3.2 Feature Extraction
Feature extraction means segmentation of an image. Segmentation algorithms for monochrome images generally are based on basic properties of grey-level values: discontinuity. In this approach an image partition is based on abrupt changes in grey level.
There are three type of discontinuities in a digital image : point, line and edges. In this paper, in detection is being discussed.
Line detection means detection of the discontinuities in the digital image. the most common way to look for discontinuities is to run mask through the image. the procedure involves computing the sum of products of the coefficients with grey levels contain in the region encompassed by the mask. That is, the response of the mask at any point in the image is
R = w1z1 + w2z2 + …… + w9z9 =å wizi
| w1 |
w2 |
w3 |
| w4 |
w5 |
w6 |
| w7 |
w8 |
w9 |
Where Zi is the grey level of the pixel associated with mask coefficient wi. As usual, the response of the mask is defined with respect to its center location.
Masks which used to detected lines in the image as the first approach and result is shown in figure 2.0.
Figure 2 Orthogonal mask (From Frei and Chen, 1977) and resulted image
In the second approach, the following algorithm was applied.
The algorithm is :
Begin For every (I,j) position on the image Do Take the 3×3 surrounding sub image; Sort its 9 values in the array in the array Rank; Median = Rank (5); Final image = Original image – Median; End.
Algorithm 1.0
The resulted image is shown in figure 3.0
Figure 3 Results from algorithm 1.0
3.0 Visual Detection
Visual detection is the technique used by the image interpreter without applying automatic recognition methods, for the purpose of identifying object and judging their significance.
Visual interpretation of the semantic information are concerned in this issue. In this case, the nature of objects and identification of them is considered. The most fundamental characteristics which helps for that are : tone, texture, pattern, shape, size, shadow and situation. However, one has to bear in mind that is is not sufficient during interpretation. It needs also collateral material and field check.
Visually detected line features are shown in figure 4.0.
Figure 4 Visually interpreted image
4.0 Feature Classification
The extracted features are then to be classified in to different object types. This classification can be employed as:
(i) Using the specific knowledge about the objects.
This knowledge is generally based on the nature of the object. For example, if the nature of the objects are roads then the specific knowledge abut the roads are
- Elongated objects with restricted and relatively constant width.
- Connected to a form of a network.
- Network consist of nodes and arcs.
(ii) Comparing the new image with the existing information
This is generally based on the nature of the task. For an example, if the task is updating, it needs and extra knowledge abut the existing information. It gives,
- The location of the old situation.
- Properties of objects.
5.0 Conclusion
the work presented here is only for small study area. Due to the time constraint, it was not possible to employ feature classification method as discussed in section 4.0 The result obtained from both algorithms show a similarity. However, results of this experimental work are encouraging and the method used, may be considered for updating of road network and hydrography features with the availability of high spatial resolution satellite images.
References
- Frei, W. and Chen, C.C. (1977). “Fast Boundary Detection: A Generalization and a New Algorithm. “IEEE Transaction Computer, vol. C-26.
- Rafael, C.G. and Richard, E.W., (1992). “Digital Image Processing. “ISBN 0-201-60078-1, Addison –Wesly Publishing Company.
- Bill, S., Jianying Hu, Theo Pavlidis, (1993) “Computer Assisted Tracing Of Fint Roads in Satellite Image.”, ACSM/ASPRS Annual Convention, New LA.
