Intensity based image registration of satellite images using evolutionary techniques

Image registration is the fundamental image processing technique to determine geometrical transformation that gives the most accurate match between reference and floating images. Its main aim is to align two images. Satellite images to be fused for numerous applications must be registered before use. The main challenges in satellite image registration are finding out the optimum transformation parameters. Here in this work the non-alignment parameters are considered to be rigid and affine transformation. An intensity based satellite image registration technique is being used to register the floating image to the native co-ordinate system where the normalized mutual information (NMI) is taken as the similarity metric for optimizing and updating transform parameters. Because of no assumptions are made regarding the nature of the relationship between the image intensities in both modalities NMI is very general and powerful and can be applied automatically without prior segmentation on a large variety of data and as well works better for overlapped images as compared to mutual information(MI). In order to get maximum accuracy of registration the NMI is optimized using Genetic algorithm, particle swarm optimization and hybrid GA-PSO. The random initialization and computational complexity makes GA oppressive, whereas weak local search ability with a premature convergence is the main drawback of PSO. Hybrid GA-PSO makes a trade-off between the local and global search in order to achieve a better balance between convergence speed and computational complexity. The above registration algorithm is being validated with several satellite data sets. The hybrid GA-PSO outperforms in terms of optimized NMI value and percentage of mis-registration error

Shear-Resize Factorizations For Fast Multi-Modal Volume Registration

Intensity-based methods work well for multi-modal image registration owing to their effectiveness and simplicity, but the computation for geometric transforms is a heavy load. To accelerate the transformation, in this paper, we present two shear-resize matrix factorizations for general 3D linear transformation, to factorize a transform matrix into three shears and a fixed resize, or four shears and a customizable resize. Shears can be implemented very fast by memory shift, and a resize can be done by axis-aligned resampling. The factorizations can be applied to both rigid-body and affine transformations. The experiments on MRI and CT volumes show that our method is 10 times faster than the naive transformation. The method is quite promising for hardware and parallel implementation, and is also valid for mono-modal image registration