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Poster Session
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Research on the application of relaxation technology to the extraction of linear feature from satellite image
Method
Relaxation process is conducted the co-influence of pixel in neighbour field. It makes the lines on one direction extended corresponding to another direction weakened. Similarly, nonlinear liable is strengthen by the non-label in the neighbour field and weakened by suitable linear label. It takes relaxation iteration a few times to be weakened. The points on longer curve may get higher label probability, while others get non-label probability.
In order to use (2), (3) to accomplish linear enhancement it is necessary to solve the problems like to choice of detector, the estimation of initial probability and the definition of compatibility coefficient among objects. It goes as following :
- Choose detector: There are three kinds of local detector applied to the extraction of linear feature : linear detector, semi-linear detector and non-linear detector. The extraction of linear feature from remote sensing image, which aims at using gray of pixel, should use non-linear detector, becomes it is fit for the detector of points with the following two characters: a. The gray of the point should be higher than that of it two neighbour points on the same vertical direction of this point shoul have the features as shown in (fig. 1).
fig.1. non-linear detector
As for non-linear detector, when condition |b| - |lUc|>Td (defined threshold) is given, e can judge that there exists line in district B non-linear detector divides the whole district into sorts of small districts which can all satisfy the following conditions.
When {|B2|>A2|} and {|B2|>|C2|},
we judge that line exists in district B.
Linear detector demands the gray along the lien direction bigger the average gray of all the pixels involved in the two small neighbour fields.
And so it obviously responds to both noise and border. Semi-linear detector compares the two neighbour small fields seperately and only reflects the noise of single point : while non linear detector reflects neither noise nor border and sometimes the extracted linear feature is interrupted.
- Estimation of initial probablit: As non-linear reflects neither noise nor border, and its function is more distinguished at the choice of threshold, the application of contextual information may carry out the connection of local discontinuity.
Now we beging the estimation of the initial probability of non linear detector as direction number k-=8 is chosen (with reference to Fig2 and Fig 3).
Fig. 2. linear feature detector model on vertical direction
Fig. 3. linear feature detector model on 8 different direction
The initial probability estimation of certain point is defined according to different import values of eight detectors on different directions. To explain accurately, we suppose mk (x,y) represent point (x,y)'s import value of detector on qk direction, P(q)(x,y) (lk) represent point (x,y) is initial probability with label lk. After we multiply detector value with certain coefficient, then to be standardized, the following initial probability is gained.
Eq.4 & 5.
To add smaller constant to the above formula can ensure every initial probability not to the zero and non-linear label probability not to be 1, so that the input of changing coefficient
makes the result more reliable.
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