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This paper compares different image processing routines to identify suitable remote sensing variables for urban classifi- cation in the Marion County, Indiana, USA, using a Landsat 7 Enhanced Thematic Mapper Plus (ETM� ) image. The ETMmultispectral, panchromatic, and thermal images are used. Incorporation of spectral signature, texture, and surface temperature is examined, as well as data fusion techniques for combining a higher spatial resolution image with lower spatial resolution multispectral images. Results indicate that incorporation of texture from lower spatial resolution images or of a temperature image cannot improve classification accuracies. However, incorporation of textures derived from a higher spatial resolution panchromatic image improves the classification accuracy. In particular, use of data fusion result and texture image yields the best classifi- cation accuracy with an overall accuracy of 78 percent and a kappa index of 0.73 for eleven land use and land cover classes.
Lu et al. (Tue,) studied this question.