Urban Classification Using Full Spectral Information of Landsat ETM+ Imagery in Marion County, Indiana

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Abstract
This paper compares different image processing routines to identify suitable remote sensing variables for urban classification in the Marion County, Indiana, USA, using a Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image. The ETM+ multispectral, 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 classification accuracy with an overall accuracy of 78 percent and a kappa index of 0.73 for eleven land use and land cover classes.