Abstract
Image registration is a key component of spatial analyses that involve different data sets of the same area. Automatic approaches in this domain have witnessed the application of several intelligent methodologies over the past decade; however accuracy of these approaches have been limited due to the inability to properly model shape as well as contextual information. In this paper, we investigate the possibility of an evolutionary computing based framework towards automatic image registration. Cellular Neural Network has been found to be effective in improving feature matching as well as resampling stages of registration, and complexity of the approach has been considerably reduced using corset optimization. CNN-prolog based approach has been adopted to dynamically use spectral and spatial information for representing contextual knowledge. The salient features of this work are feature point optimisation, adaptive resampling and intelligent object modelling. Investigations over various satellite images revealed that considerable success has been achieved with the procedure. Methodology also illustrated to be effective in providing intelligent interpretation and adaptive resampling.
Highlights
Image registration deals with the determination of local similarity between images, and involves the calculation of spatial geometric transforms that aligns related images to a common observational framework
Our studies have found that techniques such as Cellular Automata (CA) (Mitchell et al 1996), Cellular Neural Network (Orovas, Austin 1997) and Multiple Attractor Cellular Automata (Sikdar et al 2000) along with Genetic Algorithm (GA) (Jian, Vemuri 2005) can be efficiently used for modeling feature shapes
Root Mean Square (RMSE) value indicates the error in registration and a least value is preferred for a perfect registration
Summary
Image registration deals with the determination of local similarity between images, and involves the calculation of spatial geometric transforms that aligns related images to a common observational framework. Registration is a critical component of various spatial analyses; its accuracy is affected by various factors such as geometrical complexity, noise, vague boundaries, mixed pixel problems, and fine characteristics of detailed structures (Fonseca, Costa 2004). Automatic image registration approaches have been broadly categorized as area based and feature based, among which the former adopts a region specific strategy whereas the latter a feature based one. Increasing resolution of satellite images have limited the accuracy of area based strategies and in turn popularized object based approaches. Different existing feature based algorithms lack contextual interpretation capability and adopt computationally complex methods (Zitová, Flusser 2003). Efficiency of these methods are situation and image-specific due to involvement of various parameters like spatial and spectral resolution, sensor characteristics etc. Efficiency of these methods are situation and image-specific due to involvement of various parameters like spatial and spectral resolution, sensor characteristics etc. (Viola, Wells 1997; Mohanalin et al 2009)
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