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