Extraction of urban built-up land features from landsat imagery using a thematic-oriented index combination technique

This paper proposes a technique to extract urban built-up land features from Lundsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery taking two cities in southeastern China as examples. The study selected three indices, 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 classes, built-up land, open water body, and vegetation, respectively. Consequently, the seven bands of an original Landsat image were reduced into three thematic-oriented bonds derived from above indices. The three new bands were then combined to compose a new image. This considerably reduced data correlation and redundancy between original multispectral bands, and thus significantly avoided the spectral confusion of the above three land-use classes. As a result, the spectral signatures of the three urban land-use classes are more distinguishable in the new composite image than in the original seven-band image as the spectral clusters of the classes are well separated. Through a supervised classification, a principal components analysis, or a logic calculation on the new image, the urban built-up lands were finally extracted with overall accuracy ranging from 91.5 to 98.5 percent. Therefore, the technique is effective and reliable. In addition, the advantages of SAVI over NDVI and MNDWI over NDWI in the urban study are also discussed in this paper.