SAR and Optical Image Matching Eased on Phase Congruency and Template Matching

2021 ◽  
Author(s):  
Qingbo Ji ◽  
Lingjie Wang ◽  
Changbo Hou ◽  
Qiang Zhang ◽  
Qingquan Liu ◽  
...  
Keyword(s):  
Image Matching ◽  
Template Matching ◽  
Optical Image ◽  
Phase Congruency

scholarly journals ROBUST MULTIMODAL IMAGE MATCHING BASED ON MAIN STRUCTURE FEATURE REPRESENTATION

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 583-589
Author(s):  
Y. Fu ◽  
Y. Ye ◽  
G. Liu ◽  
B. Zhang ◽  
R. Zhang
Keyword(s):  
Image Matching ◽  
Template Matching ◽  
Feature Representation ◽  
Image Feature ◽  
Feature Descriptor ◽  
Phase Congruency ◽  
Main Structure ◽  
Structure Information ◽  
Remote Sensing Image Processing ◽  
Multimodal Images

Abstract. Image matching is a crucial procedure for multimodal remote sensing image processing. However, the performance of conventional methods is often degraded in matching multimodal images due to significant nonlinear intensity differences. To address this problem, this letter proposes a novel image feature representation named Main Structure with Histogram of Orientated Phase Congruency (M-HOPC). M-HOPC is able to precisely capture similar structure properties between multimodal images by reinforcing the main structure information for the construction of the phase congruency feature description. Specifically, each pixel of an image is assigned an independent weight for feature descriptor according to the main structure such as large contours and edges. Then M-HOPC is integrated as the similarity measure for correspondence detection by a template matching scheme. Three pairs of multimodal images including optical, LiDAR, and SAR data have been used to evaluate the proposed method. The results show that M-HOPC is robust to nonlinear intensity differences and achieves the superior matching performance compared with other state-of-the-art methods.

scholarly journals RESEARCH PROGRESS ON FEATURE MATCHING OF SAR AND OPTICAL IMAGES

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 77-82 ◽  
Author(s):  
H. Qian ◽  
J. W. Yue ◽  
M. Chen
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Optical Image ◽  
Research Progress ◽  
Phase Congruency ◽  
Matching Method ◽  
Sift Algorithm ◽  
Matching Problems ◽  
Optical Images ◽  
Edge Features

Abstract. Before obtaining information and identifying ground target from images, image matching is necessary. However, problems of strong pixel noise interference and nonlinear gray scale differences in synthetic aperture radar image still exist. Feature matching becomes a kind possible solution. To learn the research progress of SAR and optical image matching, as well as finding solutions for above matching problems, a summary for feature matching with SAR and optical image is indispensable. By listing three typical methods below, we can discuss and compare how researchers improve and innovate methods for feature matching from different angles in matching process. First method is feature matching method proposed by CHEN Min et. It uses phase congruency method to detect point features. Feature descriptors are based on gaussian-gamma-shaped edge strength maps instead of original images. This method combines both edge features and point features to reach a match target. The second one is SAR-SIFT algorithm of F. Dellinger et. This kind of method is based on improvement of sift algorithm. It proposes a SAR-Harris method and also a calculation method for features descriptors named gradient by ratio. Thirdly, it is feature matching method proposed by Yu Qiuze et. By using edge features of image and improvement of hausdorff distance for similarity measure, it applies genetic algorithm to accelerate matching search process to complete matching tasks. Those methods are implemented by using python programs, and are compared by some indexes. Experimental data used multiple sets of terrasar and optical image pairs of different resolutions. To some extent, the results demonstrate that all three kinds of feature methods can improve the matching effect between SAR and optical images. It can be easier to reach match purposes of SAR and optical images by using image edge features, while such methods are too dependent on the edge features.

scholarly journals Deep Learning for SAR-Optical Image Matching

2019 ◽  
Author(s):  
Lloyd Haydn Hughes ◽  
Nina Merkle ◽  
Tatjana Burgmann ◽  
Stefan Auer ◽  
Michael Schmitt
Keyword(s):  
Deep Learning ◽  
Image Matching ◽  
Optical Image

Stratified Image Matching Using Template Matching

2004 ◽  
Vol 2004.42 (0) ◽  
pp. 433-434
Author(s):  
Akihiro ISSIKI ◽  
Shuji SEO ◽  
shigeharu MIYATA
Keyword(s):  
Image Matching ◽  
Template Matching

scholarly journals Robust Fine Registration of Multisensor Remote Sensing Images Based on Enhanced Subpixel Phase Correlation

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4338
Author(s):  
Zhen Ye ◽  
Jian Kang ◽  
Jing Yao ◽  
Wenping Song ◽  
Sicong Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Template Matching ◽  
Correlation Method ◽  
Phase Correlation ◽  
Phase Congruency ◽  
Structural Representation ◽  
Remote Sensing Images ◽  
L1 Norm ◽  
Sensing Applications ◽  
Fine Registration

Automatic fine registration of multisensor images plays an essential role in many remote sensing applications. However, it is always a challenging task due to significant radiometric and textural differences. In this paper, an enhanced subpixel phase correlation method is proposed, which embeds phase congruency-based structural representation, L1-norm-based rank-one matrix approximation with adaptive masking, and stable robust model fitting into the conventional calculation framework in the frequency domain. The aim is to improve the accuracy and robustness of subpixel translation estimation in practical cases. In addition, template matching using the enhanced subpixel phase correlation is integrated to realize reliable fine registration, which is able to extract a sufficient number of well-distributed and high-accuracy tie points and reduce the local misalignment for coarsely coregistered multisensor remote sensing images. Experiments undertaken with images from different satellites and sensors were carried out in two parts: tie point matching and fine registration. The results of qualitative analysis and quantitative comparison with the state-of-the-art area-based and feature-based matching methods demonstrate the effectiveness and reliability of the proposed method for multisensor matching and registration.

Image Fast Matching Basing on Local Information

2011 ◽  
Vol 268-270 ◽  
pp. 1376-1381
Author(s):  
De Jun Tang ◽  
Wei Shi Zhang ◽  
Lian Fu Li ◽  
Yan Si
Keyword(s):  
Image Matching ◽  
Template Matching ◽  
Image Features ◽  
Local Information ◽  
Aerial Image ◽  
Intelligent Robots ◽  
Wide Range ◽  
Industrial Inspection ◽  
Edge Points ◽  
Local Image

The image matching technology is very important technology in computer vision. It is a wide range of application areas, such as aerial image analysis, industrial inspection, and stereo vision, medical, meteorological, and intelligent robots. The article introduces several important image matching technology, and some common fast image matching usage. Propose the image fast matching method basing on local information, mainly use template matching basing on local image features to achieve, by extraction of the selected feature points (including the obvious point, corner points, edge points, edge line, etc.) extracted, and through the calculation of similarity, and by using fast matching algorithm to achieve fast and accurate image matching requirements.

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