scholarly journals Review on Multitemporal Classification Methods of Satellite Images for Crop and Arable Land Recognition

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 999
Author(s):  
Joanna Pluto-Kossakowska
Keyword(s):  
Satellite Images ◽  
Arable Land ◽  
Machine Learning Algorithms ◽  
Automatic Identification ◽  
Classification Methods ◽  
Landsat Images ◽  
Significant Difference ◽  
Neural Network Classifiers ◽  
Optical Satellite Images ◽  
Selection Of

This paper presents a review of the conducted research in the field of multitemporal classification methods used for the automatic identification of crops and arable land using optical satellite images. The review and systematization of these methods in terms of the effectiveness of the obtained results and their accuracy allows for the planning towards further development in this area. The state of the art analysis concerns various methodological approaches, including selection of data in terms of spatial resolution, selection of algorithms, as well as external conditions related to arable land use, especially the structure of crops. The results achieved with use of various approaches and classifiers and subsequently reported in the literature vary depending on the crops and area of analysis and the sources of satellite data. Hence, their review and systematic conclusions are needed, especially in the context of the growing interest in automatic processes of identifying crops for statistical purposes or monitoring changes in arable land. The results of this study show no significant difference between the accuracy achieved from different machine learning algorithms, yet on average artificial neural network classifiers have results that are better by a few percent than others. For very fragmented regions, better results were achieved using Sentinel-2, SPOT-5 rather than Landsat images, but the level of accuracy can still be improved. For areas with large plots there is no difference in the level of accuracy achieved from any HR images.

A Comparison of Four Adolescent Language Tests

1987 ◽  
Vol 18 (3) ◽  
pp. 250-266 ◽  
Author(s):  
R. Jane Lieberman ◽  
Ann Marie C. Heffron ◽  
Stephanie J. West ◽  
Edward C. Hutchinson ◽  
Thomas W. Swem
Keyword(s):  
Test Performance ◽  
Sixth Grade ◽  
Significant Discrepancy ◽  
Language Impaired ◽  
Language Functions ◽  
Language Tests ◽  
Significant Difference ◽  
Sixth Grade Students ◽  
Overall Performance ◽  
Selection Of

Four recently developed adolescent language tests, the Fullerton Test for Adolescents (FLTA), the Test of Adolescent Language (TOAL), the Clinical Evaluation of Language Functions (CELF), and the Screening Test of Adolescent Language (STAL), were compared to determine: (a) whether they measured the same language skills (content) in the same way (procedures); and (b) whether students performed similarly on each of the tests. First, respective manuals were reviewed to compare selection of subtest content areas and subtest procedures. Then, each of the tests was administered according to standardized procedures to 30 unselected sixth-grade students. Despite apparent differences in test content and procedures, there was no significant difference in students' performance on three of the four tests, and correlations among test performance were moderate to high. A comparison of the pass/fail rates for overall performance on the tests, however, revealed a significant discrepancy between the proportions of students identified in need of further evaluation on the STAL (20%) and the proportion diagnosed as language impaired on the three diagnostic tests (60-73%). Clinical implications are discussed.

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

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

Caracterização da Peneira Média em Clones de Coffea canephora

REVISTA FIMCA ◽  
2018 ◽  
Vol 5 (2) ◽  
pp. 28-31
Author(s):  
Darlan Darlan Sanches Barbosa Alves ◽  
Victor Mouzinho Spinelli ◽  
Marcos Santana Moraes ◽  
Carolina Augusto De Souza ◽  
Rodrigo da Silva Ribeiro ◽  
...  
Keyword(s):  
Genetic Improvement ◽  
Block Design ◽  
Coffea Canephora ◽  
Coffee Production ◽  
Randomized Block Design ◽  
Significant Difference ◽  
The Mean ◽  
Four Blocks ◽  
Selection Of

Introdução: O estado de Rondônia se destaca como tradicional produtor de café, sendo o segundo maior produtor brasileiro de C. canephora. No melhoramento genético de C. canephora, a seleção de plantas de elevada peneira média está associada à bebida de qualidade superior. Objetivos: O objetivo desse estudo foi avaliar a variabilidade genética de clones de C. canephora para o tamanho dos grãos, mensurado a partir da avaliação da peneira média (PM). Materiais e Métodos: Para isso, foi conduzido ao longo de dois anos agrícolas experimento no campo experimental da Embrapa no município de Ouro Preto do Oeste-RO, para a avaliação da peneira média de 130 genótipos (clones) com características das variedades botânicas Conilon, Robusta e híbridos intervarietais. O delineamento experimental utilizado foi de blocos ao acaso, com quatro repetições de quatro plantas por parcela. Resultados: Não houve resultados significativos para a interação clones X anos, indicando uma maior consistência no comportamento das plantas ao longo do tempo. Porém foram observadas diferenças significativas para o tamanho dos grãos entre os genótipos avaliados, possibilitando selecionar genótipos superiores. Conclusão: Os genótipos agruparam-se em cinco classes de acordo com o teste de média, subsidiando a caracterização de um gradiente de variabilidade da característica avaliada ABSTRACTIntroduction: Coffea canephora accounts for approximately 35% of the world's coffee production. The state of Rondônia stands out as a traditional coffee producer, being the second largest Brazilian producer of C. canephora. In the classical genetic improvement of C. anephora, the selection of plants of high average sieve is associated with a drink of superior quality. Objectives: The objective of this udy was to evaluate the genetic variability of Coffea canephora clones for the agronomic medium sieve (PM). Materials and Methods: The experiment was conducted in the experimental field of Embrapa, municipality of OuroPreto do Oeste-RO, located at coordinates 10º44'53 "S and 62º12'57". One hundred thirty genotypes (clones) of botanical characteristics Conilon, Robusta and intervarietal hybrids were evaluated in the agricultural years 2013-2014 and 2014-2015. The experimental design was a randomized block design with four blocks and four plants per plot, spacing 3.5 x 1.5 meters between plants. Results: Significant difference was found for the grain size. According to the F test, at 5% probability, the genotypes were grouped into five classes according to the mean test. Conclusion: The results obtained subsidized the characterization of a variability gradient of the evaluated trait.

Temperature Selection of Rainbow Trout (Salmo gairdneri) Fingerlings in Vertical and Horizontal Gradients

1971 ◽  
Vol 28 (11) ◽  
pp. 1801-1804 ◽  
Author(s):  
R. W. McCauley ◽  
W. L. Pond
Keyword(s):  
Rainbow Trout ◽  
Salmo Gairdneri ◽  
Temperature Preference ◽  
Temperature Selection ◽  
Horizontal Gradients ◽  
Significant Difference ◽  
Preferred Temperatures ◽  
Laboratory Investigations ◽  
Horizontal Temperature Gradients ◽  
Selection Of

Preferred temperatures of underyearling rainbow trout (Salmo gairdneri) were determined in both vertical and horizontal temperature gradients. No statistically significant difference was found between the preferred temperatures by the two different methods. This suggests that the nature of the gradient plays a lesser role than generally believed in laboratory investigations of temperature preference.

scholarly journals Selection of Suitable Machine Learning Algorithms for Classification Tasks in Reverse Logistics

Procedia CIRP ◽  
2021 ◽  
Vol 96 ◽  
pp. 272-277
Author(s):  
Hannah Lickert ◽  
Aleksandra Wewer ◽  
Sören Dittmann ◽  
Pinar Bilge ◽  
Franz Dietrich
Keyword(s):  
Machine Learning ◽  
Reverse Logistics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Classification Tasks ◽  
Selection Of

scholarly journals Application of Satellite Sentinel-2 Images to Study Alternate Sandbars Movement at Lower Vistula River (Poland)

2021 ◽  
Vol 13 (8) ◽  
pp. 1505
Author(s):  
Klaudia Kryniecka ◽  
Artur Magnuszewski
Keyword(s):  
Satellite Images ◽  
Average Distance ◽  
Low Flow ◽  
Visual Interpretation ◽  
Time Intervals ◽  
Manual Delineation ◽  
Vistula River ◽  
Interclass Correlation ◽  
Optical Satellite Images ◽  
Sentinel 2

The lower Vistula River was regulated in the years 1856–1878, at a distance of 718–939 km. The regulation plan did not take into consideration the large transport of the bed load. The channel was shaped using simplified geometry—too wide for the low flow and overly straight for the stabilization of the sandbar movement. The hydraulic parameters of the lower Vistula River show high velocities of flow and high shear stress. The movement of the alternate sandbars can be traced on the optical satellite images of Sentinel-2. In this study, a method of sandbar detection through the remote sensing indices, Sentinel Water Mask (SWM) and Automated Water Extraction Index no shadow (AWEInsh), and the manual delineation with visual interpretation (MD) was used on satellite images of the lower Vistula River, recorded at the time of low flows (20 August 2015, 4 September 2016, 30 July 2017, 20 September 2018, and 29 August 2019). The comparison of 32 alternate sandbar areas obtained by SWM, AWEInsh, and MD manual delineation methods on the Sentinel-2 images, recorded on 20 August 2015, was performed by the statistical analysis of the interclass correlation coefficient (ICC). The distance of the shift in the analyzed time intervals between the image registration dates depends on the value of the mean discharge (MQ). The period from 30 July 2017 to 20 September 2018 was wet (MQ = 1140 m3 × s−1) and created conditions for the largest average distance of the alternate sandbar shift, from 509 to 548 m. The velocity of movement, calculated as an average shift for one day, was between 1.2 and 1.3 m × day−1. The smallest shift of alternate sandbars was characteristic of the low flow period from 20 August 2015 to 4 September 2016 (MQ = 306 m3 × s−1), from 279 to 310 m, with an average velocity from 0.7 to 0.8 m × day−1.

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