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The radial basis function (RBF) neural network is a powerful method for remote sensing image classification. It has a
simple architecture and the learning algorithm corresponds to the solution of a linear regression problem, resulting in a
fast training process. The main drawback of this strategy is the requirement of an efficient algorithm to determine the
number, position, and dispersion of the RBF. Traditional methods to determine the centers are: randomly choose input
vectors from the training data set; vectors obtained from unsupervised clustering algorithms, such as k-means, applied to
the input data. These conduce that traditional RBF neural network is sensitive to the center initialization. In this paper,
the artificial immune network (aiNet) model, a new computational intelligence based on artificial immune networks
(AIN), is applied to obtain appropriate centers for remote sensing image classification. In the aiNet-RBF algorihtm, each
input pattern corresonds to an antigenic stimulus, while each RBF candidate center is considered to be an element, or cell,
of the immune network model. The steps are as follows: A set of candidate centers is initialized at random, where the
initial number of candidates and their positions is not crucial to the performance. Then, the clonal selection principle will
control which candidates will be selected and how they will be upadated. Note that the clonal selection principle will be
responsible for how the centers will represent the training data set. Finally, the immune network will identify and
eliminate or suppress self-recognizing individuals to control the number of candidate centers. After the above learning
phase, the aiNet network centers represent internal images of the inuput patterns presented to it. The algorithm output is
taken to be the matrix of memory cells' coordinates that represent the final centers to be adopted by the RBF network.
The stopping criterion of the proposed algorithm is given by a pre-defined number of iterations. The classification results
are evaluated by comparing with that of the k-means center selection procedures and other results from the literature
using remote sensing imagery. It is shown that aiNet-RBF NN algorithm outperform other algorithms and provides an
effective option for remote sensing image classification.
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