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Unfortunately, PCNNs are difficult to directly apply to multispectral image fusion, especially when the spectral fidelity is considered. A key problem is that most fusion methods using PCNNs usually focus on the selection mechanism either in the space domain or in the transform domain, rather than a details injection mechanism, which is of utmost importance in multispectral image fusion. Thus, a novel pansharpening PCNN model for multispectral image fusion is proposed. The new model is designed to acquire the spectral fidelity in terms of human visual perception for the fusion tasks. The experimental results, examined by different kinds of datasets, show the suitability of the proposed model for pansharpening.<\/jats:p>","DOI":"10.3390\/s20102764","type":"journal-article","created":{"date-parts":[[2020,5,12]],"date-time":"2020-05-12T10:53:55Z","timestamp":1589280835000},"page":"2764","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["An Improved Pulse-Coupled Neural Network Model for Pansharpening"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3410-8891","authenticated-orcid":false,"given":"Xiaojun","family":"Li","sequence":"first","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haowen","family":"Yan","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0335-3878","authenticated-orcid":false,"given":"Weiying","family":"Xie","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0223-1094","authenticated-orcid":false,"given":"Lu","family":"Kang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Tian","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, CAS, Shenzhen 518172, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.04.050","article-title":"Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery","volume":"214","author":"Huang","year":"2018","journal-title":"Remote Sens. 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