OPTIMUM LEARNING RATE IN BACK-PROPAGATION N EURAL NETWORK FOR CLASSIFICATION OF SATELLITE IMAGES (IRS-ID)

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AMINI J.

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Journal: SCIENTIA IRANICA Year:2008 | Volume:15 | Issue:6 Page(s): 558-567

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Title

OPTIMUM LEARNING RATE IN BACK-PROPAGATION N EURAL NETWORK FOR CLASSIFICATION OF SATELLITE IMAGES (IRS-ID)

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AMINI J. | Issue Writer Certificate

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Abstract

Remote sensing data are essentially used for land cover and vegetation classification. However, classes of interest are often imperfectly separable in the feature space provided by the spectral data. Application of Neural Networks (NN) to the classification 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 classification of satellite images. Different 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 classification 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.

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APA: Copy

AMINI, J.. (2008). OPTIMUM LEARNING RATE IN BACK-PROPAGATION N EURAL NETWORK FOR CLASSIFICATION OF SATELLITE IMAGES (IRS-ID). SCIENTIA IRANICA, 15(6), 558-567. SID. https://sid.ir/paper/289722/en

Vancouver: Copy

AMINI J.. OPTIMUM LEARNING RATE IN BACK-PROPAGATION N EURAL NETWORK FOR CLASSIFICATION OF SATELLITE IMAGES (IRS-ID). SCIENTIA IRANICA[Internet]. 2008;15(6):558-567. Available from: https://sid.ir/paper/289722/en

IEEE: Copy

J. AMINI, “OPTIMUM LEARNING RATE IN BACK-PROPAGATION N EURAL NETWORK FOR CLASSIFICATION OF SATELLITE IMAGES (IRS-ID),” SCIENTIA IRANICA, vol. 15, no. 6, pp. 558–567, 2008, [Online]. Available: https://sid.ir/paper/289722/en

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