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The characteristics of wide swath spaceborne SAR orthophotos. (a) The rotation problem of azimuth and range direction not consistent with the orientation of the image row and column in Radarsat-2. (b) The rotation problem in Sentinel-1. (c) The mountain area in wide swath SAR orthophotos without GCPs, which impact the distribution of matching points. (d) The desert area in wide swath SAR orthophotos without GCPs.
Source publication
In producing orthophoto mosaic in a large area from spaceborne synthetic aperture radar (SAR) images, SAR image matching from adjacent orbits is a technical difficulty due to the speckle noise and different imaging mechanism between azimuth and range direction. In this paper, an area-based method, SAR-Moravec, is proposed for SAR orthophoto matchin...
Citations
... However, the complex P-NG algorithm is computationally inefficient and unsuitable for extensive study areas. Existing distortion-identification methods lack sufficient refinement in distortion classification, particularly in the detailed delineation of subtle distortions; additionally, few studies have thoroughly investigated the precision of such identifications and their impact on monitoring results from a foundational perspective [27][28][29]. ...
SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods—LSM, R-Index, and P-NG—against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings.
... In general, automatic image matching methods can be classified into two main categories of area-based and feature-based methods [16]. In the area-based image matching methods, the specific windows from the reference image are slid on the source image, and by measuring radiometric similarity/distinction between the matching windows in each position, their matched positions are recognised [17]. In the feature-based image matching methods, candidate features (e.g. ...
Range‐Doppler model is a transformation that relates ground and Single‐Look Complex SAR image spaces by knowing the instantaneous position and velocity of the sensor in its trajectory. Its instantaneous position and velocities are determined with the help of vectorial functions, which their precisions directly influence the accuracy of the range‐Doppler model. Ephemeris data is one of the sources used for the estimation of temporal functions. Some reasons can cause uncertainty in the temporal function directly derived from the satellite navigation data. Ground control points are other alternatives for calibration of the range‐Doppler model whose visual measurements are difficult in the mountainous areas of Sentinel‐1 Single‐Look Complex images due to their resolutions and distortions. The temporal functions of the range‐Doppler model are refined through numerous control points extracted from the automatic matching between Single‐Look Complex images and Digital Elevation Model. For this purpose, the radiometric and geometric similarities of the Single‐Look Complex images and Digital Elevation Model in mountainous areas are first increased based on the initial range‐Doppler model, and then their geometric discrepancies are compensated with the least‐squares image matching method. The results indicated that the proposed method in mountainous areas could automatically compensate for up to 30 pixels errors and reach sub‐pixel accuracies.
... The traditional point feature refers to the corner point because it contains the local gray feature. Moravec [7] first proposed the corner point extraction algorithm in 1977, Harris [8] improved the Moravec operator and then proposed the Harris operator [9]. SIFT [10] is one of the most widespread and effective feature-based matching methods which extracts feature points in the Gaussian scale space. ...
Traditional image feature matching methods cannot obtain satisfactory results for multi-modal images in most cases because different imaging mechanisms bring significant nonlinear radiation distortion differences and geometric distortion. The key to multi-modal image matching is trying to eliminate the nonlinear radiation distortion and extract more robust features. This paper proposes a new robust feature matching method for multi-modal images. Our method starts by detecting feature points on phase congruency maps in nonlinear scale space and then removing mismatches by progressive filtering. Specifically, the phase congruency maps are generated by the Log-Gabor filter. Then, the feature points on phase congruency maps are detected in nonlinear scale space constructed by the nonlinear diffusion filter. Subsequently, the structure descriptor is established by the Log-Gabor filter, and the initial correspondences are constructed by bilateral matching. Finally, an iterative strategy is used to remove mismatches by progressive filtering. We perform comparison experiments on our proposed method with the SIFT, RIFT, VFC, LLT, LPM, and mTopKPR methods using multi-modal images. The algorithms of each method are comprehensively evaluated both qualitatively and quantitatively. Our experimental results indicate the superiority of our method over the other six matching methods.
... Image registration plays an important role in computer vision. Image registration is widely used in many aspects such as image matching [1][2][3][4][5][6][7], change detection [8,9], 3D reconstruction [10][11][12], guidance [13][14][15], mapping sciences [16][17][18][19][20][21], and mobile robot [22,23]. In general, image registration methods can be mainly divided into two kinds: gray-scale matching methods and feature-based matching methods. ...
The Oriented FAST and Rotated BRIEF (ORB) algorithm has the problem that the extracted feature points are overconcentrated or even overlapped, leading to information loss of local image features. A homogenized ORB algorithm using dynamic thresholds and improved quadtree method is proposed in this paper, named Quadtree ORB (QTORB). In the feature point extraction stage, a new dynamic local threshold calculation method is proposed to enhance the algorithm’s ability to extract feature points at homogeneous regions. Then, a quadtree method is improved and adopted to manage and optimize feature points to eliminate those excessively concentrated and overlapping feature points. Meanwhile, in the feature points optimization process, different quadtree depths are set at different image pyramid levels to prevent excessive splitting of the quadtree and increase calculation speed. In the feature point description stage, local gray difference value information is introduced to enhance the saliency of the feature description. Finally, the Hamming distance is used to match points and RANSAC is used to avoid mismatches. Two datasets, namely, the optical image dataset and SAR image dataset, are used in the experiment. The experimental result shows that, considering accuracy and real-time efficiency, the QTORB can effectively improve the distribution uniformity of feature points.
Despite impressive progress in the past decade, accurate and efficient multi-view SAR image registration remains a challenging task due to complex imaging mechanisms and various imaging conditions. Especially for rugged areas, SAR images obtained from the opposite-side view reflect different characteristics, making popular SAR image registration methods no longer applicable. To this end, we propose a geometry-aware image registration method by extracting inherent orientation features and concentrating on geometry-invariant areas. First, slant range images are terrain-corrected using Digital Elevation Model (DEM) to reduce large relative positioning errors caused by elevation. Second, the Gabor-ratio detector is introduced to obtain multi-scale orientation features, which are more robust under various imaging conditions. Then, a geometry-aware mask is produced by intersecting the 3D space ray with DEM, and thus SAR images can be divided into three categories, layover, shadow, and geometry-invariant areas. The geometry-aware matching method, which focuses on geometry-invariant areas and masks out misleading caused by geometric and radiometric distortions, is proposed to realize accurate matching. The rational polynomial coefficients (RPCs) are refined to achieve relative correction. Extensive results on dozens of SAR images demonstrate the effectiveness and universality of the proposed algorithm by quantitative evaluation using man-made and natural corner reflectors. An analysis of the factors affecting registration accuracy is also discussed.
