Abstract
Abstract A two-stage classification procedure has been applied to extract land use in a rural-urban fringe environment from SPOT High Resolution Visible (HRV) multi-spectral data. In this procedure, the SPOT HRV data were first classified into twelve land-cover types using a supervised maximum-likelihood classification (MLC). In the second stage, cover frequencies were extracted by moving a pixel window over the land-cover map obtained at the first stage. These cover frequencies were then employed in the classification of 14 land-use classes using a supervised minimum-city-block classifier. Results obtained with the cover-frequency method have been compared with those obtained using the conventional MLC approach. The overall accuracy measured by the Kappa coefficient was 0·462 for the MLC method; it was significantly improved to 0·663 with the cover-frequency method.
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