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

TL;DR

This paper presents an advanced framework for automatic feature detection and interpretation in satellite images, integrating cellular automata, neural networks, and optimization techniques to improve accuracy and efficiency.

Contribution

It introduces a novel combination of cellular automata, CNN, and core set optimization for enhanced feature detection and interpretation in satellite imagery.

Findings

CNN effectively models complex features

Core set optimization reduces complexity

System achieves high detection accuracy

Abstract

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.