Automatic Optical-to-SAR Image Registration by Iterative Line Extraction and Voronoi Integrated Spectral Point Matching

Sui, Haigang; Xu, Chuan; Liu, Junyi; Feng, Huanqing

doi:10.1109/tgrs.2015.2431498

Cited by 75 publications

(30 citation statements)

References 51 publications

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“…This approach is limited to images that contain sharp edges from runways, rivers or lakes. Sui et al [23] and Xu et al [22] propose iterative matching procedures to overcome the problem of misaligned images caused by imprecise extracted features. In [23], an iterative Voronoi spectral point matching between the line-intersection is proposed, which depends on the presence of salient straight line features in the images.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images

et al. 2017

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Improving the geo-localization of optical satellite images is an important pre-processing step for many remote sensing tasks like monitoring by image time series or scene analysis after sudden events. These tasks require geo-referenced and precisely co-registered multi-sensor data. Images captured by the high resolution synthetic aperture radar (SAR) satellite TerraSAR-X exhibit an absolute geo-location accuracy within a few decimeters. These images represent therefore a reliable source to improve the geo-location accuracy of optical images, which is in the order of tens of meters. In this paper, a deep learning-based approach for the geo-localization accuracy improvement of optical satellite images through SAR reference data is investigated. Image registration between SAR and optical images requires few, but accurate and reliable matching points. These are derived from a Siamese neural network. The network is trained using TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe, in order to learn the two-dimensional spatial shifts between optical and SAR image patches. Results confirm that accurate and reliable matching points can be generated with higher matching accuracy and precision with respect to state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images

et al. 2017

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“…By applying PSO-SIFT on resized testing images and images with short time interval, it can be speculated two reasons for the failure of the PSO-SIFT algorithm. First, even though improved, SIFT is not robust enough to detect and match feature points between the optical and SAR image due to their intrinsic differences [41]. Second, SIFT-based methods are not suitable to register large size and high-resolution SAR images since they are proposed for small-size images [51].…”

Section: Registration Performance Comparisonmentioning

confidence: 99%

“…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…”

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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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“…First, the optimal values of the rotation and scale parameters (C 1 , C 2 , C 3 , C 5 , C 6 and C 7 ) are determined using Equations (11) and (12) that minimize the deviations between observed and predicted unit vectors a x i and a y i . Second, using the estimated values of these parameters, the translation coefficients C 4 and C 8 can then be determined using Equations (13) and (14).…”

Section: The Original Two-step Approachmentioning

confidence: 99%

Improved Parameter Estimation of the Line-Based Transformation Model for Remote Sensing Image Registration

2017

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Abstract:The line-based transformation model (LBTM), built upon the use of affine transformation, was previously proposed for image registration and image rectification. The original LBTM first utilizes the control line features to estimate six rotation and scale parameters and subsequently uses the control point(s) to retrieve the remaining two translation parameters. Such a mechanism may accumulate the error of the six rotation and scale parameters toward the two translation parameters. In this study, we propose the incorporation of a direct method to estimate all eight transformation parameters of LBTM simultaneously using least-squares adjustment. The improved LBTM method was compared with the original LBTM through using one synthetic dataset and three experimental datasets for satellite image 2D registration and 3D rectification. The experimental results demonstrated that the improved LBTM converges to a steady solution with two to three ground control points (GCPs) and five ground control lines (GCLs), whereas the original LBTM requires at least 10 GCLs to yield a stable solution.

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Automatic Optical-to-SAR Image Registration by Iterative Line Extraction and Voronoi Integrated Spectral Point Matching

Cited by 75 publications

References 51 publications

Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images

Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images

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

Improved Parameter Estimation of the Line-Based Transformation Model for Remote Sensing Image Registration

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