{"url":"/method/pannet","slug":"pannet","name":"PanNet","full_name":"Pansharpening Network","full_name_withheld":false,"description_markdown":"We propose a deep network architecture for the pansharpening problem called PanNet. We incorporate domain-specific knowledge to design our PanNet architecture by focusing on the two aims of the pan-sharpening problem: spectral and spatial preservation. For spectral preservation, we add up-sampled multispectral images to the network output, which directly propagates the spectral information to the reconstructed image. To preserve the spatial structure, we train our network parameters in the high-pass filtering domain rather than the image domain. We show that the trained network generalizes well to images from different satellites without needing retraining. Experiments show significant improvement over state-of-the-art methods visually and in terms of standard quality metrics.","description_state":"present","introduced_year":null,"introduced_by":{"title":"PanNet: A Deep Network Architecture for Pan-Sharpening","paper":"/paper/pannet-a-deep-network-architecture-for-pan","first_author":"Junfeng Yang","n_authors":6,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/pannet-a-deep-network-architecture-for-pan"},"source":{"url":"http://openaccess.thecvf.com/content_iccv_2017/html/Yang_PanNet_A_Deep_ICCV_2017_paper.html","title":"PanNet: A Deep Network Architecture for Pan-Sharpening","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Convolutional Neural Networks","url":"/methods/category/convolutional-neural-networks","pwc_aliases":[]}],"n_papers_tagged":4,"archive_num_papers":4,"papers_newest_first":[{"paper":null,"title":"SDRCNN: A single-scale dense residual connected convolutional neural network for pansharpening","date":"2023-07-01","arxiv_id":"2307.00327","n_code_links":0,"syntology":null},{"paper":null,"title":"Proximal PanNet: A Model-Based Deep Network for Pansharpening","date":"2022-02-12","arxiv_id":"2203.04286","n_code_links":0,"syntology":null},{"paper":"/paper/pansharpening-by-convolutional-neural","title":"Pansharpening by convolutional neural networks in the full resolution framework","date":"2021-11-16","arxiv_id":"2111.08334","n_code_links":2,"syntology":null},{"paper":"/paper/pannet-a-deep-network-architecture-for-pan","title":"PanNet: A Deep Network Architecture for Pan-Sharpening","date":"2017-10-01","arxiv_id":null,"n_code_links":0,"syntology":null}],"papers_shown":4,"tasks":[{"task":"/task/pansharpening","name":"Pansharpening","papers":3},{"task":"/task/deep-learning","name":"Deep Learning","papers":1},{"task":"/task/image-super-resolution","name":"Image Super-Resolution","papers":1},{"task":"/task/super-resolution","name":"Super-Resolution","papers":1},{"task":"/task/satellite-image-super-resolution","name":"satellite image super-resolution","papers":1}],"tasks_shown":5,"n_tasks":5,"usage_by_year":[{"year":"2017","papers":1},{"year":"2021","papers":1},{"year":"2022","papers":1},{"year":"2023","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/pannet"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}