A supervised neural classifier based on Fisher criterion is implemented to classify two regions in a real speckled SAR image. Regions around pre-classified pixels are presented to train the neural network that learns a sub-optimal set of masks via back-propagation algorithm. Classification performance is evaluated by using the ground truth. Results with higher than 90% of correct classification are obtained. The results are also compared with a statistical classifier based on Kullback-Liebler distance via the Kappa coefficient.
Citation:
Alexsandro M. Jacob, Elder M. Hemerly, David Fernandes, "SAR Image Classification Using a Neural Classifier Based on Fisher Criterion," sbrn, pp.168, VII Brazilian Symposium on Neural Networks (SBRN'02), 2002