Self-similar texture modeling using FARIMA processes with applications to satellite images

2001 ◽  
Vol 10 (5) ◽  
pp. 792-797 ◽  
Author(s):  
J. Ilow ◽  
H. Leung
Keyword(s):  
Satellite Images ◽  
Texture Modeling ◽  
Self Similar ◽  
Farima Processes ◽  
Similar Texture

Self-similar collapse with non-radial motions

2006 ◽  
Vol 20 ◽  
pp. 1-4
Author(s):  
A. Nusser
Keyword(s):  
Self Similar

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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