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1 October 1999 Use of hyperspectral imagery for material classification in outdoor scenes
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We present an efficient segmentation algorithm to discriminate between different materials, such as painted metal, vegetation, and soils, using hyperspectral imagery. Most previously attempted segmentation techniques have used a relatively small number of infrared frequency bands that use thermal emission instead of solar radiation. This motivated the use of hyperspectral (or multispectral) imagery for segmentation purposes taken at the visible and near infrared bands with high spectral dimensionality. We propose a segmentation algorithm that uses either a pattern- matching technique using the selected band regions or a principal component analysis method. Segmentation results are provided using several hyperspectral images. We also present a band-selection process based on either pairwise performance evaluation or a band-thickening method to select the particular band regions that contain important band- value information for segmentation. A hyperspectral data set that contains a number of spectral band-value curves collected from eleven hyperspectral images is used as an evaluation data set for the band-selection process.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Heesung Kwon, Sandor Z. Der, Nasser M. Nasrabadi, and Hyeonjoon Moon "Use of hyperspectral imagery for material classification in outdoor scenes", Proc. SPIE 3804, Algorithms, Devices, and Systems for Optical Information Processing III, (1 October 1999);

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