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Proceedings Paper

A random optimization approach for inherent optic properties of nearshore waters
Author(s): Aijun Zhou; Yongshuai Hao; Kuo Xu; Heng Zhou
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Paper Abstract

Traditional method of water quality sampling is time-consuming and highly cost. It can not meet the needs of social development. Hyperspectral remote sensing technology has well time resolution, spatial coverage and more general segment information on spectrum. It has a good potential in water quality supervision. Via the method of semi-analytical, remote sensing information can be related with the water quality. The inherent optical properties are used to quantify the water quality, and an optical model inside the water is established to analysis the features of water. By stochastic optimization algorithm Threshold Acceptance, a global optimization of the unknown model parameters can be determined to obtain the distribution of chlorophyll, organic solution and suspended particles in water. Via the improvement of the optimization algorithm in the search step, the processing time will be obviously reduced, and it will create more opportunity for the increasing the number of parameter. For the innovation definition of the optimization steps and standard, the whole inversion process become more targeted, thus improving the accuracy of inversion. According to the application result for simulated data given by IOCCG and field date provided by NASA, the approach model get continuous improvement and enhancement. Finally, a low-cost, effective retrieval model of water quality from hyper-spectral remote sensing can be achieved.

Paper Details

Date Published: 25 October 2016
PDF: 6 pages
Proc. SPIE 10156, Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology, 101560O (25 October 2016); doi: 10.1117/12.2245067
Show Author Affiliations
Aijun Zhou, Dalian Naval Academy (China)
Yongshuai Hao, Dalian Naval Academy (China)
Kuo Xu, Dalian Naval Academy (China)
Heng Zhou, Dalian Naval Academy (China)


Published in SPIE Proceedings Vol. 10156:
Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology

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