Remote sensing image processing is characterized with features of massive data processing, intensive computation, and complex processing algorithms. These characteristics make the rapid processing of remote sensing images very difficult and inefficient. The rapid development of general-purpose graphic process unit (GPGPU) computing technology has resulted in continuous improvement in GPU computing performance. Its strong floating point calculating capability, high intensive computation, small volume, and excellent performance-cost ratio provide an effective solution to the problems faced in remote sensing image processing. However, current usage of GPU in remote sensing image processing applications has been limited to specific parallel algorithms and their optimization of processes, rather than formed well-established models and methods. This has introduced serious problems to the development of remote sensing image processing algorithms on GPU architectures. For example, GPU parallel strategies and algorithms are highly coupled and non-reusable. The processing system is closely associated with the GPU hardware so that programming for remote sensing algorithms on GPU is nothing but easy. In this paper, we attempt to explore a reusable GPU-based remote sensing image parallel processing model and to establish a set of parallel programming templates, which provides programmers with a more simple and effective way for programming parallel remote sensing image processing algorithms.