SAR image registration based on Susan algorithm

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.

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.

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.

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.

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.

Common feature representation in optical and synthetic aperture radar (SAR) image registration is one of the most challenging tasks due to the significant geometric and radiometric differences. This letter proposed a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prominent structure-guided feature (PSGF)</i> representation for SAR and optical image registration. Firstly, the prominent structure of the image is highlighted based on windowed inherent variations, which is conducive to identifying more accurate and reliable corresponding points. Secondly, the maximum response index filter banks are proposed to extract structure features with multi-orientation convolution results. Then the structure feature-guided representation generated from this filtering map is quantized in histograms. Finally, the descriptor with radiation invariance is employed for feature matching, enabling automatic image registration with high accuracy. Comparative analysis with state-of-the-art methods on diverse terrain data demonstrates the superiority of the proposed PSGF method for SAR and optical image registration.

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.

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.

The low information consistency and serious interference caused by synthetic aperture radar (SAR) imaging characteristics and target attributes affect the accuracy of SAR image information description, which reduces the performance of SAR image registration. In this work, the polar speeded up robust features (PSURF) descriptor is proposed for SAR image registration. In PSURF, according to the advantages of pole space in the relationship between pixels, a quantitative model of information change based on pole space is established to improve the integrity, consistency, and separability of image features. Firstly, the support region is transformed into polar coordinates. Then, the image gray level changes in the radial and angular directions are quantified with different scales. Finally, the PSURF descriptor based on the scale of keypoints is constructed covering the omnidirectional space.

Synthetic Aperture Radar (SAR) image time series play an important role in many applications. To construct pixel-level SAR image time series, we propose a locally adaptive image matching technique for the high-precision geometric registration of SAR images. The basic idea is to adapt the local characteristics of ground objects during the process of image registration. Then, by analyzing the spatial distribution of the error of each matched pair in the previous iteration, local areas are divided based on the local clustering of pairs with large errors. A new polynomial is then used to satisfy the local geometric constraint. Based on this proposed matching technique, we introduce a pixel-level SAR image time series modeling method. The experimental results show that the average geometric error of corresponding pixels in this algorithm is 0.073 pixels, while that of the NEST software is 0.242 pixels. The Pearson correlation coefficients of 20 pixels’ time series are above 0.85, indicating that the series bears high curve similarity and geometric precision, which suggests the proposed technique can provide high-quality SAR image time series.

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.

Although Synthetic Aperture Radar (SAR) can capture rich land cover information as a most important advanced technique in the field of international earth observation, the application effects still limited significantly. The reason is that the study of SAR imaging processing, SAR image processing and SAR applications are conducted respectively, the integrated study are lacked for parameter selection of SAR imaging processing and SAR image processing and targets identification of SAR application. Focusing on above science problems, the study for phase high fidelity model of high precision interferometric SAR, SAR non-stationary backscattering characteristics and cognition, SAR three-dimensional electromagnetic scattering modeling and its scattering mechanism and SAR multi-parameters optimization information extraction has been conducted. The typical natural distributed targets and man-made targets such as surface deformation, sea ice, building complex and collapsed building are selected, the method and application study oriented to SAR environmental parameters inversion are conduced, the main result and conclusion are as follows: (1) The overall theoretical frames of SAR information integrated processing was proposed; The simulation and ground validation system used to assess the effects of SAR information integrated processing was created; furthermore, the spaceborne SAR imaging algorithm with three steps focusing processing was proposed and realized the multi-modal integrated imaging processing and improved the imaging processing precision. (2) In terms of SAR information integrated processing of natural targets, focusing on phase high fidelity imaging processing of interferometric SAR surface deformation monitoring, an novel spaceborne SAR high fidelity simulation method based on stationary RCS and improved Goldstein SAR interferogram filter based on empirical mode decomposition were proposed. Focusing on multi-parameters optimization processing of sea ice types classification, the multi-channel spaceborne SAR sparse imaging method based on compressive sensing, Kalman filter for removal of scalloping and inter-scan banding in ScanSAR images and SVM sea ice classification method combined with sea ice concentration were proposed. (3) In terms of SAR information integrated processing of man-made targets, focusing on non-stationary backscattering from building complex, mitigation of azimuth ambiguities in spaceborne stripmap SAR iamges using selective restoration, SAR image despeckling by selective 3D filtering of multiple compressive reconstructed images and man-made target detection in urban areas based on a new azimuth stationary extraction method were proposed. Focusing on three-dimensional modeling and fast extraction of collapsed building, H-a-p method used for collapsed building extraction was proposed.

The usage of a hypersonic platform for remote sensing application has promising prospects, especially for hypersonic platform-borne synthetic aperture radar (SAR) imaging. However, the high-speed of hypersonic platform will lead to extreme friction between the platform and air, which will cause the ionization of air. The ionized gas forms the plasma sheath wrapped around the hypersonic platform. The plasma sheath will severely affect the propagation of SAR signal and further affect the SAR imaging. Therefore, hypersonic platform-borne SAR imaging should be studied from a physical perspective. In this paper, hypersonic platform-borne SAR imaging under plasma sheath is analyzed. The SAR signal propagation in plasma sheath is computed using scatter matrix method. The proposed SAR signal model is verified by using a ground experiment system. Moreover, the effect of attenuation caused by plasma sheath on SAR imaging is studied under different SAR parameters and plasma sheath. The result shows that attenuation caused by plasma sheath will degrade the SAR imaging quality and even cause the point and area targets to be submerged into the noise. The real SAR images under plasma sheath also illustrate this phenomenon. Furthermore, by studying imaging results under different SAR and plasma parameters, it can be concluded that the severe degradation of SAR imaging quality appears at condition of high plasma sheath electron density and low SAR carrier frequency. The work in this paper will be beneficial for the study of hypersonic platform-borne SAR imaging and design of hypersonic SAR imaging systems in the future.

In general, synthetic aperture radar (SAR) imaging and image processing are two sequential steps in SAR image processing. Due to the large size of SAR images, most image processing algorithms require image segmentation before processing. However, the existence of speckle noise in SAR images, as well as poor contrast and the uneven distribution of gray values in the same target, make SAR images difficult to segment. In order to facilitate the subsequent processing of SAR images, this paper proposes a new method that combines the back-projection algorithm (BPA) and a first-order gradient operator to enhance the edges of SAR images to overcome image segmentation problems. For complex-valued signals, the gradient operator was applied directly to the imaging process. The experimental results of simulated images and real images validate our proposed method. For the simulated scene, the supervised image segmentation evaluation indexes of our method have more than 1.18%, 11.2% and 11.72% improvement on probabilistic Rand index (PRI), variability index (VI), and global consistency error (GCE). The proposed imaging method will make SAR image segmentation and related applications easier.

Precise registration of synthetic aperture radar (SAR) images is a nontrivial task since a change in radar-acquisition geometry generates image shifts. In existing system, either the transformation functions are oversimplified, or external measures such as digital elevation model and flight track are required to be precise. In this paper, we proposed a generative Bayesian approach to modelling the displacement vectors that map the position of each pixel in the image, thus avoiding degradation of the transformation function. Rather than providing a point estimate for the transformation function, the proposed method yields a full posterior density function of the transformation function. Especially, the Bayesian model learns all the parameters adaptively, and the procedure is fully automatic. The proposed model is comparable in accuracy to state-of-the-art optical flow methods on the challenging Sintel benchmarks, and outperforms currently published SAR image registration methods on some real SAR data with critical scenes.