---
title: "Spatial-Spectral Similarity-Guided Fusion Network for Pansharpening"
authors:
  - "Jiazhuang Xiong"
  - "Yongshan Zhang"
  - "Xinxin Wang"
  - "Lefei Zhang"
doi: "10.24963/ijcai.2025/233"
url: "https://ijcai.org/proceedings/2025/233"
pdf: "https://ijcai.org/proceedings/2025/0233.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "2089-2097"
track: "Main Track"
keywords:
  - "Computer Vision: CV: Multimodal learning"
  - "Computer Vision: CV: Low-level Vision"
---

# Spatial-Spectral Similarity-Guided Fusion Network for Pansharpening

Jiazhuang Xiong, Yongshan Zhang, Xinxin Wang, Lefei Zhang

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 2089-2097. DOI: [10.24963/ijcai.2025/233](https://doi.org/10.24963/ijcai.2025/233)

## 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.