19 December 2008 Comparison of optimization methods for the hyperspectral semi-analytical model
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During recent years, more and more efforts have been focused on developing new models based on ocean optics theory to retrieve water's bio-geo-chemical parameters or inherent optical properties (IOPs) from either ocean color imagery or in situ measurements. Basically, these models are sophisticated, and hard to invert directly, look up table (LUT) technique or optimization methods are employed to retrieve the unknown parameters, e.g., chlorophyll concentration, CDOM absorption, etc. Many researches prefer to use time-consuming global optimization methods, e.g., genetic or evolutionary algorithm, etc. In this study, different optimization methods, smooth nonlinear optimization (NLP), global optimization (GO), nonsmooth optimization (NSP), are compared based on the sophisticated hyper-spectral semianalytical (SA) algorithm developed by Lee et al., retrieval accuracy and performance are evaluated. It is found that retrieval accuracy don't have much difference, the performance difference, however, is much larger, NLP works very well for the SA model. For a given model, it is better to analyze the model is linear, nonlinear or nonsmooth category problem, sometimes, convex also need to be determined, or linearize some nonsmooth problem caused by if decision, then select the corresponding category optimization methods. Initial values selection is a big issue for optimization, the simple statistical models (e.g., OC2 or OC4) are used to retrieve the unknowns as initial values.
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KePing Du, KePing Du, Ying Xi, Ying Xi, LiRan Sun, LiRan Sun, Xuegang Zhang, Xuegang Zhang, "Comparison of optimization methods for the hyperspectral semi-analytical model", Proc. SPIE 7150, Remote Sensing of Inland, Coastal, and Oceanic Waters, 71501K (19 December 2008); doi: 10.1117/12.804879; https://doi.org/10.1117/12.804879

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