An improved SIFT matching algorithm for multi-source satellite images with multiple similar contents

2014 ◽  
Author(s):  
D.F. Ren ◽  
Q.B. Wang
Keyword(s):  
Satellite Images ◽  
Matching Algorithm

scholarly journals A NOVEL SUB-PIXEL MATCHING ALGORITHM BASED ON PHASE CORRELATION USING PEAK CALCULATION

2016 ◽  
Vol XLI-B1 ◽  
pp. 253-257 ◽  
Author(s):  
Junfeng Xie ◽  
Fan Mo ◽  
Chao Yang ◽  
Pin Li ◽  
Shiqiang Tian
Keyword(s):  
Satellite Images ◽  
Phase Correlation ◽  
Matching Accuracy ◽  
Matching Algorithm ◽  
Pixel Matching ◽  
Fitting In ◽  
Geometrical Relationship ◽  
Matching Window ◽  
Correlation Matching ◽  
Better Than

The matching accuracy of homonymy points of stereo images is a key point in the development of photogrammetry, which influences the geometrical accuracy of the image products. This paper presents a novel sub-pixel matching method phase correlation using peak calculation to improve the matching accuracy. The peak theoretic centre that means to sub-pixel deviation can be acquired by Peak Calculation (PC) according to inherent geometrical relationship, which is generated by inverse normalized cross-power spectrum, and the mismatching points are rejected by two strategies: window constraint, which is designed by matching window and geometric constraint, and correlation coefficient, which is effective for satellite images used for mismatching points removing. After above, a lot of high-precise homonymy points can be left. Lastly, three experiments are taken to verify the accuracy and efficiency of the presented method. Excellent results show that the presented method is better than traditional phase correlation matching methods based on surface fitting in these aspects of accuracy and efficiency, and the accuracy of the proposed phase correlation matching algorithm can reach 0.1 pixel with a higher calculation efficiency.

scholarly journals Searching and describing objects in satellite images on the basis of modeling reasoning

2020 ◽  
Vol 44 (5) ◽  
pp. 772-781
Author(s):  
D.R. Kasimov
Keyword(s):  
Satellite Images ◽  
Search Strategy ◽  
Graph Matching ◽  
Inference Rule ◽  
Adjacency Graph ◽  
Matching Algorithm ◽  
Image Objects ◽  
Network Technologies ◽  
Neural Network Technologies ◽  
Case Based

The article presents an approach to a problem of contextual search and description of objects in raster satellite images, which consists in modeling reasoning on the basis of structured cases. As a result of image processing, an adjacency graph of color regions is constructed. The object is characterized by color, attributes of the form of segments of the border and the shape of the object as a whole. A structured case is represented in the form of a beam graph, whose arcs are ordered according to a positive bypass of the region boundaries. Using a graph matching algorithm, occurrences of cases stored in the system database are detected in the analyzed image. When the occurrence is detected, a case-based inference rule is applied. The degree to which an object belongs to a certain class depends not only on the properties of the object itself, but also on the reliability of the surrounding objects. The contextual search strategy contains stages of recursion and iteration. In contrast to neural network technologies, the proposed approach allows one not only to classify image objects, but also to form their structured descriptions. In addition, the classification decision issued by the system has a reasoned justification. The results of the experiment show that reasoning based on structured cases allows refining the results of classification and increasing the reliability of object recognition in satellite images.

scholarly journals A NOVEL SUB-PIXEL MATCHING ALGORITHM BASED ON PHASE CORRELATION USING PEAK CALCULATION

2016 ◽  
Vol XLI-B1 ◽  
pp. 253-257
Author(s):  
Junfeng Xie ◽  
Fan Mo ◽  
Chao Yang ◽  
Pin Li ◽  
Shiqiang Tian
Keyword(s):  
Satellite Images ◽  
Phase Correlation ◽  
Matching Accuracy ◽  
Matching Algorithm ◽  
Pixel Matching ◽  
Fitting In ◽  
Geometrical Relationship ◽  
Matching Window ◽  
Correlation Matching ◽  
Better Than

The matching accuracy of homonymy points of stereo images is a key point in the development of photogrammetry, which influences the geometrical accuracy of the image products. This paper presents a novel sub-pixel matching method phase correlation using peak calculation to improve the matching accuracy. The peak theoretic centre that means to sub-pixel deviation can be acquired by Peak Calculation (PC) according to inherent geometrical relationship, which is generated by inverse normalized cross-power spectrum, and the mismatching points are rejected by two strategies: window constraint, which is designed by matching window and geometric constraint, and correlation coefficient, which is effective for satellite images used for mismatching points removing. After above, a lot of high-precise homonymy points can be left. Lastly, three experiments are taken to verify the accuracy and efficiency of the presented method. Excellent results show that the presented method is better than traditional phase correlation matching methods based on surface fitting in these aspects of accuracy and efficiency, and the accuracy of the proposed phase correlation matching algorithm can reach 0.1 pixel with a higher calculation efficiency.

Correction to “A Coarse-to-Fine Matching Algorithm for FLIR and Optical Satellite Images Registration” [Jul 12 599-603]

2012 ◽  
Vol 9 (4) ◽  
pp. 798-798 ◽  
Author(s):  
Peng Wang ◽  
Zhi-guo Qu ◽  
Ping Wang ◽  
Ying-hui Gao ◽  
Zhen-kang Shen
Keyword(s):  
Satellite Images ◽  
Matching Algorithm ◽  
Optical Satellite Images ◽  
Coarse To Fine

scholarly journals SOLVING THE POINT CORRESPONDING PROBLEM BETWEEN MULTI RESOLUTION SATELLITE IMAGES USING A MODIFIED SVD-MATCHING ALGORITHM

2008 ◽  
Vol 36 (1) ◽  
pp. 27-37
Author(s):  
Ahmed F. Elaksher ◽  
Abdullatif A. Alharthy
Keyword(s):  
Satellite Images ◽  
Matching Algorithm

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

Multitemporal distribution analysis of Dodonaea viscosa (L.) Jacq. by remote sensing in Durango, Mexico

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Marco, A. Márquez-Linares ◽  
Jonathan G. Escobar--Flores ◽  
Sarahi Sandoval- Espinosa ◽  
Gustavo Pérez-Verdín
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Methodology ◽  
Supervised Classification ◽  
Satellite Images ◽  
Ground Cover ◽  
Distribution Analysis ◽  
Dodonaea Viscosa ◽  
Artificial Neural ◽  
Landsat Satellite

Objective: to determine the distribution of D. viscosa in the vicinity of the Guadalupe Victoria Dam in Durango, Mexico, for the years 1990, 2010 and 2017.Design/Methodology/Approach: Landsat satellite images were processed in order to carry out supervised classifications using an artificial neural network. Images from the years 1990, 2010 and 2017 were used to estimate ground cover of D. viscosa, pastures, crops, shrubs, and oak forest. This data was used to calculate the expansion of D. viscosa in the study area.Results/Study Limitations/Implications: the supervised classification with the artificial neural network was optimal after 400 iterations, obtaining the best overall precision of 84.5 % for 2017. This contrasted with the year 1990, when overall accuracy was low at 45 % due to less training sites (fewer than 100) recorded for each of the land cover classes.Findings/Conclusions: in 1990, D. viscosa was found on only five hectares, while by 2017 it had increased to 147 hectares. If the disturbance caused by overgrazing continues, and based on the distribution of D. viscosa, it is likely that in a few years it will have the ability to invade half the study area, occupying agricultural, forested, and shrub areas

Designing a coherent land registration system for rural Portugal

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Tiago NUNES ◽  
Miguel COUTINHO
Keyword(s):  
Satellite Images ◽  
Field Research ◽  
Digital Native ◽  
Web Based ◽  
Registration System ◽  
Land Registration ◽  
Daily Rate ◽  
Property Owners ◽  
Property Identification

After almost a century of several attempts to establish a coherent land registration system across the whole country, in 2017 the Portuguese government decided to try a new, digital native approach to the problem. Thus, a web-based platform was created, where property owners from 10 pilot municipalities could manually identify their lands’ properties using a map based on satellite images. After the first month of submissions, it became clear that at the current daily rate, it would take years to achieve the goal of 100% rural property identification across just the 10 municipalities. Field research during the first month after launch enabled us to understand landowners’ relationships with their land, map their struggles with the platform, and prototype ways to improve the whole service. Understanding that these improvements would still not be enough to get to the necessary daily rate, we designed, tested and validated an algorithm that allows us to identify a rural property shape and location without coordinates. Today, we are able to help both Government and landowners identify a rural property location with the click of a button.

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