Examining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case
Classification accuracies were shown to be influenced by image spatial resolution, window size used in texture extraction and differences in spatial structure within and between categories. The more heterogeneous are the land use/cover units and the more fragmented are the landscapes, the finer the resolution required. Texture was more effective for improving the classification accuracy of land use classes at finer resolution levels. For spectrally homogeneous classes, a small window is preferable. But for spectrally heterogeneous classes, a large window size is required.
Document Type: Research Article
Affiliations: 1: Department of Geography San Diego State University San Diego CA 92182-4493 USA 2: Department of Environmental Science, Policy & Management University of California at Berkeley Berkeley CA 94720 USA
Publication date: 2004-06-01