A Union Matching Method for SAR Images Based on SIFT and Edge Strength

Chen, Tianze; Chen, Limin

doi:10.1109/jstars.2014.2341173

Cited by 14 publications

(5 citation statements)

References 13 publications

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“…Two points are called a best point pair if they correspond to one another. The matched point and the best point pair are defined in Equation (4). An example is shown in Figure 4a, (4) where MP i→j is a matched point pair and indicates that the matched point of p i is q j ; MP j→i indicates that the matched point of q j is p i ; and BP i↔j is a best point pair.…”

Section: Shape Matching Using Shape Contextmentioning

confidence: 99%

“…Feature-based methods have been proven to be effective, however, respective features are difficult to match and the robustness of the methods strongly depends on feature extraction results. To improve reliability and robustness, sophisticated approaches based on multi-features or multi-layer features have been developed [4,5]. Meanwhile, registration methods using both area-based and feature-based approaches have received more attention [3,6,7].…”

Section: Introductionmentioning

confidence: 99%

“…However, most methods were only used for mono-sensor images [4,5,8] and were even limited to the same or similar imaging conditions [7,9,10]. For multi-sensor and multi-temporal imagery, the textures and grey levels at the locations of conjugate positions will not likely be similar.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Sensor SAR Image Registration Based on Object Shape

et al. 2016

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Abstract:Owing to significant differences in synthetic aperture radar (SAR) images caused by diverse imaging mechanisms and imaging conditions, inconsistent features and relationship correspondences constitute key problems for traditional image registration algorithms. This study presents a novel SAR image registration method based on the shape information of distinct ground objects, which is obtained via object extraction and morphological operations. We utilize a shape context descriptor to compare the contours of objects and detect invariant control points. The experimental results show that the proposed method can achieve a reliable and stable registration performance for SAR images of different sensors.

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Section: Shape Matching Using Shape Contextmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Sensor SAR Image Registration Based on Object Shape

et al. 2016

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“…To obtain more accurate mapping at the subpixel scale, an improved method combining spatial dependence with directivity and connectivity of linear land covers is proposed, and simulated annealing arithmetic (SAA) is applied to optimize subpixel allocation. In [30], accuracy and adaptability of the SIFT matching method for SAR images are studied under strong multiplicative speckle noise, where SIFT point matching can be optimized based on the edge point feature in SAR images.…”

Section: F Othersmentioning

confidence: 99%

Foreword to the Special Issue on Pattern Recognition in Remote Sensing

Michaelsen

et al. 2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…For example, a registration algorithm combined an adaptive sampling method with SAR-SIFT and NCC has been proposed for GF-3 images [37]. Methods based on multi-features or multi-layer features [39,40] have been developed to improve the robustness of SAR image registration. Sui [41] proposed a registration method based on iterative line extraction…”

mentioning

confidence: 99%

A Point Pattern Chamfer Registration of Optical and SAR Images Based on Mesh Grids

Fang

Xiong

et al. 2018

Remote Sensing

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Automatic image registration of optical-to-Synthetic aperture radar (SAR) images is difficult because of the inconsistency of radiometric and geometric properties between the optical image and the SAR image. The intensity-based methods may require many calculations and be ineffective when there are geometric distortions between these two images. The feature-based methods have high requirements on features, and there are certain challenges in feature extraction and matching. A new automatic optical-to-SAR image registration framework is proposed in this paper. First, modified holistically nested edge detection is employed to detect the main contours in both the optical and SAR images. Second, a mesh grid strategy is presented to perform a coarse-to-fine registration. The coarse registration calculates the feature matching and summarizes the preliminary results for the fine registration process. Finally, moving direct linear transformation is introduced to perform a homography warp to alleviate parallax. The experimental results show the effectiveness and accuracy of our proposed method. However, due to the different geometric and radiometric properties of SAR and optical images, to automatically register these two types of images, one must overcome many difficulties. In particular, optical images and SAR images have different geometrical characteristics. Whereas geometric distortions such as foreshortening and layover exist in SAR images, perspective and shadow exist in optical images, which cause the differences between the two types of images. In addition, optical images and SAR images have different radiometric distortion, the SAR sensor is an active remote sensing system, but the optical sensor is a passive system [4]. A large quantity of speckle noise in SAR images renders it difficult to obtain common features from a SAR image and an optical image [5]. For these reasons, the registration of optical images and SAR images has more challenges than mono-sensor image registration.The existing optical-to-SAR registration methods are mainly divided into two types: intensity-based registration methods and feature-based registration methods. Intensity-based registration methods include mutual information (MI) [6], cross-cumulative residual entropy [7] and normalized cross-correlation (NCC) [8]. Although this kind of registration method can register multi-sensor images with intensity differences, it is insensitive to the local differences between the two images and it requires many calculations [9]. Therefore, some improved intensity-based registration methods combined edges and gradient have been proposed [10][11][12]. For example, Cheah et al. [10] proposed the adaptation of MI measure which incorporates the spatial information by combining intensity and gradient information. Chen et al. [13] implemented MI through joint histogram estimation using various interpolation algorithms to complete multi-sensor and multiresolution image registration. Saidi et al. [14] proposed a refined automatic co-registration method (RA...

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A Union Matching Method for SAR Images Based on SIFT and Edge Strength

Cited by 14 publications

References 13 publications

Multi-Sensor SAR Image Registration Based on Object Shape

Multi-Sensor SAR Image Registration Based on Object Shape

Foreword to the Special Issue on Pattern Recognition in Remote Sensing

A Point Pattern Chamfer Registration of Optical and SAR Images Based on Mesh Grids

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