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Traditional feature matching methods such as scale-invariant featuretransform (SIFT) usually use image intensity or gradient information to detectand describe feature points; however, both intensity and gradient are sensitiveto nonlinear radiation distortions (NRD). To solve the problem, this paperproposes a novel feature matching algorithm that is robust to large NRD. Theproposed method is called radiation-invariant feature transform (RIFT). Thereare three main contributions in RIFT: first, RIFT uses phase congruency (PC)instead of image intensity for feature point detection. RIFT considers both thenumber and repeatability of feature points, and detects both corner points andedge points on the PC map. Second, RIFT originally proposes a maximum index map(MIM) for feature description. MIM is constructed from the log-Gaborconvolution sequence and is much more robust to NRD than traditional gradientmap. Thus, RIFT not only largely improves the stability of feature detection,but also overcomes the limitation of gradient information for featuredescription. Third, RIFT analyzes the inherent influence of rotations on thevalues of MIM, and realizes rotation invariance. We use six different types ofmulti-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 ismuch more superior to SIFT and SAR-SIFT. To the best of our knowledge, RIFT isthe first feature matching algorithm that can achieve good performance on allthe above-mentioned types of multi-model images. The source code of RIFT andmulti-modal remote sensing image datasets are made public .

RIFT: Multi-modal Image Matching Based on Radiation-invariant Feature Transform