Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 419 - 430
Опубликована: Янв. 1, 2024
Язык: Английский
Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 419 - 430
Опубликована: Янв. 1, 2024
Язык: Английский
Remote Sensing, Год журнала: 2023, Номер 15(20), С. 4958 - 4958
Опубликована: Окт. 13, 2023
Monitoring the shipyard production state is of great significance to shipbuilding industry development and coastal resource utilization. In this article, it first time that satellite remote sensing (RS) data utilized monitor dynamically efficiently, which can make up for traditional collection mode. According imaging characteristics optical images in shipyards with a different state, are analyzed establish reliable evidence. Firstly, order obtain data, high-level semantic information extracted by transfer learning convolutional neural networks (CNNs). Secondly, evidence fusion, conflict from core sites shipyard, an improved DS fusion method proposed, constructs correlation metric measure degree designs similarity credibility Thirdly, weight all calculated according correct The introduction iterative idea motivated fact result aligns more closely desired result, introduced result. This effectively solve improve monitoring accuracy state. experiments, Yangtze River Delta Bohai Rim selected verify proposed accurately recognize reveals potential RS monitoring, also provides new research thought perspective other industrial monitoring.
Язык: Английский
Процитировано
1Earth Science Informatics, Год журнала: 2023, Номер 17(1), С. 797 - 812
Опубликована: Дек. 27, 2023
Язык: Английский
Процитировано
1Опубликована: Июль 11, 2024
Язык: Английский
Процитировано
0Опубликована: Июнь 28, 2024
Язык: Английский
Процитировано
0Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 419 - 430
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
0