Goodman James A, Lee ZhongPing, Ustin Susan L
Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems, University of Puerto Rico at Mayagüez, P.O. Box 9048, Mayagüez, Puerto Rico.
Appl Opt. 2008 Oct 1;47(28):F1-F11. doi: 10.1364/ao.47.0000f1.
Hyperspectral instruments provide the spectral detail necessary for extracting multiple layers of information from inherently complex coastal environments. We evaluate the performance of a semi-analytical optimization model for deriving bathymetry, benthic reflectance, and water optical properties using hyperspectral AVIRIS imagery of Kaneohe Bay, Hawaii. We examine the relative impacts on model performance using two different atmospheric correction algorithms and two different methods for reducing the effects of sunglint. We also examine the impact of varying view and illumination geometry, changing the default bottom reflectance, and using a kernel processing scheme to normalize water properties over small areas. Results indicate robust model performance for most model formulations, with the most significant impact on model output being generated by differences in the atmospheric and deglint algorithms used for preprocessing.
高光谱仪器提供了从本质上复杂的沿海环境中提取多层信息所需的光谱细节。我们使用夏威夷卡内奥赫湾的高光谱航空可见光/红外成像光谱仪（AVIRIS）图像，评估了一种用于推导水深、海底反射率和水体光学特性的半分析优化模型的性能。我们研究了使用两种不同的大气校正算法和两种不同的减少镜面反射影响的方法对模型性能的相对影响。我们还研究了不同的视角和光照几何条件、改变默认的海底反射率以及使用核处理方案对小区域水体特性进行归一化的影响。结果表明，大多数模型公式的模型性能稳健，对模型输出影响最大的是预处理中使用的大气和去镜面反射算法的差异。
