Through-Wall Imaging With UWB Radar System - KEMT FEI TUKE

4.3 Highlighting of a Building Contours 68
theoretical approach was chosen for correcting the spread losses. Because most of
the objects that are scanned, including walls, have flat surfaces, the spread losses
could be expressed by reciprocal proportion [156]:
T2
a2 = a1
T1
(4.3.2)
where a1 is the wave magnitude at position T1 and a2 is the wave magnitude at
position T2
Compensation of the wave attenuation and losses should improve the magnitudes
level of all scanned objects according to their reflection properties. However,
it has to be done carefully because the small magnitudes from far objects behind
the wall are increased including the noise. In critical scenarios the noise and
clutters from far positions could be raised into the target magnitude level.
After the preprocessed and calibrated dataset BP C is migrated and attenuation
and spread looses described above are compensated, the final image Iw(X, Z) is
obtained:
Iw(xT , zT ) = 1
N
N�
awa2w1w2BP Cn (X, k = (T OAT X−T + T OAT −RX)). (4.3.3)
n=1
4.3.2 Wall Highlighting using Hough Transform
The walls that represent the building contours are mostly distorted by clutters
and noise on the processed image Iw(xT , zT ). We are looking for big straight walls,
with good reflections, that represent building contours. To highlight such walls we
propose the following image processing steps: The image Iw(xT , zT ) is transformed
to the gray-level. The edges are found with the edge detector so the picture is
converted to the binary image IwDE(xT , zT ). The Hough transform [129, 52] is
applied. The peaks in Hough space are filtered according to the wall direction
assumptions and lines which represent the investigated walls are drawn back to
the Iw(xT , zT ) image.
Canny edge detector [25] is used to detect edges, as it is very often used for
detection of lines in combination with HT in SAR images obtained by an aircraft
[33] or for ground penetrating radar SAR scans [56].
The HT is used to find the lines in the image:
IHT = HT (IwDE(xT , zT )). (4.3.4)
HT is a feature extraction technique used in image analysis, computer vision, and
digital image processing [129, 52]. HT is mostly used for finding straight lines
(or certain class of shapes) hidden in larger amounts of other data. The main
advantage of the HT technique is that it is tolerant to gaps in feature boundary