scholarly journals Spectral properties of soils and agricultural crops for analysis of satellite images of the Akmola region

2021 ◽  
Vol 79 (1) ◽  
pp. 315-324
Author(s):  
A.E. Yerzhanova ◽  
◽  
S.E. Kerimkulov ◽  
Keyword(s):  
Data Analysis ◽  
Wavelength Range ◽  
Spectral Properties ◽  
Satellite Images ◽  
Recognition Algorithm ◽  
Spectral Brightness ◽  
Agricultural Crops ◽  
Brightness Coefficient ◽  
Spectral Brightness Coefficient ◽  
Different Types

This paper considers the spectral properties of soils and vegetation and their analysis for further application of the results of the article for processing satellite images. Basically, the soils and soils of the Akmola region and agricultural crops inherent in this region are considered. When analyzing the spectral brightness coefficient (SCR), there are differences in the SCR of soils of different types and vegetation. Based on the results of data analysis, the following conclusions were obtained: soil recognition is informative in the wavelength range from 700 nm to 1300 nm; crop recognition is informative in the wavelength range from 850 nm to 1100 nm. When developing an object recognition algorithm, two fixed points of 0.55 microns and a point of 0.66-0.68 microns will be considered for the presence of extremes to determine the type of object.

scholarly journals Spectral characteristics of some agricultural crops in different phenological phases of vegetation

2020 ◽  
pp. 56-70
Author(s):  
V. A. Tabunschik ◽  
Т. M. Chekmareva ◽  
R. V. Gorbunov
Keyword(s):  
Software Package ◽  
Satellite Images ◽  
Spectral Characteristics ◽  
Spectral Brightness ◽  
Space Images ◽  
Brightness Coefficient ◽  
Spectral Reflectivity ◽  
Spectral Brightness Coefficient ◽  
Sentinel 2 ◽  
Phenological Phases

For deciphering crops from satellite images at different time periods, it is necessary to have information about the spectral reflectivity of plants during their passage through the phenological phases of vegetation. An attempt was made to evaluate the spectral reflectivity of the main fruit crops and grapes in different phenological phases of the growing season using Sentinel-2 satellite images and the ENVI software package. Field research methods, plots were selected on which peach, grapes, cherries, apple trees, plums, and apricots grow are used. It was established that planting crops was carried out by mixing cultivars in order to reduce the risk of additional costs as a result of possible adverse natural processes and phenomena. For each section, the maximum, minimum, and average values of the spectral brightness coefficient were obtained and analyzed within 13 bands of Sentinel-2 satellite images. Space images were selected for 04/07/2019, 04/27/2019 and 05/12/2019, as the most suitable for the periods of the beginning of flowering (04/07/2019), the end of flowering (04/27/2019) and the beginning of fruit ripening (12/05/2019), with minimal cloud overlap values. To eliminate the external influence of the soil within each pixel of the image, the linear spectral separation module of the ENVI software package was used, a reference soil fragment was selected and its spectral characteristics were obtained, which made it possible to depict graphs of the spectral curves of the crops under study within each section. It was not possible to obtain a distinction of the spectral brightness coefficient for all sections, which is associated with the presence of additional external elements.

scholarly journals Analysis of the spectral properties of wheat growth in different vegetation periods

2021 ◽  
Vol 6 (2 (114)) ◽  
pp. 96-102
Author(s):  
Akbota Yerzhanova ◽  
Akmaral Kassymova ◽  
Gulzira Abdikerimova ◽  
Manshuk Abdimomynova ◽  
Zhuldyz Tashenova ◽  
...  
Keyword(s):  
Plant Species ◽  
Satellite Images ◽  
Action Plan ◽  
Main Tool ◽  
Normal Growth ◽  
Spectral Brightness ◽  
Space Images ◽  
Brightness Coefficient ◽  
Spectral Brightness Coefficient ◽  
Spectral Ranges

The article presents a technique for studying space images based on the analysis of the spectral brightness coefficient (SBC) of space images of the earth's surface. Recognition of plant species, soils, and territories using satellite images is an applied task that allows to implement many processes in agriculture and automate the activities of farmers and large farms. The main tool for analyzing satellite imagery data is the clustering of data that uniquely identifies the desired objects and changes associated with various reasons. Based on the data obtained in the course of experiments on obtaining numerical SBC values, the patterns of behavior of the processes of reflection of vegetation, factors that impede the normal growth of plants, and the proposed clustering of the spectral ranges of wave propagation, which can be used to determine the type of objects under consideration, are revealed. Recognition of these causes through the analysis of SBC satellite images will create an information system for monitoring the state of plants and events to eliminate negative causes. SBC data is divided into non-overlapping ranges, i.e. they form clusters reflecting the normal development of plant species and deviations associated with negative causes. If there are deviations, then there is an algorithm that determines the cause of the deviation and proposes an action plan to eliminate the defect. It should be noted that the distribution of the brightness spectra depends on the climatic and geographical conditions of the plant species and is unique for each region. This study refers to the Akmola region, where grain crops are grown

Sensitivity of the spectral brightness coefficient to variation of the components of seawater

1987 ◽  
Vol 47 (3) ◽  
pp. 951-955
Author(s):  
A. A. Kovalev ◽  
S. B. Kostyukevich ◽  
E. K. Naumenko ◽  
V. E. Plyuta
Keyword(s):  
Spectral Brightness ◽  
Brightness Coefficient ◽  
Spectral Brightness Coefficient

Market of Insurance in Russia: Major Tendencies and Problems of Development

2006 ◽  
pp. 115-127
Author(s):  
T Natkhov
Keyword(s):  
Data Analysis ◽  
Statistical Data ◽  
Statistical Data Analysis ◽  
Medium Term ◽  
Insurance Sector ◽  
Different Types ◽  
Basic Characteristics

The article considers recent tendencies in the development of the market of insurance in Russia. On the basis of statistical data analysis the most urgent problems of the insurance sector are formulated. Basic characteristics of different types of insurance are revealed, and measures on perfection of the insurance institution in the medium term are proposed.

Automated Emission Data Registration and Segmentation for IC Analysis

2016 ◽  
Author(s):  
Franco Stellari ◽  
Peilin Song
Keyword(s):  
Data Analysis ◽  
Registration Accuracy ◽  
Emission Data ◽  
Data Registration ◽  
Different Types ◽  
Test Chips

Abstract In this paper, the development of advanced emission data analysis methodologies for IC debugging and characterization is discussed. Techniques for automated layout to emission registration and data segmentations are proposed and demonstrated using both 22 nm and 14 nm SOI test chips. In particular, gate level registration accuracy is leveraged to compare the emission of different types of gates and quickly create variability maps automatically.

A dual U-Net algorithm for automating feature extraction from satellite imagery

2021 ◽  
pp. 154851292098354
Author(s):  
Samuel Humphries ◽  
Trevor Parker ◽  
Bryan Jonas ◽  
Bryan Adams ◽  
Nicholas J Clark
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Military Operations ◽  
Detection Algorithms ◽  
Us Military ◽  
Standard Product ◽  
Different Types ◽  
Road Intersections

Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.

scholarly journals The esteem needs in the main character of ‘Me Before You’ movie

2021 ◽  
Vol 8 (3) ◽  
pp. 1285-1298
Author(s):  
Diana Anggraeni ◽  
Herland Franley Manalu ◽  
Desty Anggraini
Keyword(s):  
Data Analysis ◽  
Self Esteem ◽  
Psychological Effects ◽  
Main Character ◽  
Descriptive Data ◽  
Literary Works ◽  
Human Needs ◽  
Different Types ◽  
The Status

Humans have gone through many incidents, both good and bad experiences, and sometimes these experiences are shared with others in the form of stories. The stories, as one of the forms of literary works, would be nothing without the created characters within them because they provide the viewers with a purpose and a reason for us to learn about what happens in the story. Besides, they act as one important element in the movie with various psychological effects. This research aims to analyze the characteristics and the hierarchy of human needs, especially esteem needs, that appear in the main character named Will Traynor in the ‘Me Before You’ movie directed by Thea Sharrock. This study uses descriptive data analysis which describes a phenomenon and the main character in the movie. The results revealed seven characters comprising the esteem needs hierarchy: sensitive, open-minded, friendly, kind, confident, humble, and stubborn. The esteem needs hierarchy is the desire to have the need to be approved, valued, and recognized to have some self-esteem. This is striking in the movie because of the status of the character, Will Traynor as a lord, and Louisa Clark who is only a maid and has no superiority over Will in her life. The findings imply the personality of humans differ in their characters and psychology as shown from the esteem needs hierarchy in Will’s personality expressing the different types of characteristics.

scholarly journals Satellite-based system of area estimation for main agricultural crops of Ukraine

2020 ◽  
pp. 78-90
Author(s):  
O. O. Kryvoshein ◽  
O. A. Kryvobok ◽  
T. I. Adamenko
Keyword(s):  
Satellite Images ◽  
Agricultural Land ◽  
Spring Barley ◽  
Spectral Characteristics ◽  
Systematic Sampling ◽  
Support Vector ◽  
Agricultural Crops ◽  
Similar System ◽  
Area Estimation ◽  
Growing Period

The article studies one of the most important issues of agricultural production maintenance – development of a system of crops area estimation in Ukraine. The objective of this paper is to describe the similar system that uses high resolution satellite data and operational agrometeorological data from the network of the Hydrometeorological Centre of Ukraine as input information. The system is based on step-by-step solving of the following tasks: obtaining geoinformation data for individual agricultural crops; development of methods for multispectral satellite images classification; development of software applications to automate the process of these images classification with subsequent classification of crop areas. The research uses the following algorithms (or classifiers) to classify the agricultural land: SVM (support vector machine), RF ("random forest") and NN (neural networks). The choice of the most accurate of them formed the basis of the general method of classification. The values of spectral characteristics of red and infrared channels of a complete set of cloudless satellite images during the growing period were used as input data (features). As a result, in 2018 some test calculations were conducted to estimate the area of agricultural crops in Kyiv Region. The results of evaluation of accuracy of the satellite-based agricultural crops area estimation using the statistical data showed that the lowest accuracy is typical for winter wheat and corn. The accuracy of soybeans and spring barley classification is quite low for most of the tested fields. Sunflower and rapeseed crops showed the highest accuracy. In order to improve the accuracy of classification, it is necessary to introduce more classification features (in a temporary aspect) by processing more satellite images during the growing period, and to increase the number of test samples through systematic sampling of ground data across the regions in Ukraine. We suggest using the scheme of main agricultural crops area estimation satellite-based system by the Hydrometeorological Centre of Ukraine.

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