Unmixing of the seabed for benthic habitat mapping in shallow coastal waters is a difficult problem due to the confounding effects of space variant bathymetry and water optical properties, which result in signatures for the same habitat classes to look different at the water surface across the image. This paper discusses different approaches to modify the linear unmixing approach to account for variable water optical properties and bathymetry and their implementation in the Hyperspectral Coastal Image Analysis Toolbox (HyCIAT). This toolbox allows the processing of hyperspectral imagery of shallow coastal areas to estimate water column optical properties, bathymetry, and perform unmixing for bottom composition. HyCIAT has been developed as part of the UPRM Hyperspectral Solutionware project to develop software tools for hyperspectral image processing. The tool has been developed under the MATLABTM environment and it includes a series of algorithms developed by UPRM researches under a graphical user interface that facilitates its use by the remote sensing community. The paper describes algorithms implemented in the toolbox, gives an overview of the graphical user interface, and presents results of its applications to AVIRIS and AISA hyperspectral imagery collected over Kaneohe Bay in Hawaii and over Southwestern Puerto Rico, respectively.
[1]
Miguel Vélez-Reyes,et al.
Subsurface unmixing with application to underwater classification
,
2007,
SPIE Remote Sensing.
[2]
C. Mobley,et al.
Hyperspectral remote sensing for shallow waters. I. A semianalytical model.
,
1998,
Applied optics.
[3]
Peter Gege,et al.
A TOOL FOR INVERSE MODELING OF SPECTRAL MEASUREMENTS IN DEEP AND SHALLOW WATERS
,
2006
.
[4]
C. Mobley,et al.
Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization.
,
1999,
Applied optics.
[5]
Curtis D. Mobley,et al.
Hydrolight 3.0 User's Guide.
,
1995
.
[6]
James A. Goodman,et al.
Classification of benthic composition in a coral reef environment using spectral unmixing
,
2007
.
[7]
Miguel Vélez-Reyes,et al.
New developments and application of the UPRM MATLAB hyperspectral image analysis toolbox
,
2007,
SPIE Defense + Commercial Sensing.
[8]
Marcos J. Montes,et al.
Tafkaa atmospheric correction of hyperspectral data
,
2004,
SPIE Optics + Photonics.
[9]
Alexey Castrodad-Carrau,et al.
An algorithm for retrieval of optical properties, bathymetry and benthic cover in shallow waters from hyperspectral imagery
,
2005
.