EARSeL eProceedings Vol. 3, No. 3, 363-371, 2004

Abstract
Multispectral images have been transformed into Tasseled Cap features to characterize the wetland properties for mapping purpose. The texture derivatives were applied to the brightness, greenness, and wetness using three texture measures based on the grey-level co-occurrence matrix method. In this study, the data-driven window size over which texture measures are derived will be determined based on the experimental semivariograms instead of a trial-and-error method. Eight combinations of window sizes have been analyzed to evaluate the benefit of the proposed strategy. A supervised classification based on the maximum likelihood algorithm was applied to the three Tasseled Cap features and to their combination with each texture inputs under different window sizes. Classification accuracy is measured by the overall accuracy for the whole set of classification. User's accuracy and kappa coefficient are used to estimate individual class accuracy. The combination of multiple window sizes from the Tasseled Cap features to derive texture measures for classification purposes is proposed according to the semivariograms. The overall accuracy of the spectral-textural classification shows a 95.5% accuracy, higher than the multispectral classification alone. For the purpose of wetland mapping of the study site, the proposed combinations of multiple window sizes provide wetland class 92.6% accuracy higher than randomly selected identical window sizes.

ISSN 1729-3782