Physical Sciences and Mathematics Commons^™

Improving The Retrieval Of Water Inherent Optical Properties In Noisy Hyperspectral Data Through Statistical Modeling, David B. Gillis, Jeffrey H. Bowles, Wesley J. Moses

Wesley Moses

The use of the Mahalanobis distance in a lookup table approach to retrieval of in-water Inherent Optical Properties (IOPs) led to significant improvements in the accuracy of the retrieved IOPs, as high as 50% in some cases, with an average improvement of 20% over a wide range of case II waters. Previous studies have shown that inherent noise in hyperspectral data can cause significant errors in the retrieved IOPs. For LUT-based retrievals that rely on spectrum matching, the particular metric used for spectral comparisons has a significant effect on the accuracy of the results, especially in the presence of noise …