Abstract
Combining tensor decomposition and image nonlocal information for remote sensing image fusion method (NCTCP) can effectively preserve the spatial structure of the image, and can obtain good image fusion results, accordingly. However, the NCTCP that is a serial algorithm cannot handle massive remote sensing images due to the computing resources limitation of a single computer. To address this issue, we propose a distributed parallel nonlocal tensor CP decomposition optimization algorithm (DP_NCTCP) based on the Spark platform. The alternating direction method of multipliers(ADMM) in NCTCP is divided into two distributed computing subtasks that can be executed on Spark in parallel to improve the efficiency. Compared with NCTCP, the DP_NCTCP achieves high speedups without the degradation of fusion quality measures accuracy by fusing the real hyperspectral images.
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