Hybrid Artificial Neural Networks for Modeling Shallow-Water Bathymetry via Satellite Imagery

被引：0
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作者：
Kaloop, Mosbeh R. [1 ,2 ,3 ]
El-Diasty, Mohammed [4 ,5 ]
Hu, Jong Wan [6 ,7 ]
Zarzoura, Fawzi [8 ]
机构：
[1] Incheon National University, Department of Civil and Environmental Engineering, Incheon,22012, Korea, Republic of
[2] Incheon National University, Incheon Disaster Prevention Research Center, Incheon,22012, Korea, Republic of
[3] Mansoura University, Public Works and Civil Engineering Department, Mansoura,35516, Egypt
[4] Mansoura University, Public Works and Civil Engineering Department, Mansoura,35516, Egypt
[5] Sultan Qaboos University, Civil and Architectural Engineering Department, Muscat,123, Oman
[6] Incheon National University, Department of Civil and Environmental Engineering, Incheon,22012, Korea, Republic of
[7] Incheon National University, Incheon Disaster Prevention Research Center, Incheon,22012, Korea, Republic of
[8] Mansoura University, Public Works and Civil Engineering Department, Mansoura,35516, Egypt
来源：
IEEE Transactions on Geoscience and Remote Sensing | 2022年 / 60卷
关键词：
Compendex;
D O I：
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中图分类号：
学科分类号：
摘要：
Particle swarm optimization (PSO) - Satellite imagery - Maps - Regression analysis - Infrared devices - Bathymetry - Machine learning - Mapping - Neural networks - Optical radar
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