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 this problem, this paper proposes a novel feature matching algorithm that is robust to large NRD. The proposed method is called radiation-variation insensitive 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. The 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 analyses the inherent influence of rotations on the values of the MIM and realises rotation invariance. We use six different types of multi-modal 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 superior to SIFT and SAR-SIFT on multi-modal images. To the best of our knowledge, RIFT is the first feature matching algorithm that can achieve good performance on all the abovementioned types of multi-modal images. The source code of RIFT and the multi-modal image datasets are publicly available1.

Traditional image feature matching methods cannot obtain satisfactory results for multi-modal remote sensing images (MRSIs) in most cases because different imaging mechanisms bring significant nonlinear radiation distortion differences (NRD) and complicated geometric distortion. The key to MRSI matching is trying to weakening or eliminating the NRD and extract more edge features. This paper introduces a new robust MRSI matching method based on co-occurrence filter (CoF) space matching (CoFSM). Our algorithm has three steps: (1) a new co-occurrence scale space based on CoF is constructed, and the feature points in the new scale space are extracted by the optimized image gradient; (2) the gradient location and orientation histogram algorithm is used to construct a 152-dimensional log-polar descriptor, which makes the multi-modal image description more robust; and (3) a position-optimized Euclidean distance function is established, which is used to calculate the displacement error of the feature points in the horizontal and vertical directions to optimize the matching distance function. The optimization results then are rematched, and the outliers are eliminated using a fast sample consensus algorithm. We performed comparison experiments on our CoFSM method with the scale-invariant feature transform (SIFT), upright-SIFT, PSO-SIFT, and radiation-variation insensitive feature transform (RIFT) methods using a multi-modal image dataset. The algorithms of each method were comprehensively evaluated both qualitatively and quantitatively. Our experimental results show that our proposed CoFSM method can obtain satisfactory results both in the number of corresponding points and the accuracy of its root mean square error. The average number of obtained matches is namely 489.52 of CoFSM, and 412.52 of RIFT. As mentioned earlier, the matching effect of the proposed method was significantly greater than the three state-of-art methods. Our proposed CoFSM method achieved good effectiveness and robustness. Executable programs of CoFSM and MRSI datasets are published: https://skyearth.org/publication/project/CoFSM/

Severe nonlinear radiation distortion (NRD) is the bottleneck problem of multimodal image matching. Although many efforts have been made in the past few years, such as the radiation-variation insensitive feature transform (RIFT) and the histogram of orientated phase congruency (HOPC), almost all these methods are based on frequency-domain information that suffers from high computational overhead and memory footprint. In this article, we propose a simple but very effective multimodal feature matching algorithm in the spatial domain, called locally normalized image feature transform (LNIFT). We first propose a local normalization filter to convert original images into normalized images for feature detection and description, which largely reduces the NRD between multimodal images. We demonstrate that normalized matching pairs have a much larger correlation coefficient than the original ones. We then detect oriented FAST and rotated brief (ORB) keypoints on the normalized images and use an adaptive nonmaximal suppression (ANMS) strategy to improve the distribution of keypoints. We also describe keypoints on the normalized images based on a histogram of oriented gradient (HOG), such as a descriptor. Our LNIFT achieves rotation invariance the same as ORB without any additional computational overhead. Thus, LNIFT can be performed in near real-time on images with 1024 $\times 1024$ pixels (only costs 0.32 s with 2500 keypoints). Four multimodal image datasets with a total of 4000 matching pairs are used for comprehensive evaluations, including synthetic aperture radar (SAR)–optical, infrared–optical, and depth–optical datasets. Experimental results show that LNIFT is far superior to RIFT in terms of efficiency (0.49 s versus 47.8 s on a $1024 \times 1024$ image), success rate (99.9% versus 79.85%), and number of correct matches (309 versus 119). The source code and datasets will be publicly available at https://ljy-rs.github.io/web .

Point cloud registration (PCR) is an important task in photogrammetry and remote sensing, whose goal is to seek a seven-parameter similarity transformation to register a pair of point clouds. Traditional iterative closest point (ICP) variants highly rely on the initial parameters, and most of them cannot deal with cross-source (multisource) point clouds with scale changes. In this article, we propose a flexible correspondence-based PCR method, which is initial-guess free, fast, and robust. We first decompose the full seven-parameter registration problem into three subproblems, i.e., scale, rotation, and translation estimations, based on line vectors. Then, we propose a one-point random sample consensus (RANSAC) algorithm to estimate the scale and translation parameters. For the rotation estimation, we introduce a graduated optimization strategy into Tukey’s biweight function and propose a scale-annealing biweight estimator. We evaluate the proposed method on both same-source and cross-source data. Results show that the proposed method is robust against over 99% outliers and is one to two orders of magnitude faster than its competitors. The source code of our method will be made public.