Hyperspectral image unmixing over benthic habitats

Benthic habitats are the different bottom environments as defined by distinct physical, geochemical, and biological characteristics. Hyperspectral remote sensing has great potential to map and monitor the complex dynamics associated with estuarine and nearshore benthic habitats. However, utilizing hyperspectral unmixing to map these areas requires compensating for variable bathymetry and water optical properties. In this paper, we compare two methods to unmix hyperspectral imagery in estuarine and nearshore benthic habitats. The first method is a two-stage method where bathymetry and optical properties are first estimated using Lee's inversion model and linear unmixing is then performed using variable endmembers derived from propagating bottom spectral signatures to the surface using the estimated bathymetry and optical properties. In the second approach, a nonlinear optimization approach is used to simulatenously retrieve abundances, optical properties, and bathymetry. Preliminary results are presented using AVIRIS data from Kaneohe Bay, Hawaii. SHOALS data from the area is used to evaluate the accuracy of the retrieved bathymetry and comparisons between abundance estimates for sand, algae and coral are performed. These results show the potential of the nonlinear approach to provide better estimates of bottom coverage but at a significantly higher computational price. The experimental work also points to the need for a well characterized site to use for unmixing algorithms testing and validation.

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