25 October 2016 A random optimization approach for inherent optic properties of nearshore waters
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Proceedings Volume 10156, Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology; 101560O (2016) https://doi.org/10.1117/12.2245067
Event: International Symposium on Optoelectronic Technology and Application 2016, 2016, Beijing, China
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.
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Aijun Zhou, Aijun Zhou, Yongshuai Hao, Yongshuai Hao, Kuo Xu, Kuo Xu, Heng Zhou, Heng Zhou, } "A random optimization approach for inherent optic properties of nearshore waters", Proc. SPIE 10156, Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology, 101560O (25 October 2016); doi: 10.1117/12.2245067; https://doi.org/10.1117/12.2245067
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