Remote sensing data fusion algorithms with parallel computing

The advances in satellite technologies, image analysis techniques and computational power make possible processing huge amounts of high resolution images in real time. Acquiring high resolution images has a drawback, as the pixel resolution increases the surveyed area decreases. Multispectral scene is an image stack including numerous spectral bands from the electromagnetic wave spectrum, leading to richer spectral resolution. On the other hand, higher spatial resolution is included in the Panchromatic image. In order to have an image with higher spectral and spatial resolution, the applied merging process is called fusion. In this paper, fourteen different image fusion techniques were implemented. Serial implementations of all these approaches have longer execution time disadvantage compared to parallel approaches. To decrease execution time, the methods were modified with parallel computing approaches. This paper presents a comparison regarding speed performance of all fourteen methods' serial and parallel implementations to increase pixel resolution and keep spatial resolution high by combining spectral and spatial information of high and low resolution images of the same co-registered region. Additionally, spectral quality assessments of methods are presented.