Some insights of spectral optimization in ocean color inversion
Conference Name:Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2011. Conference Address: Prague, Czech republic. Time:September 21, 2011 - September 22, 2011. The Society of Photo-Optical Instrumentation Engineers (SPIE) Over the past few decades, various algorithms ha...
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ftxiamenuniv:oai:dspace.xmu.edu.cn:2288/85354 2023-05-15T18:17:59+02:00 Some insights of spectral optimization in ocean color inversion Lee, Zhongping Franz, Bryan Shang, Shaoling Dong, Qiang Arnone, Robert 商少凌 2011 http://dspace.xmu.edu.cn/handle/2288/85354 en_US eng SPIE Proceedings of SPIE - The International Society for Optical Engineering, 2011,8175 0277-786X 20114614522252 http://dspace.xmu.edu.cn/handle/2288/85354 http://dx.doi.org/10.1117/12.897875 Algorithms Backscattering Color Computer simulation Hydrophilicity Oceanography Optimization Reflection Remote sensing Sea ice Conference 2011 ftxiamenuniv 2020-07-21T11:40:37Z Conference Name:Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2011. Conference Address: Prague, Czech republic. Time:September 21, 2011 - September 22, 2011. The Society of Photo-Optical Instrumentation Engineers (SPIE) Over the past few decades, various algorithms have been developed for the retrieval of water constituents from the measurement of ocean color radiometry, and one of those approaches is spectral optimization. This approach defines an error function (or cost function) between the observed spectral remote sensing reflectance and an estimated spectral remote sensing reflectance over the range of observed wavelengths, with the latter modeled using a few variables that represent the optically active properties (such as the absorption coefficient of phytoplankton and the backscattering coefficient of particles). The values of the variables when the error function reaches a minimum are the optimized properties. The applications of this approach implicitly assume that there is only one global minimum condition, and that any local minimum (if exist) can be avoided through the numerical optimization scheme. Here, with data from numerical simulations, we show the shape of the error surface as a mechanism to visualize the solution space for the model variables. Further, using two established models as examples, we demonstrate how the solution space changes under different model assumptions as well as the impacts on the quality of the retrieved water properties. ? 2011 SPIE. Conference Object Sea ice Xiamen University Institutional Repository |
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Xiamen University Institutional Repository |
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ftxiamenuniv |
language |
English |
topic |
Algorithms Backscattering Color Computer simulation Hydrophilicity Oceanography Optimization Reflection Remote sensing Sea ice |
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Algorithms Backscattering Color Computer simulation Hydrophilicity Oceanography Optimization Reflection Remote sensing Sea ice Lee, Zhongping Franz, Bryan Shang, Shaoling Dong, Qiang Arnone, Robert 商少凌 Some insights of spectral optimization in ocean color inversion |
topic_facet |
Algorithms Backscattering Color Computer simulation Hydrophilicity Oceanography Optimization Reflection Remote sensing Sea ice |
description |
Conference Name:Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2011. Conference Address: Prague, Czech republic. Time:September 21, 2011 - September 22, 2011. The Society of Photo-Optical Instrumentation Engineers (SPIE) Over the past few decades, various algorithms have been developed for the retrieval of water constituents from the measurement of ocean color radiometry, and one of those approaches is spectral optimization. This approach defines an error function (or cost function) between the observed spectral remote sensing reflectance and an estimated spectral remote sensing reflectance over the range of observed wavelengths, with the latter modeled using a few variables that represent the optically active properties (such as the absorption coefficient of phytoplankton and the backscattering coefficient of particles). The values of the variables when the error function reaches a minimum are the optimized properties. The applications of this approach implicitly assume that there is only one global minimum condition, and that any local minimum (if exist) can be avoided through the numerical optimization scheme. Here, with data from numerical simulations, we show the shape of the error surface as a mechanism to visualize the solution space for the model variables. Further, using two established models as examples, we demonstrate how the solution space changes under different model assumptions as well as the impacts on the quality of the retrieved water properties. ? 2011 SPIE. |
format |
Conference Object |
author |
Lee, Zhongping Franz, Bryan Shang, Shaoling Dong, Qiang Arnone, Robert 商少凌 |
author_facet |
Lee, Zhongping Franz, Bryan Shang, Shaoling Dong, Qiang Arnone, Robert 商少凌 |
author_sort |
Lee, Zhongping |
title |
Some insights of spectral optimization in ocean color inversion |
title_short |
Some insights of spectral optimization in ocean color inversion |
title_full |
Some insights of spectral optimization in ocean color inversion |
title_fullStr |
Some insights of spectral optimization in ocean color inversion |
title_full_unstemmed |
Some insights of spectral optimization in ocean color inversion |
title_sort |
some insights of spectral optimization in ocean color inversion |
publisher |
SPIE |
publishDate |
2011 |
url |
http://dspace.xmu.edu.cn/handle/2288/85354 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
http://dx.doi.org/10.1117/12.897875 |
op_relation |
Proceedings of SPIE - The International Society for Optical Engineering, 2011,8175 0277-786X 20114614522252 http://dspace.xmu.edu.cn/handle/2288/85354 |
_version_ |
1766193846203449344 |