Analysis of Hyperspectral Image Data for the CoBOP Experiments
CORNELL UNIV ITHACA NY
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LONG-TERM GOALS. Our long-term goal is the development of spectral analysis tools that fully exploit the information content in hyperspectral image data, particularly as it applies to remote sensing of ocean color. OBJECTIVES. Our objectives are to develop specific algorithms and procedures to classify water types, differentiate among different bottom types and extract bathymetry from passive hyperspectral image data. When the water type and bottom reflectance are uniform over the study area, bathymetric mapping with passive remote sensing data is a relatively straightforward, one-variable problem and requires a minimum of field data. It is even possible to extract a relative water attenuation coefficient from spectral image data. The problem quickly becomes much more complex when the water type and bottom type vary over the scene. In that case, the depth cannot be determined without simultaneously resolving the bottom reflectance and basic optical water properties. Bathymetric mapping is thus an inherently multivariate problem requiring at least several spectral bands. We expect that effective use of hyperspectral image data will lead to significant improvements in the accuracy and detail of the results.
- Physical and Dynamic Oceanography