27 September 2006 Hyperspectral signatures of an eastern North American temperate forest
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We describe a new approach to unsupervised classification that automatically finds dense parts of the hyperspectral data cloud. These dense regions are identified as the cluster centers required for unsupervised classification. The approach is tested using AVIRIS hyperspectral imagery from central Texas that has spectrally well separated land covers. The algorithm is then applied to the more stressing case of separating coniferous and deciduous forests in eastern Virginia. We find that the major spectral difference is brighter reflectance in the NIR plateau for deciduous forests compared to adjacent coniferous stands. This difference is sufficient to distinguish the forest types, and is confirmed by comparison to ground truth information.
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John Cipar, John Cipar, Ronald Lockwood, Ronald Lockwood, Thomas Cooley, Thomas Cooley, } "Hyperspectral signatures of an eastern North American temperate forest", Proc. SPIE 6298, Remote Sensing and Modeling of Ecosystems for Sustainability III, 629802 (27 September 2006); doi: 10.1117/12.679056; https://doi.org/10.1117/12.679056

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