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Abstract
Artificial Neural Networks for Improvement of Classification Accuracy in Landsat ETM+ Images
Meysam Argany
University of Tehran,
Iran
Email: margani@ut.ac.ir
Remote sensing data are often used in land cover and land use applications. However, classes of interest are often imperfectly separable in the feature space provided by the spectral data. The application of Neural Network (NN) to the classification of satellite images is increasingly emerging. Without any assumption about the probabilistic model to be made, the networks are capable to forming highly non-linear decision boundaries in the feature space. Training has an important role in the NN. The objective of this paper is to develop an Artificial Neural Network (ANN) for classification of Landsat ETM+ images into various types of land-use, especially for urban areas. So specific land-use classes are defined including city, water, bare rock, vegetation, and various types of soils. The test area is an image from Karaj in Iran.
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