{"type":"ScholarlyArticle","title":"Spatial-Spectral Similarity-Guided Fusion Network for Pansharpening","authors":[{"name":"Jiazhuang Xiong","slug":"jiazhuang-xiong","given":"Jiazhuang","family":"Xiong","affiliation":""},{"name":"Yongshan Zhang","slug":"yongshan-zhang","given":"Yongshan","family":"Zhang","affiliation":""},{"name":"Xinxin Wang","slug":"xinxin-wang","given":"Xinxin","family":"Wang","affiliation":""},{"name":"Lefei Zhang","slug":"lefei-zhang","given":"Lefei","family":"Zhang","affiliation":""}],"abstract":"Pansharpening fuses lower-resolution multispectral (LRMS) images with high-resolution panchromatic (PAN) images to generate high-resolution multispectral (HRMS) images that preserves both spatial and spectral information. Most deep pansharpening methods face challenges in cross-modal feature extraction and fusion, as well as in exploring the similarities between the fused image and both PAN and LRMS images. In this paper, we propose a spatial-spectral similarity-guided fusion network (S3FNet) for pansharpening. This architecture is composed of three parts. Specifically, a shallow feature extraction layer learns initial spatial, spectral and fused features from PAN and LRMS images. Then, a multi-branch asymmetric encoder, consisting of spatial, spectral and fusion branches, generates corresponding high-level features at different scales. A multi-scale reconstruction decoder, equipped with a well-designed cross-feature multi-head attention fusion block, processes the intermediate feature maps to generate HRMS images. To ensure HRMS images retain maximum spatial and spectral information, a similarity-constrained loss is defined for network training. Extensive experiments demonstrate the effectiveness of our S3FNet over state-of-the-art methods. The code is released at https://github.com/ZhangYongshan/S3FNet.","doi":"10.24963/ijcai.2025/233","url":"https://ijcai.org/proceedings/2025/233","pdf":"https://ijcai.org/proceedings/2025/0233.pdf","markdown":"https://ijcai.org/proceedings/2025/233.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/233","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":233,"pages":{"first":2089,"last":2097},"keywords":["Computer Vision: CV: Multimodal learning","Computer Vision: CV: Low-level Vision"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}