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Proceedings Paper

Comparison of optimization methods for the hyperspectral semi-analytical model
Author(s): KePing Du; Ying Xi; LiRan Sun; Xuegang Zhang
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Paper Abstract

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.

Paper Details

Date Published: 19 December 2008
PDF: 9 pages
Proc. SPIE 7150, Remote Sensing of Inland, Coastal, and Oceanic Waters, 71501K (19 December 2008); doi: 10.1117/12.804879
Show Author Affiliations
KePing Du, Beijing Normal Univ. (China)
State Key Lab. of Remote Sensing Science (China)
Beijing Key Lab. for Remote Sensing of Environment and Digital Cities (China)
Ying Xi, Beijing Normal Univ. (China)
State Key Lab. of Remote Sensing Science (China)
Beijing Key Lab. for Remote Sensing of Environment and Digital Cities (China)
LiRan Sun, Beijing Normal Univ. (China)
State Key Lab. of Remote Sensing Science (China)
Beijing Key Lab. for Remote Sensing of Environment and Digital Cities (China)
Xuegang Zhang, Beijing Normal Univ. (China)
State Key Lab. of Remote Sensing Science (China)
Beijing Key Lab. for Remote Sensing of Environment and Digital Cities (China)


Published in SPIE Proceedings Vol. 7150:
Remote Sensing of Inland, Coastal, and Oceanic Waters
Robert J. Frouin; Serge Andrefouet; Hiroshi Kawamura; Mervyn J. Lynch; Delu Pan; Trevor Platt, Editor(s)

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