":"Chang","family":"Shu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China"}]},{"given":"Yuezhou","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China"}]},{"given":"Zhige","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China"}]},{"given":"Yuanqing","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liao, Z., Chen, C., Ju, Y., He, C., Jiang, J., and Pei, Q. 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