Sallfus, library for satellite images fusion on homogeneous and heterogeneous computing architectures

Fusion of satellite images consists of improving the quality of a multispectral image by combining data from a high spatial resolution panchromatic image with a high spectral resolution multispectral image. To carry out this, different techniques are available, which perform various operations at the pixel level, which leads to generating a dependency between the computational requirement and the image size. Currently, there are some libraries that implement these fusion methods, however, none of them allow this fusion process to be carried out on heterogeneous architectures, which enable the integration of acceleration platforms that reduce execution time through massive parallelization. For this reason, this document presents a library called Sallfus, which allows executing and evaluating the quality and performance of image fusion methods such as the Brovey transform, Multiplicative method, Principal Component Analysis (PCA) and Wavelet A trous, on homogeneous and heterogeneous architectures. Likewise, an evaluation of the library is made from an analysis of execution times and image quality using mathematical-statistical indices such as the correlation coefficient (CC), BIAS coefficient and Root of the Root Mean Square Error (RMSE). The results of the library evaluation showed that the merging process with images of 8192 pixels, presents a speed-up of approximately 591x for Brovey, 309x for Multiplicative, 18x for PCA and 6x for A trous. Additionally, it was observed that the methods that presented the best performance both computationally and in the quality of the merged image were Brovey and Wavelet A trous. Availability and implementation: https://github.com/Parall-UD/sallfus.