{"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/fast-full-resolution-target-adaptive-cnn","title":"Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening Framework","arxiv_id":null,"date":"2023-01-05","proceeding":"MDPI Remote Sensing 2023 1","authors":["Matteo Ciotola","Giuseppe Scarpa"],"abstract":"In the last few years, there has been a renewed interest in data fusion techniques, and, in particular, in pansharpening due to a paradigm shift from model-based to data-driven approaches, supported by the recent advances in deep learning. Although a plethora of convolutional neural networks (CNN) for pansharpening have been devised, some fundamental issues still wait for answers. Among these, cross-scale and cross-datasets generalization capabilities are probably the most urgent ones since most of the current networks are trained at a different scale (reduced-resolution), and, in general, they are well-fitted on some datasets but fail on others. A recent attempt to address both these issues leverages on a target-adaptive inference scheme operating with a suitable full-resolution loss. On the downside, such an approach pays an additional computational overhead due to the adaptation phase. In this work, we propose a variant of this method with an effective target-adaptation scheme that allows for the reduction in inference time by a factor of ten, on average, without accuracy loss. A wide set of experiments carried out on three different datasets, GeoEye-1, WorldView-2 and WorldView-3, prove the computational gain obtained while keeping top accuracy scores compared to state-of-the-art methods, both model-based and deep-learning ones. The generality of the proposed solution has also been validated, applying the new adaptation framework to different CNN models.","url_abs":"https://www.mdpi.com/2072-4292/15/2/319","url_pdf":"https://www.mdpi.com/2072-4292/15/2/319/pdf?version=1673404470","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":"fast-full-resolution-target-adaptive-cnn","repo_url":"https://github.com/matciotola/fast-z-pnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"pansharpening","task_name":"Pansharpening"},{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"satellite-image-super-resolution","task_name":"satellite image super-resolution"}],"methods":[{"method_slug":"fail","method_name":"fail"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/pansharpening-on-worldview-3-adelaide","task":"Pansharpening","dataset":"WorldView-3 Adelaide","model":"Fast Z-PNN","rank_in_archive_order":2,"of":3,"metrics":{"D_lambda":"0.1373","D_rho":"0.1389"},"uses_additional_data":false},{"leaderboard":"/sota/pansharpening-on-worldview-3-adelaide","task":"Pansharpening","dataset":"WorldView-3 Adelaide","model":"Z-PNN","rank_in_archive_order":3,"of":3,"metrics":{"D_lambda":"0.1482","D_rho":"0.1360"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}