{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/remote-sensing-image-fusion-based-on-two","title":"Remote Sensing Image Fusion Based on Two-stream Fusion Network","arxiv_id":"1711.02549","date":"2017-11-07","proceeding":null,"authors":["Xiangyu Liu","Qingjie Liu","Yunhong Wang"],"abstract":"Remote sensing image fusion (also known as pan-sharpening) aims at generating\nhigh resolution multi-spectral (MS) image from inputs of a high spatial\nresolution single band panchromatic (PAN) image and a low spatial resolution\nmulti-spectral image. Inspired by the astounding achievements of convolutional\nneural networks (CNNs) in a variety of computer vision tasks, in this paper, we\npropose a two-stream fusion network (TFNet) to address the problem of\npan-sharpening. Unlike previous CNN based methods that consider pan-sharpening\nas a super resolution problem and perform pan-sharpening in pixel level, the\nproposed TFNet aims to fuse PAN and MS images in feature level and reconstruct\nthe pan-sharpened image from the fused features. The TFNet mainly consists of\nthree parts. The first part is comprised of two networks extracting features\nfrom PAN and MS images, respectively. The subsequent network fuses them\ntogether to form compact features that represent both spatial and spectral\ninformation of PAN and MS images, simultaneously. Finally, the desired high\nspatial resolution MS image is recovered from the fused features through an\nimage reconstruction network. Experiments on Quickbird and \\mbox{GaoFen-1}\nsatellite images demonstrate that the proposed TFNet can fuse PAN and MS\nimages, effectively, and produce pan-sharpened images competitive with even\nsuperior to state of the arts.","url_abs":"http://arxiv.org/abs/1711.02549v3","url_pdf":"http://arxiv.org/pdf/1711.02549v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"remote-sensing-image-fusion-based-on-two","repo_url":"https://github.com/liouxy/tfnet_pytorch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"two","task_name":"Vocal Bursts Valence Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.02549","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}