  Automatic feature extraction domain has witnessed the application of many
intelligent methodologies over past decade; however detection accuracy of these
approaches were limited as object geometry and contextual knowledge were not
given enough consideration. In this paper, we propose a frame work for accurate
detection of features along with automatic interpolation, and interpretation by
modeling feature shape as well as contextual knowledge using advanced
techniques such as SVRF, Cellular Neural Network, Core set, and MACA. Developed
methodology has been compared with contemporary methods using different
statistical measures. Investigations over various satellite images revealed
that considerable success was achieved with the CNN approach. CNN has been
effective in modeling different complex features effectively and complexity of
the approach has been considerably reduced using corset optimization. The
system has dynamically used spectral and spatial information for representing
contextual knowledge using CNN-prolog approach. System has been also proved to
be effective in providing intelligent interpolation and interpretation of
random features.
