{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:46:31Z","timestamp":1778082391665,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,13]],"date-time":"2022-11-13T00:00:00Z","timestamp":1668297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61971388"],"award-info":[{"award-number":["61971388"]}]},{"name":"Alexander von Humboldt Foundation","award":["61971388"],"award-info":[{"award-number":["61971388"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Different scales of the objects pose a great challenge for the segmentation of remote sensing images of special scenes. This paper focuses on the problem of large-scale variations of the target objects via a dynamical receptive field of the deep network. We construct a Gaussian dynamic convolution network by introducing a dynamic convolution layer to enhance remote sensing image understanding. Moreover, we propose a new Gaussian pyramid pooling (GPP) for multi-scale object segmentation. The proposed network can expand the size of the receptive field and improve its efficiency in aggregating contextual information. Experiments verify that our method outperforms the popular semantic segmentation methods on large remote sensing image datasets, including iSAID and LoveDA. Moreover, we conduct experiments to demonstrate that the Gaussian dynamic convolution works more effectively on remote sensing images than other convolutional layers.<\/jats:p>","DOI":"10.3390\/rs14225736","type":"journal-article","created":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T04:24:10Z","timestamp":1668399850000},"page":"5736","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Gaussian Dynamic Convolution for Semantic Segmentation in Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Mingzhe","family":"Feng","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Haide College and Institute of Advanced Ocean Study, Ocean University of China, Qingdao 266100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1870-9037","authenticated-orcid":false,"given":"Xin","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Haide College and Institute of Advanced Ocean Study, Ocean University of China, Qingdao 266100, China"},{"name":"The Department of Aerospace and Geodesy, Technical University of Munich, 80333 Munich, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyu","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Haide College and Institute of Advanced Ocean Study, Ocean University of China, Qingdao 266100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2842-3526","authenticated-orcid":false,"given":"Haoran","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, M., Hu, X., Zhao, L., Lv, Y., Luo, M., and Pang, S. 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