Multi-Sensor SAR Image Registration Based on Object Shape

Rui, Jie; Wang, Chao; Zhang, Hong; Jin, Fei

doi:10.3390/rs8110923

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…Moreover, they used RMSE and standard deviation to assessment their proposed registration method. 41 Besides, RMSE is also usually used to evaluate the registration results. Kumar and his coauthors used RMSE to evaluate their proposed methods.…”

Section: Objective Assessmentmentioning

confidence: 99%

A structural similarity-inspired performance assessment model for multisensor image registration algorithms

Jiao

Deng

et al. 2017

International Journal of Advanced Robotic Systems

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In order to assess the performance of multisensor image registration algorithms that are used in the multirobot information fusion, we propose a model based on structural similarity whose name is vision registration assessment model. First of all, this article introduces a new image concept named superimposed image for testing subjective and objective assessment methods. Therefore, we assess the superimposed image but not the registered image, which is different from previous image registration assessment methods that usually use reference and sensed images. Then, we calculate eight assessment indicators from different aspects for superimposed images. After that, vision registration assessment model fuses the eight indicators using canonical correlation analysis, which is used for evaluating the quality of an image registration results in different aspects. Finally, three kinds of images which include optical images, infrared images, and SAR images are used to test vision registration assessment model. After evaluating three state-of-the-art image registration methods, experiments indict that the proposed structural similarity-motivated model achieved almost same evaluation results with that of the human object with the consistency rate of 98.3%, which shows that vision registration assessment model is efficient and robust for evaluating multisensor image registration algorithms. Moreover, vision registration assessment model is independent of the emotional factors and outside environment, which is different from the human.

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Section: Objective Assessmentmentioning

confidence: 99%

A structural similarity-inspired performance assessment model for multisensor image registration algorithms

Jiao

Deng

et al. 2017

International Journal of Advanced Robotic Systems

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“…The correlation between gradient direction is taken as the main measure, and the difference of Zernike moment and error ellipse is used as auxiliary parameters to realize fine matching, taking into account the matching success rate and computational efficiency (Ding, Tong, Qin, 2018). In addition to its application in landmark matching, feature line matching is often used to match multi-source remote sensing images with obvious edges and contours (Yu, Lu, Hu, 2013;Wang, Wang, Li, 2011;Li, Zhang, Zhang, 2019), such as SAR images and optical images (Xiang, Wang, You, 2018;Rui, Wang, Zhang, 2019), infrared images and optical images (Li, Jiang, Xu, 2015;Hu, Wang, Liu, 2013) . In these processes, feature line matching is transformed into feature point matching (Liu, Huo, Han, 2017;Song, 2014) and intensity-based registration (Liang, Liu, Huang, 2014;Moorthil, Sivakumar, 2018).…”

Section: Introductionmentioning

confidence: 99%

A Landmark Matching Algorithm for the Geostationary Satellite Images Based on Multi-Level Grids

Hou

Qin

Niu

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The resolution of geostationary satellite image is not high and the image is covered with clouds. At present, when the extracted feature points are unstable, there are some problems, such as low matching accuracy or even matching failure. In this paper, a landmark matching algorithm is proposed to directly establish the multi-level grids for the image coastline and the coastline template. Through the similarity measure of the multi-level grids, the landmark matching is realized layer by layer. First of all, we've finished cloud detection, establishment of landmark data set, and extraction of image coastline. Then we design and implement the landmark matching algorithm based on multi-level grids. Finally, through analysis from different levels of landmarks and different proportion of cloud cover, the advantages and applicable conditions of this algorithm are given. The experimental results show that: 1) with the increase of cloud cover, the correct rate of landmark matching decreases, but the decrease is small. It shows that the matching algorithm in this paper is stable. Correct matching rate could always be stable at about 75 percent in the fourth level. 2) when the proportion of cloud cover is less than 20 percent, the higher the matching level, the higher the matching accuracy. When the cloud cover is more than 20 percent, the matching accuracy in the fourth level is the highest. This algorithm provides a stable method for the landmark matching of geostationary satellite image.

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“…In addition, Ma et al [42] proffered a robust point matching algorithm that combines the position, scale, and orientation of each key point to enhance the feature matching, this algorithm is called PSO-SIFT because it was inspired by the SIFT algorithm. Considering the linear features are often incomplete and fragmented [43], while the contours with large information content are easily to be distinguished and matched, some contour-based methods [26,27,43,44] have been proposed for multi-sensor image registration.…”

Section: Introductionmentioning

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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Multi-Sensor SAR Image Registration Based on Object Shape

Cited by 5 publications

References 16 publications

A structural similarity-inspired performance assessment model for multisensor image registration algorithms

A structural similarity-inspired performance assessment model for multisensor image registration algorithms

A Landmark Matching Algorithm for the Geostationary Satellite Images Based on Multi-Level Grids

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

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