21 December 1998 Classification of vegetation types using a high-spectral and spatial resolution hyperspectral sensor
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Abstract
In October 1997, the TRWIS III sensor was mounted into a small airplane for the purpose of collecting hyperspectral data over a variety of scenes in Ventura County, California, a largely agricultural area about 100 km from Los Angeles. The resulting hyperspectral 384 band data was analyzed using two different approaches. The first was a physically based procedure using the ratios of spectra selected based upon ground truth. Ratios between images in different bands is a way to emphasize the spectral difference and minimize the effect of illumination. The spectral bands selected were in the vicinity of the near infrared 'red edge' chlorophyll feature. The second procedure is an image processing procedure to transpose the image cube using an orthogonal subspace projection of the hyperspectral data cube. In general, a transformation over the full spectral region of the data (from approximately 400 nanometers to 2.45 micrometers) did not give results separating the tree types as well as the physically based ratio method. However, if the spectral region was restricted to 20 to 30 bands in vicinity of the red edge feature, then similar vegetation separation was achieved. In this paper, the analysis using both procedures will be discussed and compared.
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Edwin M. Winter, Edwin M. Winter, } "Classification of vegetation types using a high-spectral and spatial resolution hyperspectral sensor", Proc. SPIE 3498, Sensors, Systems, and Next-Generation Satellites II, (21 December 1998); doi: 10.1117/12.333632; https://doi.org/10.1117/12.333632
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