Given a satellite image, we want to make a classification (i.e. to assign a label to each pixel) coupled with a restoration process in order to remove noisy pixels.
This work takes place in the general framework concerning automatic feature extraction.

Proposed method

The proposed method belongs to variational approaches. In these approaches, the image is usually represented by a function depending on real variables (for instance the grey level associated to each pixel).
The goal is to define a functional, or energy, whose minimization leads to a piecewise constant image, the segmented regions being homogeneous and separated by smooth boundaries (constraint of homogeneity with anisotropic smoothing). A region will be defined as a set of pixels belonging to the same class.

Hypothesis :

We assume that the number and the parameters of the classes are known (supervised classification). We also suppose that the classes have a Gaussian distribution, so the parameters of each class are its mean and standard deviation. The label designing a class is set to its mean value. Herein, we are interested in the local distribution of intensity (grey level), but other discrimant features as the texture one for instance can be considered.

- Proposed model
- Algorithm
- Comparison with a stochastic model
- Results : synthetic image
- Results : first satellite image
- Results : second satellite image
- Conclusion

- Bibliography