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13 June 2014 Subsurface unmixing for benthic habitat mapping using hyperspectral imagery and lidar-derived bathymetry
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Abstract
Mapping of benthic habitats from hyperspectral imagery can be achieved by integrating bio-optical models with common techniques for hyperspectral image processing, such as spectral unmixing. Several algorithms have been described in the literature to compensate or remove the effects of the water column and extract information about the benthic habitat characteristics utilizing only measured hyperspectral imagery as input. More recently, the increasing availability of lidar-derived bathymetry information offers the possibility to incorporate this data into existing algorithms, thereby reducing the number of unknowns in the problem, for the improved retrieval of benthic habitat properties. This study demonstrates how bathymetry information improves the mapping of benthic habitats using two algorithms that combine bio-optical models with linear spectral unmixing. Hyperspectral data, both simulated and measured, in-situ spectral data, and lidar-derived bathymetry data are used for the analysis. The simulated data is used to study the capabilities of the selected algorithm to improve estimates of benthic habitat composition by combining bathymetry data with the hyperspectral imagery. Hyperspectral images captured over Emique in Puerto Rico using an AISA Eagle sensor is used to further test the algorithms using real data. Results from analyzing this imagery demonstrate increased agreement between algorithm output and existing habitat maps and ground truth when bathymetry data is used jointly with hyperspectral imagery.
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Maria C. Torres-Madronero, Miguel Velez-Reyes, and James A. Goodman "Subsurface unmixing for benthic habitat mapping using hyperspectral imagery and lidar-derived bathymetry", Proc. SPIE 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, 90880M (13 June 2014); https://doi.org/10.1117/12.2053491
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