Based on a data dimension compression technique

this paper studies the principles and method of remote sensing information extraction for urban built up land. With the detailed analysis of urban land use types

the study selects three indices

i.e. Normalized Difference Built up Index(NDBI)

Modified Normalized Difference Water Index (MNDWI) and Soil Adjusted Vegetation Index(SAVI)

to represent three major urban land use/cover classes

including built up land

water body

and vegetation. This reduced 7 multispectral bands of a Landsat 7 ETM+ subscene of Fuzhou city to three index bands generated from the original multispectral bands and thus dramatically decreased band correlation

data redundancy and spectral confusion between different land use/cover classes. The three index bands are then used to compose a new image. A maximum likelihood based supervised classification was carried out on the new three band image and the built up land is finally extracted by masking out non built up land classes. The extraction result achieves a 91.2% overall accuracy. Therefore

the method is an effective one for the remote sensing information extraction of urban land use.