A comparison of inter-analyst differences in the classification of a Landsat ETM+ scene in South-Central Virginia.


This study examined inter-analyst classification variability based on training site signature selection only for six classifications from a 10 km2 Landsat ETM+ image centered over a highly heterogeneous area in south-central Virginia. Six analysts classified the image at the 30 m ETM+ resolution varying only location and number of training sites. These classifications were then degraded to coarser resolutions, assigning the dominant land cover to the new cell resolution. Analyst-to-analyst differences were noted at the varying scales as well as overall accuracy assessment results compared to a land cover map digitized from an August 3, 2002 Ikonos panchromatic image. Results indicated that highest accuracies for all six analysts occurred at the 450 m scale resolution (i.e. 20.25 ha), corresponding to a 364 m2 (13.25 ha) average patch size for all classes. Spectral separability for training site data was analyzed for each of the six classifications. These tests included a Euclidean Distance, Transformed Divergence, and Jeffries-Matusita Distance evaluation. All spectral separability tests pointed to areas of class confusion within each interpretation, but prediction of an analyst accuracy ranking based on separability amongst all six interpretations was not achieved. This study was initiated to examine land cover variability between analysts as it applies to the process of creating leaf area index (LAI) surface maps used in the validation of medium resolution LAI products.


Natural Resources and the Environment

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Proceedings of the Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Reno, NV


ASPRS 2006 Annual Conference

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