Remote Sensing Image Registration Method Research Articles

A remote sensing image registration method based on synchronous atmospheric correction

A remote sensing image registration method based on synchronous atmospheric correction

A High-Resolution Remote Sensing Image Registration Method Combining Object and Point Features

Point feature-based (PFB) remote sensing image registration methods have achieved great accomplishments. However, such methods based on the hand-crafted point features (HCPFs) have encountered limitations of robustness, accuracy, efficiency, and automation for high-resolution remote sensing (HRRS) image registration. A two-stage registration framework specialized for HRRS images, which combines object and point features is introduced in this article. Specifically, in the first stage, we propose to abstract and represent the macroscopic semantic objects in HRRS images as the deep convolutional object features (DCOFs), which is used to implement the global coarse registration. In the second stage, specific local areas are selected according to the DCOF matching, and the PFB method is performed in the local areas to achieve refinement. The salient points of this proposal include that, 1) the proposed DCOF is more discriminative and expressive than the HCPF at the macroscopic scale so as to effectively achieve automatic and robust global preregistration. 2) We implement the object feature extraction in an object detection fashion which is more-or-less plug and play. 3) Our designed two-stage strategy plays to the advantages of the DCOF and HCPF in different scenarios, thereby improving accuracy and efficiency. Finally, experiments demonstrate the effectiveness of our method. Compared with the state-of-the-art PFB methods, it improves the accuracy by about 3$\%$–6$\%$ and the efficiency by about 15$\%$–30$\%$.

A robust multimodal remote sensing image registration method and system using steerable filters with first- and second-order gradients

Co-registration of multimodal remote sensing (RS) images (e.g., optical, infrared, LiDAR, and SAR) is still an ongoing challenge because of nonlinear radiometric differences (NRD) and significant geometric distortions (e.g., scale and rotation changes) between these images. In this paper, a robust matching method based on the Steerable filters is proposed consisting of two critical steps. First, to address severe NRD, a novel structural descriptor named the Steerable Filters of first- and second-Order Channels (SFOC) is constructed, which combines the first- and second-order gradient information by using the steerable filters with a multi-scale strategy to depict more discriminative structure features of images. Then, a fast similarity measure is established called Fast Normalized Cross-Correlation (Fast-NCCSFOC), which employs the Fast Fourier Transform (FFT) technique and the integral image to improve the matching efficiency. Furthermore, to achieve reliable registration performance, a coarse-to-fine multimodal registration system is designed consisting of two pivotal modules. The local coarse registration is first conducted by involving both detection of interest points (IPs) and local geometric correction, which effectively utilizes the prior georeferencing information of RS images to address global geometric distortions. In the fine registration stage, the proposed SFOC is used to resist significant NRD, and to detect control points (CPs) between multimodal images by a template matching scheme. The performance of the proposed matching method has been evaluated with many different kinds of multimodal RS images. The results show its superior matching performance compared with the state-of-the-art methods. Moreover, the designed registration system also outperforms the popular commercial software (e.g., ENVI, ERDAS, and PCI) in both registration accuracy and computational efficiency. Our system is available at https://github.com/yeyuanxin110/SFOC-Multimodal_Remote_Sensing_Image_Registration_System.

Remote sensing image registration based on full convolution neural network and k-nearest neighbor ratio algorithm

Aiming at the problem of low accuracy of remote sensing image registration caused by the negative effects of noise and imaging in some traditional algorithms, an effective remote sensing image registration method based on depth feature from coarse to fine is proposed. In the coarse registration stage, the full convolution neural network is used to extract the features of the input image, then the nearest neighbor distance ratio algorithm is used to coarse match the features and finally an approximate transformation matrix is obtained. In the stage of fine registration, firstly, the image features are extracted by the improved convolutional neural network based on shortcut connection, then the affine transform coefficients are obtained by the combination of approximate transform matrix and k-nearest neighbor ratio algorithm. Finally, the image to be registered can be transformed according to the coefficients to achieve the purpose of registration. Experimental results show that, compared with the comparison method, the proposed method can increase the correct matching correspondence, so as to improve the accuracy of registration.

Dual-Features Student-t Distribution Mixture Model Based Remote Sensing Image Registration

In the work, we present a novel multiviewpoint and multitemporal remote sensing image registration method based on a dual-features Student- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> distribution mixture model (DSMM) under a variational Bayesian (VB) framework. The main contributions of the work are: 1) guided image filter (GIF) is adopted to smooth edges and strengthen ridges of images for heightening characteristics of feature point; 2) a Student- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> distribution mixture model (SMM) based DSMM designs a global and local descriptor to estimate correspondences from local to global scale; and 3) local structure constraints are designed to preserve relationships of neighbors of points and the scale of neighborhood structure of points to constrain transformation. The experimental results demonstrate the better performance of our DSMM against five state-of-the-art methods.

HSI-MSER: Hyperspectral Image Registration Algorithm Based on MSER and SIFT

Image alignment is an essential task in many applications of hyperspectral remote sensing images. Before any processing, the images must be registered. The Maximally Stable Extremal Regions (MSER) is a feature detection algorithm that extracts regions by thresholding the image at different grey levels. These extremal regions are invariant to image transformations making them ideal for registration. The Scale-Invariant Feature Transform (SIFT) is a well-known keypoint detector and descriptor based on the construction of a Gaussian scale-space. This article presents a hyperspectral remote sensing image registration method based on MSER for feature detection and SIFT for feature description. It efficiently exploits the information contained in the different spectral bands to improve the image alignment. The experimental results over nine hyperspectral images show that the proposed method achieves a higher number of correct registration cases using less computational resources than other hyperspectral registration methods. Results are evaluated in terms of accuracy of the registration and also in terms of execution time.

Remote Sensing Image Registration Based on Deep Learning Regression Model

We propose a novel remote sensing image registration method based on the deep learning regression network. Different from the traditional methods of feature extraction and feature matching, we pair the image blocks from sensed and reference images, and then directly learn the displacement parameters of the four corners of the sensed image block relative to the reference image. In addition, we develop the dual deep learning network with weight sharing to fully extract the registration pair image features. The proposed method is tested on different period Landsat-7 and WorldView-3 images and compared with scale-invariant feature transform (SIFT), fast and rotated brief (ORB), and other deep learning methods. The proposed method outperforms all the comparing methods.

Modality-Free Feature Detector and Descriptor for Multimodal Remote Sensing Image Registration

The nonlinear radiation distortions (NRD) among multimodal remote sensing images bring enormous challenges to image registration. The traditional feature-based registration methods commonly use the image intensity or gradient information to detect and describe the features that are sensitive to NRD. However, the nonlinear mapping of the corresponding features of the multimodal images often results in failure of the feature matching, as well as the image registration. In this paper, a modality-free multimodal remote sensing image registration method (SRIFT) is proposed for the registration of multimodal remote sensing images, which is invariant to scale, radiation, and rotation. In SRIFT, the nonlinear diffusion scale (NDS) space is first established to construct a multi-scale space. A local orientation and scale phase congruency (LOSPC) algorithm are then used so that the features of the images with NRD are mapped to establish a one-to-one correspondence, to obtain sufficiently stable key points. In the feature description stage, a rotation-invariant coordinate (RIC) system is adopted to build a descriptor, without requiring estimation of the main direction. The experiments undertaken in this study included one set of simulated data experiments and nine groups of experiments with different types of real multimodal remote sensing images with rotation and scale differences (including synthetic aperture radar (SAR)/optical, digital surface model (DSM)/optical, light detection and ranging (LiDAR) intensity/optical, near-infrared (NIR)/optical, short-wave infrared (SWIR)/optical, classification/optical, and map/optical image pairs), to test the proposed algorithm from both quantitative and qualitative aspects. The experimental results showed that the proposed method has strong robustness to NRD, being invariant to scale, radiation, and rotation, and the achieved registration precision was better than that of the state-of-the-art methods.

A Novel Harris Feature Detection-Based Registration for Remote Sensing Image

In view of the significant intensity difference between remote sensing image pairs, weak robustness, and insufficient key point correspondence, the novel remote sensing image registration method is proposed. Firstly, a nonlinear scale space is established by means of the anisotropic diffusion equation and fast explicit diffusion. Then, an improved gradient calculation method is used to calculate the gradient amplitude of the nonlinear scale-space image to establish the gradient amplitude space of the nonlinear scale space, and the multiscale Harris method is used to detect the feature points in the gradient amplitude space. The experimental results show that this feature extraction method can consider the boundaries and smoothness of objects and reduce the problem of gray-level difference to increase the number of feature points with potential of being correctly matched, and the distribution of feature points is relatively uniform. In addition, the improved gradient calculation method can effectively reduce the impact of nonlinear intensity differences on image registration. Overall, the algorithm can effectively solve the problem of registration difficulties caused by the significant grayscale difference between multisource remote sensing images and enhance the robustness. Compared with other advanced algorithms, this one has higher accuracy and more correct correspondence relations, and the registration performance has been significantly improved.

A Multitemporal Remote Sensing Image Registration Method Based on Water Bodies for the Lake-Rich Region

Accurate multitemporal remotely sensed image registration is essential for water body change detection and analysis of lake dynamics. However, lake-rich regions, such as Siberia, are mostly dominated by rivers and lakes, with few stable geometry features such as those typically used as control points for image registration. Lakes in Arctic regions are generally not static; their shorelines tend to expand and shrink seasonally, and may change substantially between different years, making it difficult to find consistent features for image registration. Consequently, traditional image-to-image registration methods, and even many sophisticated registration algorithms, rarely achieve accurate geometric correction in Arctic regions due to a lack of sufficient control points. In this article, we proposed a summary lake spatial attribute, the inferred deepest point of the lake (DPL), as a feature that is relatively insensitive to lake area changes, and therefore useful for registration of multitemporal images. The central focus of the DPL estimation algorithm is the identification of the largest inner circle (LIC) of the lake polygon. First, the internal Voronoi diagram of a lake polygon is calculated by the ``divide-and-conquer” method. The medial axis (MA) is then calculated by Voronoi diagram simplification, and finally, the LIC center is obtained by computing the distance from the MA intersection to all polygon edges. The approach was used to register HJ-1 and Landsat multispectral scanner (MSS) images in Siberia, where water bodies dominate the landscape and change significantly over time. The proposed method found a large number of control points from the extracted water bodies. Subpixel registration accuracy of 0.62 pixels (18.5 m) and 0.33 pixels (19.6 m) root mean square error (RMSE) was obtained for the HJ-1 and MSS images, respectively. In comparison, the alternative method of using lake centroids, only achieved 0.75 pixels (22.4 m) and 0.49 pixels (29.3 m) RMSE. This registration accuracy improvement is potentially important for large-scale regional cartography and change detection applications.

Remote Sensing Image Registration Based on Dynamic Threshold Calculation Strategy and Multiple-Feature Distance Fusion

Remote sensing image registration is widely used in civilian and military applications such as target recognition, environmental transformation monitoring, and military damage assessment. The severe outliers in the extraction of feature points caused by nonrigid transformation and viewpoint changes in the process of capturing remote sensing images increase the difficulty of registration. Therefore, we present a remote sensing image registration method based on dynamic threshold calculation strategy (DTCS) and multiple-feature distance fusion. The main idea of our approach is to maximize inliers while ensuring the optimal correspondence. First, DTCS gradually screens reliable inliers to reduce the negative effect of outliers over the iterations. The multiple-feature distance fusion Gaussian mixture model is then introduced to compensate for the defect of a single feature, and DTCS acting as the prior probability combines with the deterministic annealing to achieve the optimal mapping from local to global scale. Moreover, structure constraint based on local applying force is added into the global constraint to control the alignment of feature points more accurately in the overlapping area, so as to guide the subsequent image transformation. Extensive experiments show that our method performs better in most cases compared with nine state-of-the-art methods.

Secure Remote Sensing Image Registration Based on Compressed Sensing in Cloud Setting

High-resolution remote sensing image registration has recently been widely applied in many fields such as agriculture, forestry, and urban planning. A high-resolution remote sensing image can depict the texture features of ground details more clearly, but it also brings new challenges to registration such as texture similarity, image storage space, and image information leakage. In this paper, to solve the above problems, we propose a finite-state chaotic compressed sensing (CS) cloud remote sensing image registration method. First, chaotic CS with a finite state not only saves storage space required for an image but also improves the security of the image in the transmission process. Next, we process the image in the cloud platform, improve the speed of image processing, and facilitate real-time data processing. Finally, we propose an improved scale invariant feature transform (SIFT) image registration method based on local and global information (LG-SIFT) that reduces the impact of texture similarity on high-resolution remote sensing images. The experimental results show that the runtime of the original processing method is twice as long as that of the proposed scheme and that the accuracy of registration improves considerably.

Multi-source Remote Sensing Image Registration Based on Contourlet Transform and Multiple Feature Fusion

Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.

Multi-Temporal Remote Sensing Image Registration Using Deep Convolutional Features

Registration of multi-temporal remote sensing images has been widely applied in military and civilian fields, such as ground target identification, urban development assessment, and geographic change assessment. Ground surface change challenges feature point detection in amount and quality, which is a common dilemma faced by feature-based registration algorithms. Under severe appearance variation, detected feature points may contain a large proportion of outliers, whereas inliers may be inadequate and unevenly distributed. This paper presents a convolutional neural network (CNN) feature-based multi-temporal remote sensing image registration method with two key contributions: (i) we use a CNN to generate robust multi-scale feature descriptors and (ii) we design a gradually increasing selection of inliers to improve the robustness of feature point registration. Extensive experiments on feature matching and image registration are performed over a multi-temporal satellite image data set and a multi-temporal unmanned aerial vehicle image dataset. Our method outperforms four state-of-the-art methods in most scenarios.

Remote Sensing Image Registration Based on Phase Congruency Feature Detection and Spatial Constraint Matching

In this paper, a novel remote sensing image registration method based on phase congruency (PC) and spatial constraint is proposed. PC can provide intrinsic and meaningful image features, even when there are complex intensity changes or noise. Image features will be well detected from the corresponding PC images by the SAR-SIFT operator. It means that the feature detection methods in the frequency domain (PC) and the spatial domain (SAR-SIFT operator) are combined. To further improve the result of registration, spatial constraints, including point and line constraint, are established by utilizing the position and orientation information. Then, one to more matches can be removed and the influence of adjacent point can be greatly eliminated. The experimental results demonstrate that our method can obtain a better registration performance with higher accuracy and more correct correspondences than the state-of-the-art methods, such as SIFT, SAR-SIFT, SURF, PSO-SIFT, RIFT, and GLPM.

Remote Sensing Image Registration Using Multiple Image Features

Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

Remote Sensing Image Registration Based on Retrofitted SURF Algorithm and Trajectories Generated From Lissajous Figures

In this letter, we propose a novel remote sensing image registration method by optimizing the Speeded Up Robust Features (SURF) and developing a new similarity measure function based on trajectories generated from Lissajous figures. Compared with SURF which has a low feature-matching rate in some complex cases, the retrofitted SURF algorithm is more robust and accurate. The algorithm greatly improves the correct matching rate to over 80%. Furthermore, the recognition capability of the similarity measure is enhanced by using a trajectory disturbance strategy, which is a significant displacement in the trajectory induced by a minor error of the transformation parameters. Experiments show the promising performance of the proposed image registration method.