On the classification of multispectral satellite images using the multilayer perceptron

For classifying multispectral satellite images, a multilayer perceptron (MLP) is trained using either (i) ground truth data or (ii) the output of a K-means clustering program or (iii) both, as applied to certain representative parts of the given data set. In the second case, different sets of clustered image outputs, which have been checked against actual ground truth data wherever available, are used for testing the MLP. The cover classes are, typically, different types of (a) vegetation (including forests and agriculture); (b) soil (including mountains, highways and rocky terrain); and (c) water bodies (including lakes). Since the extent of ground truth may not be sufficient for training neural networks, the proposed procedure (of using clustered output images) is believed to be novel and advantageous. Moreover, it is found that the MLP offers an accuracy of more than 99% when applied to the multispectral satellite images in our library. As importantly, comparison with some recent results shows that the proposed application of the MLP leads to a more accurate and faster classification of multispectral image data.