Abstract
Macroscopic images are kind of environments in which complex patterns are present. Satellite images are one of these classes where many patterns are present. This fact reflects the challenges in detecting patterns present in this kind of environments. SPOT1b satellite images provide valuable information. These images are affordable and can be applicable in wide applications. This paper demonstrates an approach to generate detection plane that visualize patterns present in the satellite image. The detection plane uses rough neural network to provide optimal representation in backpropagation architecture. Rough set theory combined with multilayer perceptron constitutes the rough neural network. Reduction in the feature dimensionality via the rough module improves the recognition ability of the neural network. It is found that the rough module provides the neural network with optimal features. The ability of the neural network to efficiently detect and visualize the pattern stems from a developed extraction algorithm. The result of the hybrid architecture provides the plane with the best features that visualize the phenomena under investigation. Together with the novel extraction algorithm, the developed system provides a tool to visualize patterns present in SPOT1b Satellite image.