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Hyperspectral image assessment of oil-contaminated wetland

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In exploring the nature of hyperspectral data, this study has focused on one of its most challenging applications--oil spill detection--in order to uncover the potential limits of such data. The classification performance of conventional techniques can be improved by testing the accuracy of the existing classifiers using a ground data image as a reference. Moreover, a created prototype demonstrates how hyperspectral data can supplement information on environmental deterioration due to oil pollution, specifically the Patuxent River wetland at the Chesapeake Bay in Maryland. The data allow an assessment of the current state of wetland losses and habitat changes due to oil pollution of local waters and associated wetlands. Airborne Imaging Spectro-Radiometer for Applications (AISA) hyperspectral imagery was used for this study and the results were derived using the Environment for Visualizing Images (ENVI) software. The use of different classifiers showed low accuracy and class overlap for many classes. Therefore, a ground data image was created using maximum likelihood (ML) classification to compare the results of several classifiers and to assess the accuracy of each technique. Using 2D scatter plots for selecting regions of interest yielded more accurate results than digitizing polygons for training samples. It allowed precise identification of grass stress and soil damaged by polluted water.
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Document Type: Research Article

Affiliations: George Washington University Department of Electrical and Computer Engineering Washington DC USA

Publication date: February 1, 2005

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