Proceedings Paper
Classification of coastal areas by airborne hyperspectral image| Format | Member Price | Non-Member Price |
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Paper Abstract
In recent years hyperspectral remote sensing has been widely used in the applications of geology agriculture forest ocean etc. This work assessed the feasibility of hyperspectral technique in coastal zone remote sensing. Data was acquired by Operational Modular Imaging Spectrometer (OMIS). Field spectrum of each class was measured and analyzed to extract certain spectral feature. We proffer a hybrid decision tree classification algorithm combined with optimum spectral features of class pairs to every tree node and step by step classified out coastal vegetation water body rocky shore sand beach mudflat and artificial objects etc. The results show that hyperspectral data can be used to classify coastal landscape more accurate than multispectral image.
Paper Details
Date Published: 12 May 2005
PDF: 6 pages
Proc. SPIE 5832, Optical Technologies for Atmospheric, Ocean, and Environmental Studies, (12 May 2005); doi: 10.1117/12.619684
Published in SPIE Proceedings Vol. 5832:
Optical Technologies for Atmospheric, Ocean, and Environmental Studies
Daren Lu; Gennadii G. Matvienko, Editor(s)
PDF: 6 pages
Proc. SPIE 5832, Optical Technologies for Atmospheric, Ocean, and Environmental Studies, (12 May 2005); doi: 10.1117/12.619684
Show Author Affiliations
Hongjie Zhou, Zhejiang Univ. (China)
State Key Lab. of Ocean Dynamic Processes and Satellite Oceanography, SOA (China)
Zhihua Mao, State Key Lab. of Ocean Dynamic Processes and Satellite Oceanography, SOA (China)
State Key Lab. of Ocean Dynamic Processes and Satellite Oceanography, SOA (China)
Zhihua Mao, State Key Lab. of Ocean Dynamic Processes and Satellite Oceanography, SOA (China)
Difeng Wang, State Key Lab. of Ocean Dynamic Processes and Satellite Oceanography, SOA (China)
Published in SPIE Proceedings Vol. 5832:
Optical Technologies for Atmospheric, Ocean, and Environmental Studies
Daren Lu; Gennadii G. Matvienko, Editor(s)
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