Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities

More than most European cities, Istanbul is experiencing considerable pressure from urban development due to a rapidly increasing population. As a consequence the land use activities in urban and suburban areas are changing dramatically. To provide cost-effective information about the current state and how it is changing in order to develop integrated policies, multi-temporal remotely sensed data, with its synoptic and regular coverage, is being used. Nevertheless, the mapping and monitoring of urban change through remote sensing is difficult owing to the complex urban land use patterns. Although many image processing techniques have been developed for this purpose, they are complicated by differences amongst images caused by differences in the effects of the atmosphere, illumination, and surface moisture. One technique which is relatively unaffected by these problems is based on artificial neural network (ANN) classification algorithms. The main objective of this study was to examine the performance of two ANN classifiers for land use classification using Landsat TM data. Two different supervised ANN approaches were used: the multi layer perceptron (MLP) and the learning vector quantization (LVQ). The performance of these classifiers was compared to the more conventional maximum likelihood approach.