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Parallel computing is a common method to accelerate remote sensing image processing. The paper briefly describes six commonly used interpolation functions and studies three commonly used parallel computing methods of the corresponding nine interpolation algorithms in remote sensing image processing. Firstly, two kinds of general parallel interpolation algorithms (for CPU and GPU respectively) were designed. Then, in two typical application scenarios (data-intensive and computing-intensive), four computing methods (one serial method and three parallel methods) of these interpolation algorithms were tested. Finally, the acceleration effects of all parallel algorithms were compared and analyzed. On the whole, the acceleration effect of parallel interpolation algorithm is better in computer-intensive scenario. In CPU-oriented methods, the speedup of all parallel interpolation algorithms mainly depends on the number of physical cores of CPU, while in GPU-oriented methods, a speedup is greatly affected by the computation complexity of an algorithm and the application scenario. GPU has a better acceleration effect on the interpolation algorithms with bigger computation complexity, and has more advantages in the computing-intensive scenarios. In most cases, GPU-based interpolation is ideal for efficient interpolation.

Parallel Computing Method of Commonly Used Interpolation Algorithms for Remote Sensing Images