Development of thunderstorm monitoring technologies and algorithms by integration of radar, sensors, and satellite images

2017 ◽  
Author(s):  
Vitaliy A. Shapovalov ◽  
Anton S. Boldyrev ◽  
Aida A. Adzhieva
Keyword(s):  
Satellite Images ◽  
Radar Sensors ◽  
Monitoring Technologies

SLAM Using Relational Trees and Semantics

2012 ◽  
Vol 452-453 ◽  
pp. 648-653 ◽  
Author(s):  
A. Sarkar ◽  
R. Reiger ◽  
S. Roy ◽  
R. Chaterjee ◽  
A. Datta ◽  
...  
Keyword(s):  
Satellite Images ◽  
Semantic Mapping ◽  
Posterior Density ◽  
Radar Sensors ◽  
Control Information ◽  
Segmented Image ◽  
Simulated Environment ◽  
Actual Environment ◽  
Consecutive Time ◽  
Type Information

This work attempts to develop a method for SLAM using semantics based on FastSLAM 2.0. Our approach to semantic mapping consists of segmenting images obtained from two sensors (optical and radar) aboard a UAV. We then identify landmarks within the segmented image, followed by the construction of relational trees with the landmarks; these trees are then used at consecutive time-steps of the robot’s motion for its localization as well as update of the landmarks. The term semantics has been used for region-landmarks which are validated with a look-up table (LUT) of the predefined surface type information, a superset of the robot’s actual environment. Finally, based on particle filters, the posterior density of the state of the robot is estimated and a 2-D semantics map is constructed. The methodology has been tested in a situation wherein the robot’s true environment and path have been simulated. For simulation we consider satellite images of optical and radar sensors of the robot’s environment. At different time-steps the robot’s images are cropped from these images, incorporating errors in the robot’s control information. Experiments carried out on the simulated environment have provided encouraging results.

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.

A Novel Change Detection Method for Unregistered Optical Satellite Images

2012 ◽  
Vol E95.B (5) ◽  
pp. 1890-1893
Author(s):  
Wang LUO ◽  
Hongliang LI ◽  
Guanghui LIU ◽  
Guan GUI
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Detection Method ◽  
Optical Satellite Images

IMIKO-Radar Project: Laboratory Interference Measurements of Automotive Radar Sensors

2020 ◽  
Author(s):  
Alicja Ossowska ◽  
Leen Sit ◽  
Sarath Manchala ◽  
Thomas Vogler ◽  
Kevin Krupinski ◽  
...  
Keyword(s):  
Automotive Radar ◽  
Radar Sensors ◽  
Interference Measurements

The solar radiation evaluation by satellite images processing

2010 ◽  
Vol 1 (08) ◽  
pp. 344-347 ◽  
Author(s):  
M. Boulifa ◽  
A. Adane ◽  
A. Mefti ◽  
S. Ameur ◽  
Z. Ameur
Keyword(s):  
Solar Radiation ◽  
Satellite Images ◽  
Images Processing

Satellite Images Vectorization Based on Clustering and Interpolation Technique

2017 ◽  
Vol 13 (3) ◽  
pp. 47-64
Author(s):  
Rehab F. Hassan ◽  
◽  
Lubna mhammed Bader
Keyword(s):  
Satellite Images ◽  
Interpolation Technique

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

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