A PARALLEL IMPLEMENTATION FRAMEWORK FOR REMOTELY SENSED IMAGE FUSION

The fusion algorithms are data-intensive and computation-intensive, whose processing performance is low if common implementation techniques are adopted. This paper presents a parallel implementation framework based on a generalized fusion model and parallel computing for remotely sensed image fusion. The steps in the implementation corresponding to each fusion algorithm are mainly the same, which discard the process steps, input and output （I/O）operations not impacting the last results. The parallel processing mechanism adopts the data decomposition strategies where different data blocks can be processed in different CPU cores. The developed software module shows that the framework can be applied to implement many algorithms which usually are divided into three general categories. The experiments show that the parallel implementation framework is high performance, which not only decreases the processing time in single CPU core environment but also efficiently utilizes the resources of multi-cores in computer