Unified Cloud-Based Framework for Hyperspectral and Multispectral Image Fusion Incorporating Nonlocal Principles and Tensor Decomposition

Hyperspectral image (HSI) super-resolution, which aims at improving the spatial quality of HSIs by fusing a low spatial resolution HSI (LR-HSI) with a high spatial resolution multispectral image (HR-MSI), has drawn significant attention. Numerous LR-HSI and HR-MSI (HSI-MSI) fusion algorithms have emerged in recent times, yet they suffer from a lack of generality and integration, which hampers their usability for non-expert users. Moreover, these algorithms encounter significant challenges due to the exponential increase in remote sensing data volume. In this study, we propose a unified cloud-based framework for HSI-MSI fusion based on the general distributed alternating direction method of multipliers that incorporates nonlocal principles and tensor decomposition. The framework not only provides end-users with visualization modeling capabilities equipped with standard and comprehensive components, but also enhances the parallel processing capabilities of cloud computing. We employ a new proposed nonlocal adaptive low-rank coupled tensor canonical polyadic (CP) decomposition algorithm as a case study to evaluate the performance of this framework. Specifically, we establish the LR-HSIs and HR-MSIs relationship using order-4 coupled tensor CP decomposition and suggest an adaptive CP rank estimation method for achieving better super-resolution results. Experimental results on publicly available datasets demonstrate that the proposed parallel distributed optimization algorithm can achieve significant speedup with guaranteed accuracy. The proposed framework enables convenient and efficient processing of large-scale remote sensing data, effectively addressing the challenges associated with handling large data volumes. The source code of our method is released and available online at https://github.com/ZpWaitingForSunshine/DNAC4TCP/.