Continued Development of the Look-up-table (LUT) Methodology for Interpretation of Remotely Sensed Ocean Color Data and Fusion of Hyperspectral Imagery with LIDAR Bathymetry.

摘要

The overall goal of this work is to refine and validate a spectrum-matching and look-up-table (LUT) technique for rapidly inverting remotely sensed hyperspectral reflectances to extract environmental information such as water-column optical properties, bathymetry, and bottom classification. The work also seeks to combine hyperspectral imagery and LIDAR bathymetry to improve the capabilities of both. My colleagues and I are developing and evaluating a new technique for the extraction of environmental information including water-column inherent optical properties (IOPs) and shallowwater bathymetry and bottom classification from remotely-sensed hyperspectral ocean-color spectra. We address the need for rapid, automated interpretation of hyperspectral imagery. The research issues center on development and evaluation of spectrum-matching algorithms, including the generation of confidence metrics for the retrieved information.