Stitching giga pixel images using parallel computing

This paper addresses the problem of stitching Giga Pixel images from airborne images acquired over multiple flight paths of Costa Rica in 2005. The set of input images contains about 10,158 images, each of size around 4072x4072 pixels, with very coarse georeferencing information (latitude and longitude of each image). Given the spatial coverage and resolution of the input images, the final stitched color image is 294,847 by 269,195 pixels (79.3 Giga Pixels) and corresponds to 238.2 GigaBytes. An assembly of such large images requires either hardware with large shared memory or algorithms using disk access in tandem with available RAM providing data for local image operation. In addition to I/O operations, the computations needed to stitch together image tiles involve at least one image transformation and multiple comparisons to place the pixels into a pyramid representation for fast dissemination. The motivation of our work is to explore the utilization of multiple hardware architectures (e.g., multicore servers, computer clusters) and parallel computing to minimize the time needed to stitch Giga pixel images. Our approach is to utilize the coarse georeferencing information for initial image grouping followed by an intensitybased stitching of groups of images. This group-based stitching is highly parallelizable. The stitching process results in image patches that can be cropped to fit a tile of an image pyramid frequently used as a data structure for fast image access and retrieval. We report our experimental results obtained when stitching a four Giga Pixel image from the input images at one fourth of their original spatial resolution using a single core on our eight core server and our preliminary results for the entire 79.3 Gigapixel image obtained using a 120 core computer cluster.