Improved accuracy performance of air for remote sensing application using multi sensor

Image registration is one of the crucial steps in the analysis of remotely sensed data. A new acquired image must be transformed, using image registration techniques, to match the orientation and scale of the related reference images. Image registration requires intensive computational effort not only because of its computational complexity, but also due to the continuous increase in image resolution. In this project, a method for improved accuracy of automatic image registration (AIR) for remote sensing application using multisensors is proposed. This new approach mainly consists in combining several segmentations of the pair of images to be registered, according to a relaxation parameter followed by a consistent characterization of the extracted objects - through the objects area, ratio between the axis of the adjust ellipse, perimeter and fractal dimension - and a robust statistical based procedure for objects matching. The application of the proposed methodology is widely used in remote sensing for simulated rotation and translation. The first dataset consists in a photograph and a rotated and shifted version of the same photograph, with different levels of added noise. It was also applied to a pair of satellite images with different spectral content and simulated translation, and to real remote sensing examples comprising different viewing angles, different acquisition dates and different sensors. Accuracy below 1^o for rotation and at the sub pixel level for translation was obtained, for the most part of the considered situations. It allows for the registration of pairs of images (multitemporal and multisensory) with differences in rotation and translation, with small differences in the spectral content, leading to sub pixel accuracy.