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dc.contributor.advisorVélez-Reyes, Miguel
dc.contributor.authorCastrodad-Carrau, Alexey
dc.description.abstractHyperspectral imagery has been shown to be a powerful technology for quantitative monitoring of shallow water coastal environments. In coastal remote sensing, to estimate sea bottom properties from hyperspectral imagery, we need to remove the effects of the atmosphere and the water column from the measured spectral signature. In our work, we use a standard algorithm available from NRL to correct for atmospheric effects in hyperspectral imagery to retrieve the water leaving remote sensing reflectance, Rrs, from which the subsurface remote sensing reflectance, rrs, is retrieved. Here, we present results in the development of an algorithm combining inversion and unmixing models to retrieve bottom reflectance, water column optical properties, bathymetry, and benthic composition from subsurface remote sensing reflectance. A bio-optical model developed by Z.P. Lee in 1998 and 1999 relates Rrs and rrs to the water optical properties (OP’s), depth, and bottom reflectance. We employ an iterative algorithm to retrieve the parameters of interest. As in Goodman (2004), Lee’s original model is enhanced by adding a linear mixing model for approximating bottom composition, which is used to extract subpixel information in low spatial resolution satellite and airborne hyperspectral sensors. Results using both simulated data and AVIRIS imagery from Hawaii are presented.en_US
dc.description.abstractLas imágenes hiperespectrales han demostrado ser una tecnología de gran alcance para el monitoreo cuantitativo de los ambientes costeros en aguas no profundas. Para estimar las características del fondo marino usando imágenes hiperespectrales, se necesita remover los efectos de la atmósfera y de la columna del agua de la firma espectral medida. En nuestro trabajo, utilizamos un algoritmo estándar de NRL para corregir por los efectos atmosféricos en imágenes hiperespectrales para recuperar la reflectancia de percepción remota en la superficie del agua, Rrs, y de la cual se puede obtener la reflectancia subsuperficial de percepción remota del agua, rrs. Aquí, presentamos resultados en el desarrollo de un algoritmo que combina inversión y separación espectral aproximando la reflectancia subsuperficial de percepción remota para extraer características ópticas de la columna de agua, batimetría y composición béntica. Un modelo bio-óptico desarrollado por Z.P. Lee en 1998 y 1999 relaciona la Rrs y rrs con las propiedades ópticas (P.O.s) de la columna de agua y del fondo es utilizado. Empleamos un algoritmo iterativo para obtener los parámetros de interés. De manera similar a Goodman (2004), el modelo de Lee es mejorado agregando un modelo de mezclado espectral lineal para aproximar la composición béntica del fondo marino, el cual es usado para extraer información a nivel de subpixel y compensar por la baja resolución espacial en sensores hiperespectrales en satélites y aviones. Se presentan resultados usando datos simulados e imágenes de AVIRIS sobre Hawaii.en_US
dc.description.sponsorshipSupported in part by CenSSIS, the Center for Subsurface Sensing and Imaging Systems, under the Engineering Research Centers Program of the National Science Foundation (Award Number EEC-9986821).en_US
dc.titleAn algorithm for retrieval of optical properties, bathymetry and benthic cover in shallow waters from hyperspectral imageryen_US
dc.rights.licenseAll rights reserveden_US
dc.rights.holder(c) 2005 Alexey Castrodad-Carrauen_US
dc.contributor.committeeHunt, Shawn D.
dc.contributor.committeeGoodman, James
dc.contributor.committeeRivera, Wilson
dc.contributor.representativeGilbes, Fernando Engineeringen_US
dc.contributor.collegeCollege of Engineeringen_US
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US

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