Supervised classification. We adopted the maximum likelihood
classifier (MLC) to classify each of the rural subset and the nonurban
pixels within the urban subset. A separate training procedure
was conducted for each subset. The training samples were
carefully selected for each class described in Section 2.3 through
an iterative procedure based on our examination of their representativeness
and separability between training classes, as well
as results in thematic accuracy assessment. Due to the significant
spectral confusion observed between wetland and forest classes,
we grouped the wetland class together into the forest category
for the supervised classification. We included the Normalized
Difference Vegetation Index (NDVI) image as additional layer for
maximum likelihood classification of each subset. Also the three
shade-normalized fraction images were included as input layers
in the supervise classification for the urban subset. Therefore, 10
“bands” were actually used for the urban subset (i.e., 6 reflectance
bands, 3 fraction bands, and NDVI), and 7 “bands” (i.e., 6
reflectance bands and NDVI) were included for the rural subset.
Finally, we combined the classified outcomes from the two
subsets and the previously extracted urban classes to produce a
complete map.