In the realm of unmanned aerial vehicle aerial photography, images obtained from disparate sensors often exhibit significant parallax and resolution disparities, which can lead to failures in image registration processes. Addressing this challenge, this study introduces an innovative approach for the registration of infrared and visible light images, utilizing a rotation-invariant Gabor representation descriptor. The methodology commences by resolving the image's weighted matrix, followed by the application of the Harris algorithm to the weighted matrix within the context of phase congruence, thereby pinpointing the image's key features. Subsequently, the Gabor representation framework is refined to precisely ascertain the orientation of key features, effectively mitigating the impact of substantial parallax. To further enhance the process, the nearest neighbor matching (NNM) algorithm, in tandem with fast sampling consistency (FSC), is deployed to filter out outliers and augment the accuracy of matches. The technique demonstrates an average accuracy of 46%, 72%, and 62% across the CVC-15 stereo, LWIR-RGB long-wave infrared, and proprietary datasets, respectively. Correspondingly, the average processing times are 6.886 seconds, 7.800 seconds, and 9.631 seconds. Experimental results prove that the efficacy of the proposed method, particularly in scenarios where the images to be registered present considerable parallax and resolution differences.