This paper compares the performances of the most commonly used keypoint detectors and descriptors (SIFT, SURF, KAZE, AKAZE, ORB, and BRISK) for Relative Radiometric Normalization (RRN) of unregistered bitemporal multi-spectral images. The keypoints matched between subject and reference images represent possible unchanged regions and are used in forming a Radiometric Control Set (RCS). The initial RCS is further refined by removing the matched keypoints with a low cross-correlation. The final RCS is used to approximate a linear mapping between the corresponding bands of the subject and reference images. This procedure is validated on five datasets of unregistered multi-spectral image pairs acquired by inter/intra sensors in terms of RRN accuracy, visual quality, quality and quantity of the samples in the RCS, and computing time. The experimental results show that keypoint-based RRN is robust against variations in spatial-resolution, illumination, and sensors. The blob detectors (SURF, SIFT, KAZE, and AKAZE) are more accurate on average than the corner detectors (ORB and BRISK) in RRN. However, they are slower in computing. The source code and datasets used in experiments are available at https://github.com/ArminMoghimi/keypoint-based-RRN to support reproducible research in remote sensing. CCBY