Recently, as the amount of data that needs to be processed is getting larger, various techniques of parallel processing have been developed to deal with large-scale data efficiently and quickly. In this paper, we shall present fast parallel orthorectification algorithms for large-scale image data on various environments, and compare their performance in terms of speed-up and execution time. Our research consists of two parts: First, we implement parallel orthorectification algorithm on CPU multicore, Xeon-phi multicore and GPU. Second, we analyze these experiment results by comparing the performance of each algorithm. More specifically, we compare the performance of CPU multicore with Xeon-phi and GPU parallelization to find which one is more efficient as well as the performance of Xeon-phi multicore and GPU parallelization. We shall show that for the former, GPU parallelization is more efficient technique than CPU multicore parallelization, while for the latter, Xeon-phi multicore parallelization is more efficient technique than GPU parallelization. This is due to the data upload/download time on GPU. Even if we extend the experiment to infinite-scale, data upload/download time on GPU is still needed. Therefore, Xeon-phi multicore parallelization is better than GPU parallelization not only on the extended environment but also the infinite-scale environment.