Pansharpened images are frequently utilized as base data in image classification applications. Nonetheless, the accuracy of image classification heavily relies on the efficiency of the pansharpening strategy applied. With numerous existing pansharpening approaches available, it becomes challenging for analysts to select the one that yields the best outcome. Recently, the deep learning (DL)-based pansharpening approaches have become popular due to their capabilities. Thus, this study aims at examining the image classification performance of pansharpened images generated by several commonly used DL-based pansharpening algorithms that rely on pre-trained models and comparing them with those of several traditional pansharpening algorithms. The experiments that were conducted in two test sites indicated that the DL-based pansharpening algorithms could be used in various circumstances. It can also be inferred that the DL-based pansharpening algorithms enhanced the image classification accuracy more effectively than many other traditional algorithms. The use of pre-trained models led to robust pansharpening results and, therefore, superior classification accuracies in most cases.