{"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/boosting-the-accuracy-of-multi-spectral-image","title":"Boosting the accuracy of multi-spectral image pan-sharpening by learning a deep residual network","arxiv_id":"1705.07556","date":"2017-05-22","proceeding":null,"authors":["Yancong Wei","Qiangqiang Yuan","Huanfeng Shen","Liangpei Zhang"],"abstract":"In the field of fusing multi-spectral and panchromatic images\n(Pan-sharpening), the impressive effectiveness of deep neural networks has been\nrecently employed to overcome the drawbacks of traditional linear models and\nboost the fusing accuracy. However, to the best of our knowledge, existing\nresearch works are mainly based on simple and flat networks with relatively\nshallow architecture, which severely limited their performances. In this paper,\nthe concept of residual learning has been introduced to form a very deep\nconvolutional neural network to make a full use of the high non-linearity of\ndeep learning models. By both quantitative and visual assessments on a large\nnumber of high quality multi-spectral images from various sources, it has been\nsupported that our proposed model is superior to all mainstream algorithms\nincluded in the comparison, and achieved the highest spatial-spectral unified\naccuracy.","url_abs":"http://arxiv.org/abs/1705.07556v2","url_pdf":"http://arxiv.org/pdf/1705.07556v2.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":[],"tasks":[],"methods":[{"method_slug":"drpnn","method_name":"DRPNN"}],"datasets_introduced":[],"methods_introduced":[{"slug":"drpnn","name":"DRPNN","full_name":"Deep Residual Pansharpening Neural Network"}],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1705.07556","atlas_url":"https://app.syntology.ai/?focus=1705.07556","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}