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

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 1 Email : arunpv2601@gmail.com representing contextual knowledge using CNN-prolog approach. System has been also proved to be effective in providing intelligent interpolation and interpretation of random features.

Integrating cellular automata, artificial neural network, and fuzzy set theory to simulate threatened orchards: application to Maragheh, Iran

“…CA encompasses the following components (Clarke, Hoppen, and Gaydos 1997;Arun and Katiyar 2013): spatial arrangement of cells having a specific state, neighborhood associations, and transition rules (Barredo et al 2003). Time in CA is considered to be discrete while space is frequently represented using two-dimensional grid cells (Shafizadeh Moghadam and Helbich 2013).…”

Intelligent adaptive resampling technique for the processing of remotely sensed imagery

“…Image is transformed into its Laplacian pyramid representation and high-frequency information of sub-pixel objects contained in the first level of the pyramid is preserved through intelligent selection of resamplers. Sub-pixel features are detected using evolutionary computing approaches such as cellular automata and their variants such as CNN and multiple attractor cellular automata (MACA) have been found to be useful for modelling random features (Arun and Katiyar 2013). CNN (Orovas and Austin 1998) is effectively used for modelling object shape to facilitate feature interpretation.…”

“…Hence, lower the number of pixels, faster will be the modelling. Coreset approximates the objects to similar shape with much lesser number of pixels, thereby reducing complexity of feature modelling/inverse mapping (Arun and Katiyar 2013). The approximated objects along with edge information are used to model feature shapes using CNN and MACA .…”

Mapathons versus automated feature extraction: a comparative analysis for strengthening immunization microplanning

“…Machine learning algorithms are designed to enhance performance by effectively teaching the computer how to extract the desired spatial data from imagery with both precision and accuracy. AFE has been leveraged for a myriad of purposes, such as mapping agricultural land use [ 13 – 16 ] and water boundaries [ 17 , 18 ], estimating human and livestock populations [ 19 , 20 ], road feature extraction [ 21 , 22 ], building feature extraction [ 23 – 29 ], and to support disaster relief efforts [ 30 , 31 ].…”