Satellite Image Matching and Registration: A Comparative Study Using Invariant Local Features

2016 ◽  
pp. 135-171 ◽  
Author(s):  
Mohamed Tahoun ◽  
Abd El Rahman Shabayek ◽  
Hamed Nassar ◽  
Marcello M. Giovenco ◽  
Ralf Reulke ◽  
...  
Keyword(s):  
Comparative Study ◽  
Image Matching ◽  
Satellite Image ◽  
Local Features

scholarly journals Hybrid Invariant Local Features for Multiple Satellite Image Matching and Registration

2019 ◽  
Vol 9 (2) ◽  
pp. 309-314
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Local Features ◽  
Feature Descriptor ◽  
Matching Method ◽  
Multiple Images ◽  
Feature Sets ◽  
Precise Relationship

Automatic image registration (IR) is very challenging and very important in the field of hyperspectral remote sensing data. Efficient autonomous IR method is needed with high precision, fast, and robust. A key operation of IR is to align the multiple images in single co-ordinate system for extracting and identifying variation between images considered. In this paper, presented a feature descriptor by combining features from both Feature from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Key point (BRISK). The proposed hybrid invariant local features (HILF) descriptor extract useful and similar feature sets from reference and source images. The feature matching method allows finding precise relationship or matching among two feature sets. An experimental analysis described the outcome BRISK, FASK and proposed HILF in terms of inliers ratio and repeatability evaluation metrics.

Image Matching with Large Viewing Angle Based on Local Features

2019 ◽  
Vol 39 (5) ◽  
pp. 0510002
Author(s):  
赵鹏图 Zhao Pengtu ◽  
达飞鹏 Da Feipeng
Keyword(s):  
Image Matching ◽  
Local Features ◽  
Viewing Angle

scholarly journals Area-Based Dense Image Matching with Subpixel Accuracy for Remote Sensing Applications: Practical Analysis and Comparative Study

2020 ◽  
Vol 12 (4) ◽  
pp. 696 ◽  
Author(s):  
Zhen Ye ◽  
Yusheng Xu ◽  
Hao Chen ◽  
Jingwei Zhu ◽  
Xiaohua Tong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Comparative Study ◽  
Image Matching ◽  
Systematic Errors ◽  
Disparity Estimation ◽  
Sensing Applications ◽  
Subpixel Accuracy ◽  
Dense Image ◽  
Remote Sensing Applications ◽  
Practical Analysis

Dense image matching is a crucial step in many image processing tasks. Subpixel accuracy and fractional measurement are commonly pursued, considering the image resolution and application requirement, especially in the field of remote sensing. In this study, we conducted a practical analysis and comparative study on area-based dense image matching with subpixel accuracy for remote sensing applications, with a specific focus on the subpixel capability and robustness. Twelve representative matching algorithms with two types of correlation-based similarity measures and seven types of subpixel methods were selected. The existing matching algorithms were compared and evaluated in a simulated experiment using synthetic image pairs with varying amounts of aliasing and two real applications of attitude jitter detection and disparity estimation. The experimental results indicate that there are two types of systematic errors: displacement-dependent errors, depending on the fractional values of displacement, and displacement-independent errors represented as unexpected wave artifacts in this study. In addition, the strengths and limitations of different matching algorithms on the robustness to these two types of systematic errors were investigated and discussed.

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