Hyperspectral analysis, the support vector machine, and land and benthic habitats

Abstract

Two different areas of current research in hyperspectral remote sensing are addressed: (1) supervised learning using all the hyperspectral bands as based on the recently introduced method called the support vector machine. (2) Hyperspectral remote sensing in shallow water to retrieve benthic properties including depth and albedo on the sea floor. The support vector technique is applied to land agricultural scenes acquired by AVIRIS with up to 16 classes, and is shown to give improved results over a number of methods all applied to the same scene. Hyperspectral remote sensing in shallow water is demonstrated on an AVIRIS scene acquired in Kaneohe Bay Hawaii, where reasonable depths and bottom albedos are retrieved. The method is based on physical modeling of the propagation of light though the atmosphere and physical modeling of the propagation of light through the water column above the sea floor. The results for shallow water remote sensing are extended by a physically realistic simulation using AVIRIS at-sensor data to model cases of spatial resolution and signal to noise ratios that might exist for a hyperspectral sensor in geostationary orbit.