Hyperspectral aquatic radiative transfer modeling using cluster computing
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Estimating aquatic biophysical quantities from remote sensor imagery is an inverse problem, which is still unsolved. In this research, forward-modeling was considered to ultimately address the inverse problem of hydrologic optics. Above-surface hyperspectral remote-sensing reflectance can be inverted to yield bottom depth and various inherent and bottom optical properties. Retrievals can be obtained by using an artificial neural network (ANN) or similar algorithm. However, due to the paucity of measured oceanic field/organic training and testing sets, synthetic data generation is usually necessary to train such an algorithm in this application domain. Such modeled data sets, which can be quite large, are typically computationally expensive and time-consuming to generate. To accomplish this efficiently, a cluster computing method was developed to produce a forward-modeled, hyperspectral bio-optical database, based on radiative transfer (RT) theory. This method enables multiple instances of the Hydrolight RT model to be run in parallel. The Hydrolight runs are independent of one another, making them good candidates for parallelization, thus reducing the time required to generate the synthetic training/testing data. The resultant inputoutput pairs in the forward-modeled database can be used to develop remote-sensing inverse algorithms. Bathymetry and water-column and bottom optical models can thus be developed while minimizing the need for in situ data, which is useful in an operational environment. Simulated remote-sensing reflectance pseudodata are of utility in hydrologic inversion since adequate field data for model development are often difficult or impractical to acquire.
American Society for Photogrammetry and Remote Sensing - Annual Conference of the American Society for Photogrammetry and Remote Sensing 2006: Prospecting for Geospatial Information Integration
author list (cited authors)
Filippi, A. M., Bhaduri, B. L., Naughton, T. J., King, A. L., & Scott, S. L
