In remote sensing, image registration is to find the best transform between a reference and input images that may be different due to changes in position or altitude of or noise in the sensors. Image registration is one of the first steps in the analysis of remotely sensed images and requires high computational resources. The computation time is affected by two factors: search data size and search space. This paper describes an efficient image registration algorithm that uses multi-resolution wavelet decomposed images to reduce the search data size, and Genetic Algorithms to optimize the search solution space. Experimental results have shown subpixel accuracy and high efficiency over conventional methods.

Digital image registration is very important in many applications, such as medical imagery, robotics, visual inspection, and remotely sensed data processing. NASA s Mission To Planet Earth (MTPE) program will be producing enormous Earth global change data, reaching hundreds of Gigabytes per day, that are collected form different spacecrafts and different perspectives using many sensors with diverse resolutions and characteristics. The analysis of such data requires integration, therefore, accurate registration of these data. Image registration is defined as the process which determines the most accurate relative orientation between two or more images, acquired at the same or different times by different or identical sensors. Registration can also provide the absolute orientation between an image and a map.

We present a uniform construct of parallel programming for a set of image processing tasks based on our distributed computing primitive (DCP) concept. Our target architecture is a heterogeneous computing network system consisting of various high performance workstations connected through a local area network. We show that DCP has advantages over non-primitive PVM-based parallel approaches in three aspects: ease-of-use, automation, and optimization.