Development of the Hyperspectral Coastal Image Analysis Toolbox (HyCIAT) with a focus on hyperspectral and lidar data fusion

The Hyperspectral Coastal Image Analysis Toolbox integrates algorithms for the estimation of water optical properties, bathymetry and fractional abundances of bottom composition in a graphical interface using Matlab to perform analysis of hyperspectral images of shallow waters. The primary algorithms included in the toolbox were previously developed by students and faculty at LARSIP at University of Puerto Rico at Mayaguez. Work was also performed to add new capabilities to these existing algorithms by incorporating the capacity to fuse lidar data into the hyperspectral processing. The HyCIAT algorithms are fundamentally based on the Lee et al. [1] semi-analytical inversion model combined with linear unmixing techniques developed by Goodman [2] and Castrodad [3]. The Lee algorithm is one of the more commonly used models for the estimation of water optical properties and bathymetry from passive hyperspectral imagery. Goodman integrated the Lee model with unmixing algorithms (LIGU) to first independently derive estimates of water properties and bathymetry, and then derive the habitat composition. Castrodad similarly combined the Lee model with an unmixing algorithm (CIUB), but this derives the water properties, bathymetry and habitat composition simultaneously. Both these techniques are included in HyCIAT as well as new capabilities for both models that allow lidar bathymetry to be used as input. This work presents the HyCIAT toolbox, and evaluates model performance using both simulated data and actual airborne hyperspectral imagery. Results indicate that accuracy in parameter retrieval is increased when the lidar data is included in the models.