Target similarity matching algorithm of big data in remote sensing image based on Henon mapping

2022 ◽  
Vol 20 (1) ◽  
pp. 51
Author(s):  
Qing Sun ◽  
Quanyuan Wu
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Remote Sensing Image ◽  
Similarity Matching ◽  
Matching Algorithm ◽  
Henon Mapping

scholarly journals A MANAGEMENT OF REMOTE SENSING BIG DATA BASE ON STANDARD METADATA FILE AND DATABASE MANAGEMENT SYSTEM

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 653-657
Author(s):  
H. Wu ◽  
K. Fu
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Management System ◽  
Database Management ◽  
High Capacity ◽  
Remote Sensing Data ◽  
Large Data ◽  
Remote Sensing Image ◽  
Database Management System ◽  
Metadata Standard

Abstract. As a kind of information carrier which is high capacity, remarkable reliability, easy to obtain and the other features，remote sensing image data is widely used in the fields of natural resources survey, monitoring, planning, disaster prevention and the others (Huang, Jie, et al, 2008). Considering about the daily application scenario for the remote sensing image in professional departments, the demand of usage and management of remote sensing big data is about to be analysed in this paper.In this paper, by combining professional department scenario, the application of remote sensing image analysis of remote sensing data in the use and management of professional department requirements, on the premise of respect the habits, is put forward to remote sensing image metadata standard for reference index, based on remote sensing image files and database management system, large data serialization of time management methods, the method to the realization of the design the metadata standard products, as well as to the standard of metadata content indexed storage of massive remote sensing image database management.

scholarly journals High-Resolution Optical Remote Sensing Image Registration via Reweighted Random Walk based Hyper-Graph Matching

2019 ◽  
Vol 11 (23) ◽  
pp. 2841 ◽  
Author(s):  
Wu ◽  
Di ◽  
Ming ◽  
Lv ◽  
Tan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Graph Matching ◽  
Remote Sensing Image ◽  
Feature Point ◽  
Optical Remote Sensing ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Optical Remote Sensing Image

High-resolution optical remote sensing image registration is still a challenging task due to non-linearity in the intensity differences and geometric distortion. In this paper, an efficient method utilizing a hyper-graph matching algorithm is proposed, which can simultaneously use the high-order structure information and radiometric information, to obtain thousands of feature point pairs for accurate image registration. The method mainly consists of the following steps: firstly, initial matching by Uniform Robust Scale-Invariant Feature Transform (UR-SIFT) is carried out in the highest pyramid image level to derive the approximate geometric relationship between the images; secondly, two-stage point matching is performed to find the matches, that is, a rotation and scale invariant area-based matching method is used to derive matching candidates for each feature point and an efficient hyper-graph matching algorithm is applied to find the best match for each feature point; thirdly, a local quadratic polynomial constraint framework is used to eliminate match outliers; finally, the above process is iterated until finishing the matching in the original image. Then, the obtained correspondences are used to perform the image registration. The effectiveness of the proposed method is tested with six pairs of high-resolution optical images, covering different landscape types—such as mountain area, urban, suburb, and flat land—and registration accuracy of sub-pixel level is obtained. The experiments show that the proposed method outperforms the conventional matching algorithms such as SURF, AKAZE, ORB, BRISK, and FAST in terms of total number of correct matches and matching precision.

Fast local adaptive multiscale image matching algorithm for remote sensing image correlation

2021 ◽  
pp. 104988
Author(s):  
Niccolò Dematteis ◽  
Daniele Giordan ◽  
Bruno Crippa ◽  
Oriol Monserrat
Keyword(s):  
Remote Sensing ◽  
Image Matching ◽  
Remote Sensing Image ◽  
Image Correlation ◽  
Matching Algorithm
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