Our findings indicate that the limited size of MSI data is not a bottleneck for self-supervised learning. However, data augmentation is critical to the model training. We use a combination of appearance-changing transformations, such as Gaussian blur, Gaussian noise and intensity distortion to maximize the generalization power of the CNN model towards the efficient recognition of distinct localization patterns in ion images with varying levels of signal and noise. A similar self-supervised learning paradigm may be applied to other hyperspectral chemical imaging modalities including Raman and infrared microscopy.