EVALUATION OF THE GREY-LEVEL CO-OCCURRENCE MATRIX METHOD FOR LAND-COVER CLASSIFICATION USING SPOT IMAGERY

Nine cover types have been classified using a textural/ spectral approach. The texture analysis is based on the grey-level cooccurrence matrix method. Texture features are created from a SPOT near-infrared image using four texture indices, seven window sizes, and two quantization levels. A supervised classification based on the maximum-likelihood algorithm is applied to the three SPOT multispectral bands combined with each texture image individually and to the three bands combined with all four texture images. Classification accuracy is measured by kappa coefficients calculated from confusion matrices. A factor analysis, based on principal components, is performed to evaluate the contribution to the classification accuracy of each variable involved in the creation of the texture features. The addition of texture features provides a significant improvement in the classification accuracy of each cover type when compared with the results obtained from the multispectral analysis alone. The window size accounts for 90% of the classification variability, 7% is explained by the statistics used as texture measures, and only 3% by the quantization level. There is a window size that optimizes the discrimination of each cover type. © 1990 IEEE