Ratio Of Exponentially Weighted Averages Research Articles

OMIRD: Orientated Modality Independent Region Descriptor for Optical-to-SAR Image Matching

Image matching between optical and SAR images remains challenging due to their different imaging principles. Despite recent developments in image matching methods for optical-to-SAR images, few have seen practical application success due to the significant geometric and radiometric differences between the two modalities. In this letter, we propose a new method called the Orientated Modality Independent Region Descriptor (OMIRD) to overcome these challenges. Our template-based approach effectively extracts consistent structural and textural features from both optical and SAR images. Additionally, we introduce a principal orientation selection method, using the Ratio of Exponentially Weighted Averages (ROEWA) and logarithm Histogram of Gradient (HOG) to identify the most meaningful feature orientations and further simplify the OMIRD descriptor. The experimental results indicate that the proposed OMIRD exhibits superior matching accuracy and robustness under various geometric distortions and non-linear grayscale variations.

A Fast Registration Method for Optical and SAR Images Based on SRAWG Feature Description

Due to differences in synthetic aperture radar (SAR) and optical imaging modes, there is a considerable degree of nonlinear intensity difference (NID) and geometric difference between the two images. The SAR image is also accompanied by strong multiplicative speckle noise. These phenomena lead to what is known as a challenging task to register optical and SAR images. With the development of remote sensing technology, both optical and SAR images equipped with sensor positioning parameters can be roughly registered according to geographic coordinates in advance. However, due to the inaccuracy of sensor parameters, the relative positioning accuracy is still as high as tens or even hundreds of pixels. This paper proposes a fast co-registration method including 3D dense feature description based on a single-scale Sobel and the ratio of exponentially weighted averages (ROEWA) combined with the angle-weighted gradient (SRAWG), overlapping template merging, and non-maxima suppressed template search. In order to more accurately describe the structural features of the image, the single-scale Sobel and ROEWA operators are used to calculate the gradients of optical and SAR images, respectively. On this basis, the 3 × 3 neighborhood angle-weighted gradients of each pixel are fused to form a pixel-wise 3D dense feature description. Aiming at the repeated feature description in the overlapping template and the multi-peak problem on the search surface, this paper adopts the template search strategy of overlapping template merging and non-maximum suppression. The registration results obtained on seven pairs of test images show that the proposed method has significant advantages over state-of-the-art methods in terms of comprehensive registration accuracy and efficiency.

High-Precision Pixelwise SAR–Optical Image Registration via Flow Fusion Estimation Based on an Attention Mechanism

Due to the severe speckle noise and complex local deformation in synthetic aperture radar (SAR) images, the problem of high-precision pixelwise registration (dense registration) between SAR and optical images remains far from resolved. In this paper, an attention-mechanism-based optical flow fusion algorithm is proposed to achieve high-precision dense SAR-optical image registration. First, two descriptors, the scale-invariant feature transform (SIFT) and a descriptor based on phase congruency (PC), are used to describe SAR and optical images to eliminate their intensity differences. Then, the saliency feature map is extracted as a query matrix to weight the optical flow energy function. When extracting the saliency feature map, the Contour Robuste d'Ordre Non Entier (CRONE) and the ratio of exponentially weighted averages (ROEWA) operator are used to eliminate additive and multiplicative noise in the optical and SAR images, respectively. Finally, the optical flow fields based on SIFT and the PC-based descriptor are fused to compensate for registration ambiguity. Experimental results show that our method is feasible, effective and robust to noise, it enables high-precision registration under local deformation.

A Robust Two-Stage Registration Algorithm for Large Optical and SAR Images

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.

Image Fusion Algorithm at Pixel Level Based on Edge Detection.

In the present scenario, image fusion is utilized at a large level for various applications. But, the techniques and algorithms are cumbersome and time-consuming. So, aiming at the problems of low efficiency, long running time, missing image detail information, and poor image fusion, the image fusion algorithm at pixel level based on edge detection is proposed. The improved ROEWA (Ratio of Exponentially Weighted Averages) operator is used to detect the edge of the image. The variable precision fitting algorithm and edge curvature change are used to extract the feature line of the image edge and edge angle point of the feature to improve the stability of image fusion. According to the information and characteristics of the high-frequency region and low-frequency region, different image fusion rules are set. To cope with the high-frequency area, the local energy weighted fusion approach based on edge information is utilized. The low-frequency region is processed by merging the region energy with the weighting factor, and the fusion results of the high findings demonstrate that the image fusion technique presented in this work increases the resolution by 1.23 and 1.01, respectively, when compared to the two standard approaches. When compared to the two standard approaches, the experimental results show that the proposed algorithm can effectively reduce the lack of image information. The sharpness and information entropy of the fused image are higher than the experimental comparison method, and the running time is shorter and has better robustness.

Universal SAR and optical image registration via a novel SIFT framework based on nonlinear diffusion and a polar spatial-frequency descriptor

Due to severe speckle noise in synthetic aperture radar (SAR) images and the large nonlinear intensity differences between SAR and optical images, the registration of SAR and optical images is a challenging problem that remains to be solved. In this paper, an improved nonlinear scale-invariant feature transform (SIFT)-framework-based algorithm that combines spatial feature detection with local frequency-domain description for the registration of SAR and optical images is proposed. First, multiscale representations of the SAR and optical images are constructed based on nonlinear diffusion to better preserve edges and obtain consistent edge information. The ratio of exponentially weighted averages (ROEWA) operator and the Sobel operator are utilized in the process of scale space construction to calculate consistent gradient information. Then, a new feature detection strategy based on the Harris–Laplace ROEWA and Harris–Laplace Sobel techniques is proposed to detect stable and repeatable keypoints in the scale space. Finally, a novel descriptor, called the rotation-invariant amplitudes of log-Gabor orientation histograms (RI-ALGH), and a simplified version, ALGH, are proposed. The proposed descriptors are built based on the amplitudes of multiscale and multiorientation log-Gabor responses and utilize an improved spatial structure of the gradient location and orientation histogram (GLOH) descriptor, which is robust to local distortions. The experimental results on both simulated and real images demonstrate that the proposed method can achieve better results than other state-of-the-art methods in terms of registration accuracy.

Unsupervised Multiregion Partitioning of Fully Polarimetric SAR Images With Advanced Fuzzy Active Contours

This article proposes an unsupervised multiregion segmentation method for fully polarimetric synthetic aperture radar (polSAR) images based on the improved fuzzy active contour model. Different from most of the active contour models that are based on the utilization of only statistical information, the proposed method makes better use of information from polarimetric data. In addition to the statistical information, an edge detector modified from the ratio of exponentially weighted averages (ROEWA) operator, a sliding window algorithm for the total received power, and a ratio operator with respect to scattering mechanisms are integrated to the proposed active contour model. We then present a layer-based fuzzy active contour framework to solve our model. The general fuzzy active contour framework is computationally much more efficient compared with the level set-based framework; however, it cannot be applied to the multiregion segmentation of SAR images due to its low robustness to strong noise. The proposed approach includes the advantages of the general fuzzy active contour framework and has good robustness. Using two fully polSAR images demonstrates that the proposed method can achieve higher efficiency and a better segmentation performance in comparison with the commonly used active contour methods.

Improved ROEWA SAR Image Edge Detector Based on Curvilinear Structures Extraction

By introducing curvilinear structures extraction (CSE) instead of watershed algorithm (WA) or nonmaximum suppression (NMS) to edge strength map (ESM), an improved ratio of exponentially weighted averages (ROEWA) edge detector with better capacity for weak edges detection is proposed to extract smooth edges and edge direction of synthetic aperture radar (SAR) images. Using the ROEWA, the ESM is calculated. Then the CSE algorithm is employed to extracted edges, by acquiring eigenvectors and eigenvalues of the Hessian matrix, the improved ESM (IESM) is obtained, which ensures the good weak edge detection capacity and smoothness of edges. Experimental results on simulated and real SAR images show that the improved ROEWA based on CSE attains better performance than the one using WA or NMS.

Adaptive pseudo-p-norm regularization based De-speckling of SAR images

ABSTRACTA novel variational model is proposed to de-speckle the multiplication noise in the synthetic aperture radar (SAR) image to provide a high-quality interpretation of SAR data. The model consists of the data fidelity term and the adaptive pseudo-p-norm regularization term. The data fidelity term ensures the convexity of the model and avoids the nonlinear image transformation. With an improved ratio of exponentially weighted averages (ROEWA) operator and the edge detection image, the exponent value of the pseudo-p-norm regularization term varies with the intensity of the edge adaptively, which results in a better retention of the image geometric structure while removing the speckle noise. Compared with the total variation (TV) regularization, the staircase-like artifacts can be restrained more effectively. Experiments demonstrate that the proposed method are more competitive both visually and quantitatively than that from the existing nonlocal mean-based and variational-based methods.

Edge Detection Algorithm for SAR Image Based on Enhanced ROEWA

Researchers generally consider that Ratio Of Exponentially Weighted Averages (ROEWA) can not calculate the edge directions of SAR images. Therefore, some directional filters are used to add directions to ROEWA. In this paper, an Enhanced ROEWA (EROEWA) algorithm is proposed. Through the further derivation of the ROEWA algorithm convolution process, the pixel-level observation formula of ROEWA algorithm is obtained. First, a new convolution strategy is used to decouple the ROEWA formula to obtain exponentially weighted averages over the four directions. Second, the SAR images are rotated by 45 and exponentially weighted averages of the four additional directions are calculated. Finally, exponentially weighted averages of eight directions are expressed as eight vectors, and edge intensity and direction are solved by vector synthesis. Experimental results show that the EROEWA has a significant enhancement effect on the edge intensity and the direction.

A Multi-Region Segmentation Method for SAR Images based on the Multi-Texture Model with Level Sets.

Synthetic Aperture Radar (SAR) image segmentation is a difficult problem due to the presence of strong multiplicative noise. To attain multi-region segmentation for SAR images, this paper presents a parametric segmentation method based on the multi-texture model with level sets. Segmentation is achieved by solving level set functions obtained from minimizing the proposed energy functional. To fully utilize image information, edge feature and region information are both included in the energy functional. For the need of level set evolution, the Ratio of Exponentially Weighted Averages (ROEWA) operator is modified to obtain edge feature. Region information is obtained by the Improved Edgeworth Series Expansion (IESE), which can adaptively model a SAR image distribution with respect to various kinds of regions. The performance of the proposed method is verified by three high resolution SAR images. The experimental results demonstrate that SAR images can be segmented into multiple regions accurately without any speckle pre-processing steps by the proposed method.

Refined segmentation of ship target in SAR images based on GVF snake with elliptical constraint

ABSTRACTOcean surveillance is an important application of synthetic aperture radar (SAR) image interpretation. In the ocean, the ship target usually attracts the attention of researchers, hence many interpretation works focus on it. The precise ship segmentation can facilitate further processing, such as feature extraction and target recognition. This paper proposes a refined segmentation method for ship target, in which the ratio of exponentially weighted averages (ROEWA) operator is used to produce the edge map and the elliptical constraint (EC) is incorporated into the energy function of the gradient vector flow (GVF) snake model to extract the ship target precisely. With this prior shape constraint, the snake contour can overcome unexpected distortions stemming from speckle noise and side lobe effect, etc. experimental results based on TerraSAR X-band and RADARSAT-2 C-band data illustrate the effective performance of the proposed method on refined segmentation for ship targets.

BUILDING HEIGHT ESTIMATION FROM HIGH RESOLUTION SAR IMAGERY VIA MODEL-BASED GEOMETRICAL STRUCTURE PREDICTION

Height extraction by radar remote sensing is an attractive issue for the building detection and recognition. According to the analysis on the building geometrical properties in the SAR imagery, a novel height estimation algorithm is proposed following a model-based geometrical structure prediction and matching strategy. The range Doppler equation is introduced and simplified for the building 2D geometrical structure prediction in the slant image plane. An evaluation function implementing the ratio of exponentially weighted averages (ROEWA) is also established for the matching between the predicted structure and the observed SAR image. By incorporating the genetic algorithm (GA), the evaluation function is maximized to get the optimal height parameter. The experimental results with the simulated and real airborne and spaceborne SAR images show that the proposed method could efficiently estimate building height from single SAR imagery, and achieve better performance than two popular algorithms with the partial occlusion case.

A High Performance Edge Point Feature Match Method of SAR Images

摘要: 针对合成孔径雷达（Synthetic aperture radar，SAR）图像特征匹配中特征提取的不稳定性和相似度优化搜索的复杂性问题，提出了一种精确高效稳健的SAR图像边缘点集匹配方法. 首先，分析了仿射变换模型在遥感图像匹配中的适应性，并对仿射变换模型进行了参数分解；其次，提出了基于方向模板的SAR图像边缘检测算子，并利用SAR图像边缘的梯度和方向特征，建立了基于像素迁移的多源SAR边缘点集相似性匹配准则，以及图像匹配的联合相似度-联合特征均方和（Square summation joint feature，SSJF）；然后，利用改进的遗传算法（Genetic algorithm，GA）来进行相似度的全局极值优化搜索，获取变换模型参数和边缘点集的对应关系；最后，从理论上分析了本文方法的性能，并利用多幅SAR图像的匹配实验以及与原有方法的对比分析，对本文方法的性能进行了验证. 关键词: 合成孔径雷达图像匹配 / 仿射变换模型 / 参数分解 / 像素迁移 / 联合相似测度 / 遗传算法

SAR Image Segmentation Based on Fuzzy Region Competition Method and Gamma Model

In this paper, we present a novel variational framework for multiphase synthetic aperture radar (SAR) image segmentation based on the fuzzy region competition method. A new energy functional is proposed to integrate the Gamma model and the edge detector based on the ratio of exponentially weighted averages (ROEWA) operator within the optimization process. To solve the optimization problem efficiently, the functional is firstly modified to be convex and differentiable by using the fuzzy membership functions. And then the constrained optimization problem is converted to an unconstrained one by using the variable splitting techniques and the augmented Lagrangian method (ALM). Finally the energy is minimized with an alternative iterative minimization algorithm. The effectiveness of our proposed algorithm is validated by experiments on both synthetic and real SAR images.

Bionic vision-based synthetic aperture radar image edge detection method in non-subsampled contourlet transform domain

For synthetic aperture radar (SAR) images, traditional edge detection methods can hardly extract the complete and true edges since they are sensitive to noise. In this study, the authors propose an edge detection method based on bionic vision in non-subsampled contourlet transform (NSCT) domain for SAR images, which makes use of the characteristics of NSCT (e.g. multiscale, multidirection edge expression and outstanding edge location) and the mechanisms of strengthening and positioning the image edge by fixational eye movements. First, source images are decomposed in the NSCT domain. The minimum mean squared error (MMSE) filter in the NSCT domain is adopted to reduce speckle noise of SAR images. Movement, difference and competition are applied on each subband to obtain original multiscale and multidirection subband edges. Lipschitz regularity and non-maximum suppression are also adopted to effectively remove noise points. Finally, hysteresis thresholding with adaptive thresholds is introduced to avoid streaking and trial-and-error, and multiscale edge fusion is used to reduce the false alarm rate. Edge extraction results of simulated and real SAR images demonstrate that the proposed method is better than other edge detection methods based on Canny operator, ratio of averages (ROA) operator, ratio of exponentially weighted averages (ROEWA) operator, wavelet modulus maximum and curvelet.

Detecting Water Bodies on RADARSAT Imagery

This paper presents a novel geodesic active contour (GAC) model based on an edge detector for rapid detection of water bodies from spaceborne synthetic aperture radar (SAR) imagery with high speckle noise. The original edge indicator function based on gradients is replaced by an edge indicator function based on the ratio of exponentially weighted averages (ROEWA) operator. Thus, the capability of edge detection and the accuracy of locating edges are greatly improved, which makes the model more appropriate for SAR images. In addition, an enhancing term is added to the original model's energy function in order to boost the strength for the contour's evolution. An unconditionally stable additive operator splitting (AOS) scheme and a fast algorithm for re-initialization of the level set function are adopted, which not only enhances the model's stability, but also speeds up the model's convergence remarkably. The experimental results on simulated and real RADARSAT-1/-2 images show its efficiency and accuracy.

An optimal multiedge detector for SAR image segmentation

Edge detection is a fundamental issue in image analysis. Due to the presence of speckle, which can be modeled as a strong, multiplicative noise, edge detection in synthetic aperture radar (SAR) images is extremely difficult, and edge detectors developed for optical images are inefficient. Several robust operators have been developed for the detection of isolated step edges in speckled images. The authors propose a new step-edge detector for SAR images, which is optimal in the minimum mean square error (MSSE) sense under a stochastic multiedge model. It computes a normalized ratio of exponentially weighted averages (ROEWA) on opposite sides of the central pixel. This is done in the horizontal and vertical direction, and the magnitude of the two components yields an edge strength map. Thresholding of the edge strength map by a modified version of the watershed algorithm and region merging to eliminate false edges complete an efficient segmentation scheme. Experimental results obtained from simulated SAR images as well as ERS-1 data are presented.