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.

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.

Haze and cloud seriously degrade the quality of optical remote sensing images, which largely decrease their interpretability and intelligibility. In this letter, we propose a two-stage haze and thin cloud removal method based on homomorphic filtering (HF) and sphere model improved dark channel prior (DCP). Compared with current dehazing methods, the most advantage of the proposed method is that our method can deal with uneven haze, thick haze, and thin cloud. We observe that haze and cloud are highly related to the illumination component and mainly located in the low frequency of an image. Thus, we adapt HF to enhance the haze image, which makes the distribution of haze more even. In the second stage, we analyze the drawback of DCP, i.e., the transmission estimated by DCP is very sensitive to noise. To draw this issue, we propose a novel sphere model to estimate a more accurate transmission map. The sphere model improved DCP is more suitable for thick haze images than the traditional DCP. Extensive experimental results show that the proposed method significantly outperforms the compared state-of-the-art methods. The source code and data sets used in the letter are made public.1

The Qinghai-Tibetan Plateau (QTP) is the highest plateau in the world. Under the background of global change, it is of unique significance to study the net primary productivity (NPP) of vegetation on the QTP. Based on the Google Earth Engine (GEE) cloud computing platform, the spatio-temporal variation characteristics of the NPP on the QTP from 2001 to 2017 were studied, and the impacts of climate change, elevation and human activity on the NPP in the QTP were discussed. The mean and trend of NPP over the QTP were “high in the southeast and low in the northwest” during 2001–2017. The trend of NPP was mostly between 0 gC·m⁻²·yr⁻¹ and 20 gC·m⁻²·yr⁻¹ (regional proportion: 80.3%), and the coefficient of variation (CV) of NPP was mainly below 0.16 (regional proportion: 89.7%). Therefore, NPP was relatively stable in most regions of the QTP. Among the correlation coefficients between NPP and temperature, precipitation and human activities, the positive correlation accounted for 81.1%, 48.6% and 56.5% of the QTP area, respectively. Among the two climatic factors, the influence of temperature on NPP was greater than that of precipitation. The change of human activities and the high temperature at low altitude had positive effects on the increase of NPP.