Classification of multi-spectral, multi-temporal and multi-sensor images using principal components analysis and artificial neural networks: Beykoz case

Abstract

The thematic maps derived from remotely-sensed images are invaluable sources of information for various investigations since they provide spatial and temporal information about the nature of Earth surface materials and objects. The robustness of classification techniques used to produce these thematic maps can be crucial especially for complex classification problems. This study aims to determine the level of contributions of multi-temporal and multi-sensor data together with their principal components for Maximum Likelihood and Artificial Neural Network classifiers. The performance of a multi-layer perceptron that learns the characteristics of the data using backpropagation algorithm is compared to that of Maximum Likelihood classifier in identifying major land cover classes present in the study area, Beykoz district of Istanbul, Turkey. The image data available for the study are from Landsat ETM+ and Terra ASTER images. Image band combinations are inputted to the neural network for training and the success of the classification is tested using test data sets. Results show that the neural network approach is an attractive and effective way of extracting land cover information using multi-spectral, multi-temporal and multi-sensor satellite images. It is also observed that the level of contribution of principal components to the results is much less than the contribution of multi-temporal data in terms of the classification accuracy. © 2018 Elsevier B.V., All rights reserved.