  Multimodal image matching is an important prerequisite for multisource image
information fusion. Compared with the traditional matching problem, multimodal
feature matching is more challenging due to the severe nonlinear radiation
distortion (NRD). Radiation-variation insensitive feature transform
(RIFT)~\cite{li2019rift} has shown very good robustness to NRD and become a
baseline method in multimodal feature matching. However, the high computational
cost for rotation invariance largely limits its usage in practice. In this
paper, we propose an improved RIFT method, called RIFT2. We develop a new
rotation invariance technique based on dominant index value, which avoids the
construction process of convolution sequence ring. Hence, it can speed up the
running time and reduce the memory consumption of the original RIFT by almost 3
times in theory. Extensive experiments show that RIFT2 achieves similar
matching performance to RIFT while being much faster and having less memory
consumption. The source code will be made publicly available in
\url{https://github.com/LJY-RS/RIFT2-multimodal-matching-rotation}
