Optimum Learning Rate in Back-Propagation Neural Network for Classication of Satellite Images (IRS-1D)

Remote sensing data are essentially used for land cover and vegetation classication. However,
classes of interest are often imperfectly separable in the feature space provided by the spectral
data. Application of Neural Networks (NN) to the classication of satellite images is increasingly
emerging. Without any assumption about the probabilistic model to be made, the networks are
capable of forming highly non-linear decision boundaries in the feature space. Training has an
important role in the NN. There are several algorithms for training and the Variable Learning
Rate (VLR) is one of the fastest. In this paper, a network that focuses on the determination of
an optimum learning rate is proposed for the classication of satellite images. Dierent networks
with the same conditions are used for this and the results showed that a network with one hidden
layer with 20 neurons is suitable for the classication of IRS-1D satellite images. An optimum
learning rate between the ranges of 0.001-0.006 was determined for training the VLR algorithm.
This range can be used for training algorithms in which the learning rate is constant.