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Automatic classification of land cover on Smith Island, VA, using HyMAP imagery

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9 Author(s)
Bachmann, C.M. ; Remote Sensing Div., Naval Res. Lab., Washington, DC, USA ; Donato, T.F. ; Lamela, G.M. ; Rhea, W.J.
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Automatic land cover classification maps were developed from Airborne Hyperspectral Scanner (HyMAP) imagery acquired May 8, 2000 over Smith Island, VA, a barrier island in the Virginia Coast Reserve. Both unsupervised and supervised classification approaches were used to create these products to evaluate relative merits and to develop models that would be useful to natural resource managers at higher spatial resolution than has been available previously. Ground surveys made by us in late October and early December 2000 and again in May, August, and October 2001 and May 2002 provided ground truth data for 20 land cover types. Locations of pure land cover types recorded with global positioning system (GPS) data from these surveys were used to extract spectral end-members for training and testing supervised land cover classification models. Unsupervised exploratory models were also developed using spatial-spectral windows and projection pursuit (PP), a class of algorithms suitable for extracting multimodal views of the data. PP projections were clustered by ISODATA to produce an unsupervised classification. Supervised models, which relied on the GPS data, used only spectral inputs because for some categories in particular areas, labeled data consisted of isolated single-pixel waypoints. Both approaches to the classification problem produced consistent results for some categories such as Spartina alterniflora, although there were differences for other categories. Initial models for supervised classification based on 112 HyMAP spectra, labeled in ground surveys, obtained reasonably consistent results for many of the dominant categories, with a few exceptions.

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Geoscience and Remote Sensing, IEEE Transactions on  (Volume:40 ,  Issue: 10 )