Image Processing Reference
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where Band 3 is near infrared and Band 2 is visible red reflectance. The index
returns pixel values ranging from −1 (no vegetation; low reflectance in both bands
2 and 3) to 1 (pixel dominated by actively photosynthesizing vegetation). Spatial
variance texture was also calculated from the VNIR mosaic. This operation high-
lights large changes in brightness value (or reflectance) between adjacent pixels and
has been shown to correlate well with urban versus non-urban land cover types
(Irons and Petersen 1981 ; Gong and Howarth 1990 ; Stuckens et al. 2000 ). Variance
texture is calculated using:
2
(
ij xM
)
(12.2)
V
=
n
1
where x ij is the reflectance value of pixel (i,j); n equals the number of pixels in a
moving window; and M is the mean value of the moving window (Leica Geosystems
2003 ) as defined by:
x
=
ij
(12.3)
M
n
Spatial variance texture was calculated for all three VNIR bands using both a 3 × 3
and 5 × 5 pixel moving window. This was done to capture fine-scale spatial texture
in urbanized regions as well as coarser-scale texture in undeveloped regions. The
NDVI and variance texture raster data were then each separated into low, medium,
and high data values using an unsupervised ISODATA algorithm. This approach
takes advantage of the inherent statistical clustering within each NDVI and texture
dataset, and provides a simple means of objective thresholding of the data.
Qualitative assessment of the MDM classification results indicated that significant
misclassification was present both within and between the various soil, vegetation,
and built classes. We then constructed an expert classification system similar to that
used by Stefanov et al. ( 2001b, 2003 ) to perform post-classification recoding of the
MDM classification result.
An expert classification system applies a sequence of decision rules to a set of
georeferenced datasets using Boolean logic (Vogelmann et al. 1998 ; Stefanov and
Netzband 2005 ; Stefanov et al. 2001b, 2003 ; Stuckens et al. 2000 ). This approach
allows for the introduction of a priori knowledge into the classification data space and
can significantly reduce errors of omission and commission. Figure 12.1 presents a
schematic example where the dashed rectangle indicates the hypothesized pixel
classification (“Soil and Bedrock”), hexagons are alternative decision pathways, and
solid rectangles indicate the variables being tested. If any one of the decision path-
ways (“Path”) is satisfied by the variables, the pixel will receive the hypothesized
classification value. There is no limitation on the number of variables or decision
pathways that can be combined within an expert system framework. Most image
processing software packages now include tools for constructing expert system or
decision tree classification frameworks.
The datasets combined in the expert system framework include the initial MDM
land cover classification, unsupervised classifications of the NDVI and spatial
 
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