The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008
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Figure 5. 3D model and contour plots generated by SURFER
The SURFER menu offered a variety of options for
manipulating data. The options ranged from minor editing of
graphics such as smoothing relief to the choice of mathematical
procedures for determining volume, area and elevation.
Negative surface area of 3D model shown in figure was
assumed as the bowl shaped cavity in the pavement surface
(area of pothole). Volume and area of pothole was then
calculated at about 0.00354 m 3 and 0.1182 m 2 respectively,
with the help of the program SURFER. Consequently, depth of
the distress of about 29.95 mm could be easily determined by
dividing the volume with the area. The severity of pothole was
classified according to the DEM data based on guidelines set by
the Public Works Department (JKR) of Malaysia. Based on
Table 1, the pothole was classified as a moderate level.
AREA (m 2 )
DEPTH (mm)
<0.1
r«~>
O
o
>0.3
<25
Low
Low
Moderate
25-50
Moderate
Moderate
High
>50
Moderate
High
High
Table 1. Severity level of pothole (JKR, 1992)
3.2 Matlab Environment
The second stage is to develop an Automated Pavement
Imaging Program (APIP). The APIP for pavement crack
analysis involved six major steps: image enhancement, image
thresholding, morphology closing, thinning, distress
classification and distress quantification.
3.2.1 Image Acquisition
The first step involved in the automated image processing is the
acquisition and digitization of the image. The height from
digital camera (5.4mm) lens to the pavement was about 1.00
meter. The digitized array size was 640 by 480 pixels, which
resulted in 480 lines vertically and 640 elements horizontally.
The original image was a mathematical representation of a
colour image in a 24-bit per pixel size format. This colour
image consisted of a combination of three 8-bit arrays, and 8 bit
arrays contained brightness value for red, green and blue,
respectively. To facilitates image processing and manipulation
of the image, a brightness level of each pixel, assigned at a
value between 0 (black) and white (255) is needed to convert
the colour image to gray scale image. Thus, the 24-bit per pixel
format was converted to a 8-bit per pixel format, and this
reduced the file space required for storage by two-thirds.
Figure 6. Schematic diagram of the Matlab environment
3.2.2 Image Enhancement Algorithm
Image enhancement was applied in an attempt to remove noise
in pavement images. Median filtering was therefore applied as
pavement image enhancement technique in this research. It is
similar to using an averaging filter, in that each output pixel is
set to an average of the pixel values in the neighborhood of the
corresponding input pixel. The size of the neighborhood used
for filtering is 3-by-3. However, with median filtering, the
value of an output pixel is determined by the median of the
neighborhood pixels, rather than the mean. The median is much
less sensitive than the mean to extreme values. Median filtering
is therefore better able to remove these outliers without
reducing the sharpness of the image.
3.2.3 Image Thresholding Algorithm
The spatial and light intensity information on the image is
usually combined with a thresholding technique for the
improved image segmentation. The segmentation relates the
threshold for a given image to the mean and standard deviation
value of the corresponding gray scale histogram through an
equation.
Once the optimal threshold value was determined, the pixels
with gray level below the threshold were referred to as distress
pixels and pixels whose gray level value exceeded the threshold
were referred to as background. The selection of an appropriate
value played a very important role in the entire process since it
was the value that ultimately defined the mapping of the
distress features in the binary image. It is found that the simple
image enhancement algorithm worked well in predicting the
presence or absence of distress features on the image as shown
in Figure 7. Figure 8 show the binary image obtained from
proposed segmentation algorithm