{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T14:43:43Z","timestamp":1790261023220,"version":"4.1.0"},"reference-count":78,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T00:00:00Z","timestamp":1697155200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Automated Identifying of Environment Changes Using Satellite Time-Series, Dragon 5 Cooperation 2020\u20132024","award":["57971"],"award-info":[{"award-number":["57971"]}]},{"name":"Automated Identifying of Environment Changes Using Satellite Time-Series, Dragon 5 Cooperation 2020\u20132024","award":["JSZRHYKJ202207"],"award-info":[{"award-number":["JSZRHYKJ202207"]}]},{"name":"Automated Identifying of Environment Changes Using Satellite Time-Series, Dragon 5 Cooperation 2020\u20132024","award":["2020010004"],"award-info":[{"award-number":["2020010004"]}]},{"name":"Jiangsu Provincial Marine Science and Technology Innovation Project, Research and Application Demonstration of Remote Sensing Monitoring Method for Jiangsu Coastal Zone Resources with Multi-source Remote Sensing Data","award":["57971"],"award-info":[{"award-number":["57971"]}]},{"name":"Jiangsu Provincial Marine Science and Technology Innovation Project, Research and Application Demonstration of Remote Sensing Monitoring Method for Jiangsu Coastal Zone Resources with Multi-source Remote Sensing Data","award":["JSZRHYKJ202207"],"award-info":[{"award-number":["JSZRHYKJ202207"]}]},{"name":"Jiangsu Provincial Marine Science and Technology Innovation Project, Research and Application Demonstration of Remote Sensing Monitoring Method for Jiangsu Coastal Zone Resources with Multi-source Remote Sensing Data","award":["2020010004"],"award-info":[{"award-number":["2020010004"]}]},{"name":"Integration and Application Demonstration in the Marine Field","award":["57971"],"award-info":[{"award-number":["57971"]}]},{"name":"Integration and Application Demonstration in the Marine Field","award":["JSZRHYKJ202207"],"award-info":[{"award-number":["JSZRHYKJ202207"]}]},{"name":"Integration and Application Demonstration in the Marine Field","award":["2020010004"],"award-info":[{"award-number":["2020010004"]}]},{"name":"national innovation and entrepreneurship training program for college students","award":["57971"],"award-info":[{"award-number":["57971"]}]},{"name":"national innovation and entrepreneurship training program for college students","award":["JSZRHYKJ202207"],"award-info":[{"award-number":["JSZRHYKJ202207"]}]},{"name":"national innovation and entrepreneurship training program for college students","award":["2020010004"],"award-info":[{"award-number":["2020010004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring the shipyard production state is of great significance to shipbuilding industry development and coastal resource utilization. In this article, it is the first time that satellite remote sensing (RS) data is utilized to monitor the shipyard production state dynamically and efficiently, which can make up for the traditional production state data collection mode. According to the imaging characteristics of optical remote sensing images in shipyards with a different production state, the characteristics are analyzed to establish reliable production state evidence. Firstly, in order to obtain the characteristics of the production state of optical remote sensing data, the high-level semantic information in the shipyard is extracted by transfer learning convolutional neural networks (CNNs). Secondly, in the evidence fusion, for the conflict evidence from the core sites of the shipyard, an improved DS evidence fusion method is proposed, which constructs the correlation metric to measure the degree of conflict in evidence and designs the similarity metric to measure the credibility of evidence. Thirdly, the weight of all the evidence is calculated according to the similarity metric to correct the conflict evidence. The introduction of the iterative idea is motivated by the fact that the fusion result aligns more closely with the desired result, the iterative idea is introduced to correct the fusion result. This method can effectively solve the conflict of evidence and effectively improve the monitoring accuracy of the shipyard production state. In the experiments, the Yangtze River Delta and the Bohai Rim are selected to verify that the proposed method can accurately recognize the shipyard production state, which reveals the potential of satellite RS images in shipyard production state monitoring, and also provides a new research thought perspective for other industrial production state monitoring.<\/jats:p>","DOI":"10.3390\/rs15204958","type":"journal-article","created":{"date-parts":[[2023,10,14]],"date-time":"2023-10-14T14:59:59Z","timestamp":1697295599000},"page":"4958","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Novel Shipyard Production State Monitoring Method Based on Satellite Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Wanrou","family":"Qin","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Song","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Institute for Natural Disaster Risk Prevention and Emergency Management, China University of Geosciences (Wuhan), Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haitian","family":"Zhu","sequence":"additional","affiliation":[{"name":"Engineering with the Remote Sensing Monitoring Department of Sea Area and Islands, National Satellite Ocean Application Service, Beijing 100081, China"},{"name":"Key Laboratory of Space Ocean Remote Sensing and Application, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinli","family":"Yu","sequence":"additional","affiliation":[{"name":"Qilu Aerospace Information Research Institute, Jinan 250101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1993-2831","authenticated-orcid":false,"given":"Yuhong","family":"Tu","sequence":"additional","affiliation":[{"name":"College of Global Change and Earth System Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"012178","DOI":"10.1088\/1755-1315\/647\/1\/012178","article-title":"Influencing Factors and Optimization of Ship Energy Efficiency under the Background of Climate Change","volume":"647","author":"Jia","year":"2021","journal-title":"IOP Conf. 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