Adaptive Estimation Of Water Depth Using Multispectral Remote Sensing

Andrew B Martinez; Richard T Joy; Maria K Kalcic; Greg Terrie; Stephen P Haimbach

doi:10.1117/12.945727

12 August 1988 Adaptive Estimation Of Water Depth Using Multispectral Remote Sensing

Andrew B Martinez, Richard T Joy, Maria K Kalcic, Greg Terrie, Stephen P Haimbach

Proceedings Volume 0925, Ocean Optics IX; (1988) https://doi.org/10.1117/12.945727
Event: 1988 Technical Symposium on Optics, Electro-Optics, and Sensors, 1988, Orlando, FL, United States

Abstract

An adaptive procedure for the estimation of water depth from passive multispectral scanner data is presented. While many authors have proposed nonadaptive, model-based estimators, most are computationally intensive and require accurate estimates of model parameters (directly or through regression) and bottom classification. By using an adaptive estimator based on the LMS algorithm, computational overhead is greatly reduced. Parameter estimation is unnecessary due to the inherent robustness of the algorithm to changes in ocean environment. This results in significant improvements in performance. Examples are given illustrating these points, and comparisons are made of methods based on adaptive estimation and on regression. Trade-offs between rate or convergence and residual error are discussed.

Citation Download Citation

Andrew B Martinez, Richard T Joy, Maria K Kalcic, Greg Terrie, and Stephen P Haimbach "Adaptive Estimation Of Water Depth Using Multispectral Remote Sensing", Proc. SPIE 0925, Ocean Optics IX, (12 August 1988); https://doi.org/10.1117/12.945727

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