ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS", Bangkok, May 23-25, 2001
341
SAR filtered data for classification (Lee et al., 1994), which is
based on the complex Wishart distribution of the multilook
covariance matrix. Based on the maximization of the Wishart
density function, a simple measure is derived
where the superscript * denotes the complex conjugate, ¡s
the m th class, C m [j) is the feature covariance matrix from
class tn , which is computed from a training area for each class,
Z(j) is the covariance matrix for the j th frequency band, J is
the number of bands, and T r ¡s the trace of a matrix. Fig. 3
shows the image after classification of SAR data.
5. CONSTRUCTION 3D OF ELEVATED OBJECTS
In an extraction method of elevated objects, to judge the size and
shape of objects by providing an impression of convexity and
concavity, the shadows of the objects are very helpful. Elevated
objects shadow and object pixels are directly connected in the
SAR images. After filtering and classification SAR data, the sets of
object and shadow pixels are searched along the range direction
of the SAR image. A simple trigonometric models (Fig. 5) and
knowledge of the SAR geometry are used to get the relative
heights of elevated objects (Kirscht and Carsten, 1998). Fig. 6
and Fig. 7 show the constructed 3D with contour map and VRML
image.
The object height ¡ s estimated as
'obj
= h
2i_
D:
yiobj = y3
V
J h °bj ^
/
v
H
y\obj ph 0 bj + .Vj h 0 bj
where Hp is the flight height.
(8)
(9)
(10)
Fig. 5 Construction of Elevated Objects
Fig. 7 The resulting 3D height map view (VRML)
6. CONCLUSION
In this paper we have presented the elevation extraction in an
urban area using airborne SAR polarimetric data with a simple
trigonometric model. In the processing of constructing 3D image
from the airborne SAR, speckle noise filtering, segmentation and
classification of SAR image Data are applied. Finally a contour
map and VRML image were given using the result of the
