In this study, the authors generate panoramic images using feature-based registration for drone-based aerial thermal images. In the case of drone aerial images, the distortion of the photographing angle due to the unstableness in the shooting altitude deteriorates the performance of the stitching. Furthermore, for the thermal aerial images, the same objects photographed at the same time zone may have different colors due to the relative temperature, which may lead to a more severe condition to be stitched. Applying the scale-invariant feature transform descriptor, they propose a posteriori outlier rejection scheme to estimate the hypothesis of the mapping function for the stitching of consecutive thermal aerial images. By extension of the method of optimal choice of initial candidate inliers (OCICI) and a posteriori outlier rejection scheme using cross-correlation calculus, the authors obtained elaborate stitching of thermal aerial images. Their proposed method is numerically verified for its quality by comparing it with other possible approaches of post-outlier rejection treatments employed of OCICI. Also, after the Poisson blending using the finite difference method is conducted, the stitching performance is compared with some benchmark software such as Matlab-toolbox, OpenCV, Autopano Giga, Hugin, and PTGui.