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Automatic feature extraction domain has witnessed the application of manyintelligent methodologies over past decade; however detection accuracy of theseapproaches were limited as object geometry and contextual knowledge were notgiven enough consideration. In this paper, we propose a frame work for accuratedetection of features along with automatic interpolation, and interpretation bymodeling feature shape as well as contextual knowledge using advancedtechniques such as SVRF, Cellular Neural Network, Core set, and MACA. Developedmethodology has been compared with contemporary methods using differentstatistical measures. Investigations over various satellite images revealedthat considerable success was achieved with the CNN approach. CNN has beeneffective in modeling different complex features effectively and complexity ofthe approach has been considerably reduced using corset optimization. Thesystem has dynamically used spectral and spatial information for representingcontextual knowledge using CNN-prolog approach. System has been also proved tobe effective in providing intelligent interpolation and interpretation ofrandom features.

An intelligent approach towards automatic shape modeling and object extraction from satellite images using cellular automata based algorithm