DocumentCode :
2102602
Title :
Automatic land-cover classification of a barrier island in the Virginia Coast Reserve using HYMAP imagery: an intercomparison of methods
Author :
Bachmann, Charles M. ; Donato, Timothy F. ; Dubois, Kevin ; Fusina, Robert A. ; Bettenhausen, Michael ; Porter, John H. ; Truitt, Barry R.
Author_Institution :
Remote Sensing Div., Naval Res. Lab., Washington, DC, USA
Volume :
2
fYear :
2001
fDate :
2001
Firstpage :
637
Abstract :
Automatic land-cover maps were developed from HYMAP hyperspectral imagery acquired May 8, 2000 over Smith Island, VA in the Virginia Coast Reserve. Both unsupervised and supervised classification approaches were used to create these products. Ground surveys made by us in late October and early December, 2000 provided ground truth data for various land-cover types. We used GPS data from these surveys to extract spectral end-members used in supervised land-cover classification models. Both approaches to the classification problem produced consistent results for some categories such as Spartina alterniflora, although there were differences for other categories
Keywords :
geophysical signal processing; geophysical techniques; image classification; terrain mapping; vegetation mapping; AD 2000 05 08; HYMAP; IR; Smith Island; Spartina alterniflora; USA; United States; Virginia; Virginia Coast Reserve; automatic land cover classification; barrier island; coast; geophysical measurement technique; hyperspectral imagery; hyperspectral remote sensing; image classification; infrared; land surface; multispectral remote sensing; remote sensing; vegetation mapping; visible; Atmospheric modeling; Cities and towns; Data mining; High performance computing; Hyperspectral imaging; Hyperspectral sensors; Laboratories; Layout; Remote sensing; Vegetation mapping;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-7031-7
Type :
conf
DOI :
10.1109/IGARSS.2001.976576
Filename :
976576
Link To Document :
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