  Traditional feature matching methods such as scale-invariant feature
transform (SIFT) usually use image intensity or gradient information to detect
and describe feature points; however, both intensity and gradient are sensitive
to nonlinear radiation distortions (NRD). To solve the problem, this paper
proposes a novel feature matching algorithm that is robust to large NRD. The
proposed method is called radiation-invariant feature transform (RIFT). There
are three main contributions in RIFT: first, RIFT uses phase congruency (PC)
instead of image intensity for feature point detection. RIFT considers both the
number and repeatability of feature points, and detects both corner points and
edge points on the PC map. Second, RIFT originally proposes a maximum index map
(MIM) for feature description. MIM is constructed from the log-Gabor
convolution sequence and is much more robust to NRD than traditional gradient
map. Thus, RIFT not only largely improves the stability of feature detection,
but also overcomes the limitation of gradient information for feature
description. Third, RIFT analyzes the inherent influence of rotations on the
values of MIM, and realizes rotation invariance. We use six different types of
multi-model image datasets to evaluate RIFT, including optical-optical,
infrared-optical, synthetic aperture radar (SAR)-optical, depth-optical,
map-optical, and day-night datasets. Experimental results show that RIFT is
much more superior to SIFT and SAR-SIFT. To the best of our knowledge, RIFT is
the first feature matching algorithm that can achieve good performance on all
the above-mentioned types of multi-model images. The source code of RIFT and
multi-modal remote sensing image datasets are made public .
