from a Landsat TM image. The index-derived map was
further filtered using the NDVI to remove the vegetation
noise, as the vegetation information was mixed with the
extracted built-up lands. Guindon et al. (2004) mapped
urban land with a combination of spectral and spatial information.
This started with an unsupervised classification and
a segment-based classification, respectively. The two classifications
were then merged using rules to generate a final
product with enhanced land-use classes and accuracy. More
recently, Xian and Crane (2005) measured urban land expansion
of the Tampa Bay watershed of Florida by using a
regression tree algorithm to map urban impervious surfaces
and an unsupervised classification to reveal related landcover
classes which achieved an accuracy greater than
85 percent.
A new technique is proposed in this paper for the
extraction of urban build-up land information. The extraction
is mainly based on a new image derived from three
thematic indices, Soil Adjusted Vegetation Index (SAVI),
Modified Normalized Difference Water Index (MNDWI), and
Normalized Difference Built-up Index (NDBI). The technique
is demonstrated through the extraction of urban built-up
lands of Quanzhou and Fuzhou Cities in southeastern China
from Landsat TM/ETM images.