{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T16:17:41Z","timestamp":1778948261040,"version":"3.51.4"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>For the pansharpening problem, previous convolutional neural networks (CNNs) mainly concatenate high-resolution panchromatic (PAN) images and low-resolution multispectral (LR-MS) images in their architectures, which ignores the distinctive attributes of different sources. In this paper, we propose a convolution network with source-adaptive discriminative kernels, called ADKNet, for the pansharpening task. Those kernels consist of spatial kernels generated from PAN images containing rich spatial details and spectral kernels generated from LR-MS images containing abundant spectral information. The kernel generating process is specially designed to extract information discriminately and effectively. Furthermore, the kernels are learned in a pixel-by-pixel manner to characterize different information in distinct areas. Extensive experimental results indicate that ADKNet outperforms current state-of-the-art (SOTA) pansharpening methods in both quantitative and qualitative assessments, in the meanwhile only with about 60,000 network parameters. Also, the proposed network is extended to the hyperspectral image super-resolution (HSISR) problem, still yields SOTA performance, proving the universality of our model. The code is available at http:\/\/github.com\/liangjiandeng\/ADKNet.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/179","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"1283-1289","source":"Crossref","is-referenced-by-count":33,"title":["Source-Adaptive Discriminative Kernels based Network for Remote Sensing Pansharpening"],"prefix":"10.24963","author":[{"given":"Siran","family":"Peng","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang-Jian","family":"Deng","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin-Fan","family":"Hu","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuwei","family":"Zhuo","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","theme":"Artificial Intelligence","location":"Vienna, Austria","acronym":"IJCAI-2022","number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2022,7,23]]},"end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:08:10Z","timestamp":1658142490000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/179"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/179","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}