Nowadays remote sensing plays a key role in the field of earth science studies due to some of the advantages, including data collection at a very low cost and time on a very large scale. Meanwhile, using hyperspectral data is of great importance due to the high spectral resolution. Because of some limitations, such as hyperspectral imaging technology, it suffers from a reduction in the spatial resolution. For this purpose, the use of pixel-level data fusion techniques has been suggested by researchers in this field. The variety of image fusion algorithms and the different capabilities of each of them has always faced a huge challenge for users. To address this challenge, the present study intends to examine a variety of new techniques for data fusion at pixel level so that users can choose optimum methods according to their needs. This study examines the 13 new techniques of data fusion on visually and quantitatively on hyperspectral images with a variety of complex and diverse classes. The hyperspectral images used in this study are hyperion sensors with spatial resolution of 30 meters. Also, to improve the spatial resolution capability, a pan-chromatic image of the Advance Land Imager (ALI) sensor was used with spatioal resolution of 10 meters. The study area is divided into two general areas: urban area and agricultural area located in city of Tehran, Iran. The results of the data fusion methods, in addition to the qualitative assessment, were quantitatively analyzed using the spectral and spatial evaluation indices as well as the processing time of each of the algorithms. The results of the evaluations show that the Coupled Non-negative Matrix Factorization (CNMF) method has a better performance than other methods as it has been able to improve the spatial resolution of pixel level objects by maintaining the spectral and spatial detail, but it requires high processing time. Also, the Non-linear Intensity Hue-Saturation (NHIS) method has the least processing time (under one second), but the spectral and spatial details of objects can not be properly maintained.