{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:18:05Z","timestamp":1780355885151,"version":"3.54.1"},"reference-count":59,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,13]],"date-time":"2020-04-13T00:00:00Z","timestamp":1586736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602423"],"award-info":[{"award-number":["61602423"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Henan Province Science and Technology Breakthrough Project","award":["172102410088"],"award-info":[{"award-number":["172102410088"]}]},{"name":"Open 331 Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources","award":["No grant number"],"award-info":[{"award-number":["No grant number"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>In this paper, we propose a new architecture of densely connected convolutional networks for pan-sharpening (DCCNP). Since the traditional convolution neural network (CNN) has difficulty handling the lack of a training sample set in the field of remote sensing image fusion, it easily leads to overfitting and the vanishing gradient problem. Therefore, we employed an effective two-dense-block architecture to solve these problems. Meanwhile, to reduce the network architecture complexity, the batch normalization (BN) layer was removed in the design architecture of DenseNet. A new architecture of DenseNet for pan-sharpening, called DCCNP, is proposed, which uses a bottleneck layer and compression factors to narrow the network and reduce the network parameters, effectively suppressing overfitting. The experimental results show that the proposed method can yield a higher performance compared with other state-of-the-art pan-sharpening methods. The proposed method not only improves the spatial resolution of multi-spectral images, but also maintains the spectral information well.<\/jats:p>","DOI":"10.3390\/ijgi9040242","type":"journal-article","created":{"date-parts":[[2020,4,14]],"date-time":"2020-04-14T03:10:01Z","timestamp":1586833801000},"page":"242","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A New Architecture of Densely Connected Convolutional Networks for Pan-Sharpening"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0095-1354","authenticated-orcid":false,"given":"Wei","family":"Huang","sequence":"first","affiliation":[{"name":"School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6703-6882","authenticated-orcid":false,"given":"Hua","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6465-8678","authenticated-orcid":false,"given":"Le","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1109\/JSTARS.2019.2915588","article-title":"Adjacent superpixel-based multiscale spatial-spectral kernel for hyperspectral classification","volume":"312","author":"Sun","year":"2019","journal-title":"IEEE J. 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