Automatic Registration Of SAR And Optical Image Based On Line And Graph Spectral Theory

Zhao, Jun; Gao, Shuxin; Sui, Haigang; Li, Yulin; Li, Lan

doi:10.5194/isprsarchives-xl-4-377-2014

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…First, feature-based methods detect significant points that correspond to distinctive points of the same scene in two images, such as corner points, line intersections and centroid pixels of close-boundary regions [2,3]. Second, each feature point from one image (called the reference image) is matched with the corresponding point of the other image (called the sensed image) by various feature descriptors or similarity measures along with spatial relationships among the keypoints, such as the famous Scale-Invariant Feature Transform (SIFT) descriptor [4], shape context [5] and spectral graph [6]. Third, due to the complex nature of SAR images, the matched keypoints often result in a high number of false matches, which have a significant impact on determining the transformational model [7].…”

Section: Introductionmentioning

confidence: 99%

An Advanced Rotation Invariant Descriptor for SAR Image Registration

et al. 2017

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The Scale-Invariant Feature Transform (SIFT) algorithm and its many variants have been widely used in Synthetic Aperture Radar (SAR) image registration. The SIFT-like algorithms maintain rotation invariance by assigning a dominant orientation for each keypoint, while the calculation of dominant orientation is not robust due to the effect of speckle noise in SAR imagery. In this paper, we propose an advanced local descriptor for SAR image registration to achieve rotation invariance without assigning a dominant orientation. Based on the improved intensity orders, we first divide a circular neighborhood into several sub-regions. Second, rotation-invariant ratio orientation histograms of each sub-region are proposed by accumulating the ratio values of different directions in a rotation-invariant coordinate system. The proposed descriptor is composed of the concatenation of the histograms of each sub-region. In order to increase the distinctiveness of the proposed descriptor, multiple image neighborhoods are aggregated. Experimental results on several satellite SAR images have shown an improvement in the matching performance over other state-of-the-art algorithms.

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Section: Introductionmentioning

confidence: 99%

An Advanced Rotation Invariant Descriptor for SAR Image Registration

et al. 2017

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“…Intensity-based registration approaches like mutual information often fail because of the different radiometric and geometric properties of the SAR and optical images. The paper of Zhao et al (2014) shows that reliable results are only achieved for image pairs with small misalignment. A problem of feature-based approaches is the feature detection from the SAR image due to speckle noise and geometric radar effects.…”

Section: Introductionmentioning

confidence: 99%

Registration of Optical and Sar Satellite Images Based on Geometric Feature Templates

Merkle¹,

Müller²,

Reinartz³

2015

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

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ABSTRACT:Image registration is required for different remote sensing applications, like change detection or image fusion. Since research studies have shown the outstanding absolute geometric accuracy of high resolution radar satellites images like TerraSAR-X, the importance of SAR images as source for geolocation enhancement has increased. Due to this fact, multi-sensor image to image registration of optical and SAR images can be used for the improvement of the absolute geometric processing and accuracy of optical images with TerraSAR-X as reference. In comparison to the common optical and SAR image registration methods the proposed method is a combination of intensity-based and feature-based approaches. The proposed method avoids the direct and often difficult detection of features from the SAR images. SAR-like templates are generated from features detected from the optical image. These templates are used for an intensity-based matching with the SAR image. The results of the matching process are ground control points, which are used for the estimation of translation parameters followed by a subpixel translation of the optical image. The proposed image registration method is tested for two pairs of TerraSAR-X and QuickBird images and one pair of TerraSAR-X and WorldView-2 images of a suburban area. The results show that with the proposed method the geometric accuracy of optical images can be enhanced.

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“…Wang and Tian (2011) proposed to combine mutual information with image gradient and Shu et al (2005) with orientation information. A drawback of most of the intensitybased approaches is that reliable results are only achieved for image pairs with small misalignment (Zhao et al, 2014). Most * Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Optical and Sar Satellite Image Registration

Merkle

Müller

Schwind

et al. 2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Over the last years several research studies have shown the high geometric accuracy of high resolution radar satellites like TerraSAR-X. Due to this fact, the impact of high resolution SAR images for image registration has increased. An aim of high accuracy image registration is the improvement of the absolute geometric accuracy of optical images by using SAR images as references. High accuracy image registration is required for different remote sensing applications and is an on-going research topic. The registration of images acquired by different sensor types, like optical and SAR images, is a challenging task. In our work, a novel approach is proposed, which is a combination of the classical feature-based and intensity-based registration approaches. In the first step of the method, spatial features, here roundabouts, are detected in the optical image. In the second step, the detected features are used to generate SAR like roundabout templates. In the third step, the templates are matched with the corresponding parts of the SAR image by using an intensitybased matching process. The proposed method is tested for a pair of TerraSAR-X and QuickBird images and a pair of TerraSAR-X and WorldView-2 images of a suburban area. The results show that the proposed method offers an alternative approach compared to the common optical and SAR images registration methods and it can be used for the geometric accuracy improvement of optical images.

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Automatic Registration Of SAR And Optical Image Based On Line And Graph Spectral Theory

Cited by 4 publications

References 12 publications

An Advanced Rotation Invariant Descriptor for SAR Image Registration

An Advanced Rotation Invariant Descriptor for SAR Image Registration

Registration of Optical and Sar Satellite Images Based on Geometric Feature Templates

A New Approach for Optical and Sar Satellite Image Registration

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