Prominent Structure-Guided Feature Representation for SAR and Optical Image Registration

The traditional template matching strategy of optical and synthetic aperture radar (SAR) is sensitive to the nonlinear transformation between two images. In some cases, the optical and SAR image pairs do not conform to the affine transformation condition. To address this issue, this study presents a novel template matching strategy which uses the One-Class Support Vector Machine (SVM) to remove outliers. First, we propose a method to construct the similarity map dataset using the SEN1-2 dataset for training the One-Class SVM. Second, a four-step strategy for optical and SAR image registration is presented in this paper. In the first step, the optical image is divided into some grids. In the second step, the strongest Harris response point is selected as the feature point in each grid. In the third step, we use Gaussian pyramid features of oriented gradients (GPOG) descriptor to calculate the similarity map in the search region. The trained One-Class SVM is used to remove outliers through similarity maps in the fourth step. Furthermore, the number of improve matches (NIM) and the rate of improve matches (RIM) are designed to measure the effect of image registration. Finally, this paper designs two experiments to prove that the proposed strategy can correctly select the matching points through similarity maps. The experimental results of the One-Class SVM in dataset show that the One-Class SVM can select the correct points in different datasets. The image registration results obtained on the second experiment show that the proposed strategy is robust to the nonlinear transformation between optical and SAR images.

As synthetic aperture radar (SAR) data is more and more popular in recent days, the fusion of SAR and optical image become inevitable and desirable problem. Image Registration is the fundamental for data fusion, and has been wildly studied. However, literature on registration between SAR and optical image remain little, which means the problem is still challenge and open. On one hand, the SAR and optical image are from totally different sensor with variant imaging mechanism. On the other hand, Simple features could not enough to maintain the accuracy and efficiency of multi-modal registration. We propose a new method for SAR and optical image registration. Different from current methods mainly based on intensity feature or point feature, our method matches two images by their boundary features. A globalized boundary detection algorithm are used for feature extraction. The extracted boundaries were then matched by coherence point drift algorithm. Experiments prove that our method could achieve pixel-level precision, and perform robust for different resolutions.

For Synthetic Aperture Radar (SAR) image registration, successive processes following feature extraction are required by both the traditional feature-based method and the deep learning method. Among these processes, the feature matching process—whose time and space complexity are related to the number of feature points extracted from sensed and reference images, as well as the dimension of feature descriptors—proves to be particularly time consuming. Additionally, the successive processes introduce data sharing and memory occupancy issues, requiring an elaborate design to prevent memory leaks. To address these challenges, this paper introduces the OptionEM-based reinforcement learning framework to achieve end-to-end SAR image registration. This framework outputs registered images directly without requiring feature matching and the calculation of the transformation matrix, leading to significant processing time savings. The Transformer architecture is employed to learn image features, while a correlation network is introduced to learn the correlation and transformation matrix between image pairs. Reinforcement learning, as a decision process, can dynamically correct errors, making it more-efficient and -robust compared to supervised learning mechanisms such as deep learning. We present a hierarchical reinforcement learning framework combined with Episodic Memory to mitigate the inherent problem of invalid exploration in generalized reinforcement learning algorithms. This approach effectively combines coarse and fine registration, further enhancing training efficiency. Experiments conducted on three sets of SAR images, acquired by TerraSAR-X and Sentinel-1A, demonstrated that the proposed method’s average runtime is sub-second, achieving subpixel registration accuracy.

An Improved Anisotropic Gaussian-Scale Invariant Feature Transform (IAG-SIFT) algorithm is proposed to register the Synthetic Aperture Radar (SAR) images. The standard SIFT algorithm generates isotropic Gaussian scale space which blurs lots of details in the image. The Anisotropic Scale Space (ASS) can preserve more details than the isotropic one. However, in many SAR registration methods, the gradient calculation of the ASS is performed by using the simple differential equation which is not appropriate for SAR images. As the SAR images contain multiplicative speckle noise, simple difference operation is not suitable for gradient calculation. It can reduce the number of correct matches in SAR image registration. So, in this paper, a Gaussian-Gamma-Shaped Bi-Window (GGS-BW) is used for ratio based gradient computation which is very effective for SAR images. The proposed IAG-SIFT method significantly improves the matching performance between the SAR images. The Experimental results show the effectiveness of proposed IAG-SIFT algorithm in SAR image registration.

The equator’s unique combination of high humidity and temperature renders optical satellite imagery highly susceptible to persistent cloud cover. In contrast, synthetic aperture radar (SAR) offers a robust alternative due to its ability to penetrate clouds with microwave imaging. This study addresses the challenges of cloud-induced data gaps and cross-sensor geometric biases by proposing an advanced optical and SAR image-matching framework specifically designed for cloud-prone equatorial regions. We use a prompt-driven visual segmentation model with automatic prompt point generation to produce cloud masks that guide cross-modal feature-matching and joint adjustment of optical and SAR data. This process results in a comprehensive digital orthophoto map (DOM) with high geometric consistency, retaining the fine spatial detail of optical data and the all-weather reliability of SAR. We validate our approach across four equatorial regions using five satellite platforms with varying spatial resolutions and revisit intervals. Even in areas with more than 50 percent cloud cover, our method maintains sub-pixel edging accuracy under manual check points and delivers comprehensive DOM products, establishing a reliable foundation for downstream environmental monitoring and ecosystem analysis.

Synthetic aperture radar (SAR) imaging has been widely applied in drones for its consistent surveillance capability and invulnerability to smoke or cloud. Yet drone platform is unstable and observing distance is much shorter than satellite. As a consequence, the SAR image registration, which is a crucial process in SAR imaging, is influenced by sparkling scattering spots and coherent speckle noise. In this paper, we propose a multi-layer method to improve SAR registration performance on drones. ORB method is applied to mitigate sparkle scattering spot. Multi-layer feature clustering can eliminate the error matches by coherent speckle noise. Experiments were carried out to valid the proposed method. Results demonstrate that the proposed method can improve SAR image registration performance on unstable aerial platforms.

We address the application of model-supported exploitation techniques to synthetic aperture radar (SAR) imagery. The emphasis is on monitoring SAR imagery using wide area 2D and/or 3D site models along with contextual information. We consider here the following tasks useful in monitoring: (a) site model construction using segmentation and labeling techniques, (b) target detection, (c) target classification and indexing, and (d) SAR image-site model registration. The 2-D wide area site models used here for SAR image exploitation differ from typical site models developed for RADIUS applications, in that they do not model specific facilities, but constitute wide area site models of cultural features such as urban clutter areas, roads, clearings, fields, etc. These models may be derived directly from existing site models, possibly constructed from electro-optical (EO) observations. When such models are not available, a set of segmentation and labeling techniques described here can be used for the construction of 2D site models. The use of models can potentially yield critical information which can disambiguate target signatures in SAR images. We address registration of SAR and EO images to a common site model. Specific derivations are given for the case of registration within the RCDE platform. We suggest a constant false alarm rate (CFAR) detection scheme and a topographic primal sketch (TPS) based classification scheme for monitoring target occurrences in SAR images. The TPS of an observed target is matched against candidate targets TPSs synthesized for the preferred target orientation, inferred from context (e.g. road or parking lot targets). Experimental results on real and synthetic SAR images are provided.

Optical and SAR images have different imaging modes and pixel expression formats, which caused much difficult for image registration. The paper proposes a registration algorithm combined Ratio gradient and Cross Cumulative Residual Entropy (CCRE) aimed at solving the problem of SAR image speckles. Compared with other traditional methods, the experiment results show that the CCRE combined with Ratio operator registration method performs satisfactorily in SAR and optical image registration and provides a significant improvement on the registration accuracy over the other algorithm

Optical and SAR images have different imaging modes and pixel expression formats, which caused much difficult for image registration. The paper proposes a registration algorithm combined Ratio gradient and Cross Cumulative Residual Entropy (CCRE) aimed at solving the problem of SAR image speckles. Compared with other traditional methods, the experiment results show that the CCRE combined with Ratio operator registration method performs satisfactorily in SAR and optical image registration and provides a significant improvement on the registration accuracy over the other algorithm

Availability of the sufficient number of matching pairs is the major challenge for optical to SAR (Synthetic Aperture Radar) image registration. Recently, a distinctive order based self-similarity operator (DOBSS) has been proposed to register the optical images. In this paper, a modified version of the distinctive order based self-similarity (M-DOBSS) is proposed to register the optical and SAR images. The proposed M-DOBSS provides more distinctiveness than the standard DOBSS operator. It can significantly increase the number of matching pairs between the optical and SAR images. Experiments on different sets of optical-SAR image pairs demonstrates the effectiveness of the proposed method.

Accurate optical and synthetic aperture radar (SAR) image registration is crucial to multisensor remote sensing applications. Though several algorithms have been proposed, the practical implementation that directly matches large and high-resolution optical and SAR images remains underexplored by the community. Equipped with the satellite positioning parameters, optical and SAR images can be roughly registered based on geographic coordinates. However, the relative positioning accuracy is still dozens, even hundreds of pixels due to the inaccuracies of sensor parameters and elevation. Consequently, we propose a robust registration algorithm, which consists of two stages, where the horizontal positioning errors can be reduced in the first stage, and then, we fine-tune the correspondences in the second stage. Specifically, we propose a novel template matching method based on the dilated convolutional feature (DCF) and epipolar-oriented phase correlation. DCF is constructed by a depthwise-separable dilated convolution with multichannel gradients, which are generated by the Sobel operator for optical images and a ratio of exponentially weighted averages (ROEWA) operator for SAR images. Due to the large reception field of dilated convolution, DCF can retain invariance even for large relative positioning errors. The epipolar-oriented rectangle template, which stretches along the epipolar line, is then proposed to capture more overlapping areas compared to square templates. Furthermore, the outlier removal is implemented in the coordinate system of optical images to avoid the effect of range compression in SAR images. Inliers are finally used to refine the rational polynomial coefficients (RPCs) based on the bundle adjustment technique. Experimental results on high-resolution optical and SAR image products of various scenarios demonstrate the effectiveness of the proposed registration framework. The relative positioning errors of the refined RPCs can be reduced from hundreds of pixels to the subpixel level.

The main difficulty of synthetic aperture radar (SAR)-optical image matching or registration lies in the significant heterogeneous characteristics introduced by the different imaging mechanisms between SAR and optical images. Instead of directly using the raw image pair, transforming the pair into a feature domain, where they have homogeneous feature representation, is believed more effective. Inspired by image segmentation, we develop an end-to-end deep learning model for the SAR-optical matching, based on a siamese U-net with a fast Fourier transform (FFT) correlation layer. First, the siamese U-net with sharing weights extracts the feature maps of the SAR and optical images and projects the heterogeneous images into a homogeneous space. Then, the two feature maps are cross-correlated or normalized cross-correlated by the FFT layer and a similarity heatmap is obtained. Finally, the heatmap is send into a softmax2d classifier to determine the best matching, and thus matching is converted into classification. The nonlinear mapping capability of deep learning can well tackle the intensity variation across the different imaging modals; the encoder–decoder architecture with skip connections in the U-net can take full advantage of the global information and simultaneously preserve the local resolution and position information and thus guarantees high accuracy and robustness; besides, the FFT correlation is helpful for the efficiency improvement and training with large image pairs. Experiments show that the proposed method can achieve a pixel-level matching error.

Synthetic Aperture Radar (SAR) is an active remote sensing system which can be installed on aircraft, satellite and other carriers with the advantages of all day and night and all-weather ability. It is the important problem that how to deal with SAR and extract information reasonably and efficiently. Particularly SAR image geometric correction is the bottleneck to impede the application of SAR. In this paper we introduces image registration and the Susan algorithm knowledge firstly, then introduces the process of SAR image registration based on Susan algorithm and finally presents experimental results of SAR image registration. The Experiment shows that this method is effective and applicable, no matter from calculating the time or from the calculation accuracy.

Synthetic Aperture Radar (SAR) images are efficient and reliable source of information in extraction of damaged regions in case of floods. In assessment of damage accurately due to floods, image registration of optical (before-flood) and SAR images (after-flood) has to be carried out efficiently. In this paper, we discuss a robust multi-sensor image registration algorithm using scale invariant feature points for keypoint extraction. For matching the keypoints, a multi-objective genetic algorithm is developed with angle, distance and vicinity criterion as the fitness functions. This optimization process helps in matching the scale invariant feature points. From the obtained results, the performance of the image registration is evaluated and it is concluded that the proposed approach is efficient.

This letter presents an effective approach for synthetic aperture radar (SAR) image registration. Corresponding edge features are extracted by exploring the local spatial relationship around corresponding speeded-up robust feature points. Only edge pairs that are matched in the aspect of distance and orientation to the center feature points are taken as effective edge features. For edge-point set matching, we introduce the coherent point drift (CPD) registration method. CPD is able to maintain the global topological structure of edges and is robust to outliers. We refine the original CPD by taking our confidence on edge points as prior information. A new objective function is deduced, resulting in a better registration performance. Experimental results show that the proposed method performs well in terms of registration robustness and accuracy.