An intelligent approach towards automatic shape modelling and object extraction from satellite images using cellular automata-based algorithms

Automatic feature extraction has witnessed the use of many intelligent methodologies over the past decade. However, inadequate modelling of feature shape and contextual knowledge has limited the detection accuracy. In this article, we present a framework for accurate feature shape modelling and contextual knowledge representation using advanced techniques such as Vector Machines, Cellular Neural Network (CNN), coreset, and Cellular Automata (CA). CNN was found to be effective in modelling different complex features, and the complexity of the approach was considerably reduced using corset optimization. Spectral and spatial information was dynamically combined using adaptive kernels when representing contextual knowledge. The methodologies were compared with contemporary methods using different statistical measures. Application of the algorithms to satellite images revealed considerable success. The methodology was also effective in providing intelligent interpolation and interpretation of random features.