scholarly journals AVALIAÇÃO DO USO DA TERRA EM MICROBACIA UTILIZANDO UMA MATRIZ DE PARTIÇÃO FUZZY

Irriga ◽  
2007 ◽  
Vol 12 (2) ◽  
pp. 216-224
Author(s):  
Sérgio Campos ◽  
Edson Luís Piroli ◽  
Célia Regina Lopes Zimback ◽  
João Batista Tolentino Rodrigues
Keyword(s):  
Supervised Classification ◽  
Satellite Image ◽  
Human Knowledge ◽  
Classification Result ◽  
Maximum Likelihood Classification ◽  
Methodology Development ◽  
Fuzzy Partition ◽  
Covering Class ◽  
Analyze Data

AVALIAÇÃO DO USO DA TERRAEM MICROBACIA UTILIZANDO UMAMATRIZ DE PARTIÇÃO FUZZY  Sérgio Campos1; Edson Luís Piroli2; Célia Regina Lopes Zimback3; João Batista Tolentino Rodrigues31Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista, Botucatu, SP,  [email protected] Experimental de Rosana, Universidade Estadual Paulista,  Rosana, SP3Departamento de Ciências do Solo, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista, Botucatu, SP  1 RESUMO A evolução da informática oferece, hoje, a possibilidade de desenvolvimento de novas técnicas e metodologias para elaboração de trabalhos em todas as áreas do conhecimento humano. Aliado a isto, a capacidade de manuseio de grande volume de dados dos computadores pessoais atuais, facilita a criação e aplicação de novas ferramentas para análise de informações. Neste trabalho objetivou-se a aplicação de uma matriz de partição fuzzy para análise os dados obtidos pelo sensor TM do satélite Landsat 5, visando elaborar a classificação supervisionada do uso da terra na microbacia hidrográfica do Arroio das Pombas, no Município de Botucatu, SP. A atribuição de pesos no momento da criação das assinaturas, possibilitou que uma simples área de treinamento oferecesse entrada em mais de uma classe de cobertura. Constatou-se, também, uma modificação no resultado da classificação quando comparada com a classificação por máxima verossimilhança, principalmente com relação à maior homogeneidade e melhor definição das bordas das classes. UNITERMOS: matrix de partição fuzzy, classificação supervisionada, imagem de satélite.  CAMPOS, S., PIROLI, E.L., ZIMBACK, C.R.L., RODRIGUES, J.B.T.ANALYSIS OF SOIL USE IN A MICROBASIN USING A FUZZY PARTITION MATRIX  2 ABSTRACT Informatics evolution presently offers the possibility of new technique and methodology development for studies in all human knowledge areas. In addition, the present personal computer capacity of handling a large volume of data makes the creation and application of new analysis tools easy. This paper aimed the application of a fuzzy partition matrix to analyze data obtained from the Landsat 5 TMN sensor, in order to elaborate the supervised classification of land use in Arroio das Pombas microbasin, in Botucatu,SP,Brazil. It was possible that one single training area present input in more than one covering class due to weight attribution at the signature creation moment. A change in the classification result was also observed when compared to maximum likelihood classification, mainly when related to bigger uniformity and better class edges classification.KEYWORDS: fuzzy partition matrix, supervised classification, satellite image.

scholarly journals Assessing the Accuracy of Different Supervised Classification Methods of Satellite Image

2020 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Ibochi Andrew Abah ◽  
Richard jeremiah Uriah
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Accuracy Assessment ◽  
Satellite Image ◽  
Image Data ◽  
Image Process ◽  
Land Use Land Cover ◽  
Error Matrix

Assessing the accuracy of the classification map is an essential area in remote sensing digital image process. This is because a poorly classified map will result in inestimable errors of spatial analysis and modeling arising from the use of such data. This study was designed to evaluate different supervised classification algorithms in terms of accuracy assessment with a view of recommending an appropriate algorithm for image processing. The analysis was carried out using Andoni L.G.A. Rivers State, Nigeria as the study area. Supervised classification of ETM+ 2014 Landsat image of the study area was carried out using ENVI 5.0 software. Seven land use/land cover categories were identified on the image data and appropriate information classes were also assigned using region of interest. The classifiers adopted for the study include SAM, SVM, and MDC and each classifier was set using appropriate thresholds and parameters. The output error matrix of the classified map produced overall accuracy and kappa coefficient for MDC as 94.00% and 0.91, SAM as 64.45% and 0.53, and SVM as 98.92% and 0.98 respectively. The overall accuracy obtained from SVM indicates that a perfect classification map will be produced from the algorithm. The advanced supervised classification should be utilized for classification of land use/ land cover for both high and medium resolution images for improved classification accuracy.

scholarly journals Satellite Image Classification for Environmental Analysis using Image Processing

2020 ◽  
Vol 9 (3) ◽  
pp. 1513-1517
Keyword(s):  
Image Processing ◽  
Image Classification ◽  
Environmental Analysis ◽  
Supervised Classification ◽  
Satellite Image ◽  
Loss Of Life ◽  
Traditional Technique ◽  
Satellite Image Classification ◽  
The Future

Conventional supervised classification of satellite pictures utilizes a solitary multi-band picture and incidental ground perceptions to build phantom marks of land spread classes. We contrasted this methodology with three choices that get marks from different pictures and timespans. signature speculation, in this unearthly marks, is gotten from various pictures inside one season, however maybe from various years. signature extension, in this phantom marks, is made with information from pictures obtained during various periods of that year; and mixes of development and speculation. Utilizing the information for India, we evaluated the nature of these various marks to characterize the pictures used to infer the mark, and for use in transient mark expansion, i.e., applying a mark acquired from the information of one or quite a long while to pictures from different years. While applying marks to the pictures they were gotten from, signature development improved exactness comparative with the customary strategy, and inconstancy in precision declined uniquely. Conversely, signature speculation didn't improve grouping. While applying marks to pictures of different years (worldly expansion), the traditional technique, utilizing a mark got from a solitary picture, brought about extremely low characterization precision. Mark's development additionally performed ineffectively yet multi-year signature speculation performed much better and this seems, by all accounts, to be a promising methodology in the transient augmentation of ghastly marks for satellite picture arrangements. This project summarizes the different audits on satellite picture characterization strategies and systems. The summary helps the analysts to choose suitable satellite picture characterization strategies or methods dependent on the requirements. Later on, the results acquired from the proposed technique will be an extraordinary measure for anticipating and examining the effect of floods. It will help salvage groups to address high caution regions first in this way, least or no loss of life will be accomplished. In the future, the technique can be adjusted to be utilized for coastline location, urbanization, deforestation, and seismic tremors.

scholarly journals TARGETED SATELLITE IMAGE CLASSIFICATION FOR URBAN MAP UPDATING USING GEOSPATIAL INFORMATION SYSTEM PLATFORM

2015 ◽  
Vol XL-1-W5 ◽  
pp. 135-139
Author(s):  
M. Davoodianidaliki ◽  
A. Abedini
Keyword(s):  
Urban Areas ◽  
Supervised Classification ◽  
Satellite Image ◽  
Support Vector ◽  
Change Analysis ◽  
Geospatial Information ◽  
Map Updating ◽  
Misclassification Errors ◽  
Long Time

Traditional map production and updating methods which usually involve field surveying and/or photogrammetry, while established and used for a long time, are time consuming and costly. Whereas satellite imagery have provided great amounts of data with high resolutions suitable for different geospatial applications. This paper focuses on taking advantage of geospatial information systems for enabling automated supervised classification of satellite images in urban areas. Such ability is provided through some attributes that determine whether features in current map have changed or not. The overall process consists of three stages: i: Geo database upgrade for addition of some attributes; ii: Classification by Support Vector Machine (SVM) and iii: Change analysis. The proposed method is applied on a sample data of Worldview 3 image of Hormozgan, Iran. The obtained results show that using such method not only can automate supervised classification but also can decrease misclassification errors through local training. Also its independent of classification method provides the ability to deploy other classification methods.

scholarly journals Supervised Classification of Satellite Image Processing using Neural Networks

2020 ◽  
Vol 9 (7) ◽  
pp. 26-31
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Supervised Classification ◽  
Satellite Images ◽  
Satellite Image ◽  
Remote Sensing Technique ◽  
Erdas Imagine ◽  
Satellite Image Processing ◽  
Landsat Satellite

Now a day’s satellite image processing plays a major role. By using remote sensing technique, we can classify the satellite images like LISS (Linear image self-scanner), LANDSAT satellite image by using ERDAS imagine software. By using ERDAS imagine software, the classification of an satellite images will take more time. Rather than ERDAS imagine software we can use NEURAL NETWORKS in MATLAB software for classifying the satellite images by using the corresponding code with respect to the image by simply changing the file name. This paper includes the method like supervised and classification by using ERDAS imagine software and MATLAB code. The aim of this projects is to realize the image classification using NEURAL NETWORKS.

Effectiveness of Supervised Classification Models for Hate Speech on Twitter

2020 ◽  
Author(s):  
Kunal Srivastava ◽  
Ryan Tabrizi ◽  
Ayaan Rahim ◽  
Lauryn Nakamitsu
Keyword(s):  
Supervised Classification ◽  
Hate Speech ◽  
The Internet ◽  
Classification Models ◽  
Personal Attack ◽  
Political Beliefs ◽  
The Many ◽  
Primary Medium ◽  
Offensive Speech

<div> <div> <div> <p>Abstract </p> <p>The ceaseless connectivity imposed by the internet has made many vulnerable to offensive comments, be it their physical appearance, political beliefs, or religion. Some define hate speech as any kind of personal attack on one’s identity or beliefs. Of the many sites that grant the ability to spread such offensive speech, Twitter has arguably become the primary medium for individuals and groups to spread these hurtful comments. Such comments typically fail to be detected by Twitter’s anti-hate system and can linger online for hours before finally being taken down. Through sentiment analysis, this algorithm is able to distinguish hate speech effectively through the classification of sentiment. </p> </div> </div> </div>

scholarly journals A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

2021 ◽  
Vol 9 (5) ◽  
pp. 1034
Author(s):  
Carlos Sabater ◽  
Lorena Ruiz ◽  
Abelardo Margolles
Keyword(s):  
Machine Learning ◽  
Supervised Classification ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Human Milk Oligosaccharides ◽  
Future Studies ◽  
High Fiber ◽  
Machine Learning Approach ◽  
Prebiotic Oligosaccharides

This study aimed to recover metagenome-assembled genomes (MAGs) from human fecal samples to characterize the glycosidase profiles of Bifidobacterium species exposed to different prebiotic oligosaccharides (galacto-oligosaccharides, fructo-oligosaccharides and human milk oligosaccharides, HMOs) as well as high-fiber diets. A total of 1806 MAGs were recovered from 487 infant and adult metagenomes. Unsupervised and supervised classification of glycosidases codified in MAGs using machine-learning algorithms allowed establishing characteristic hydrolytic profiles for B. adolescentis, B. bifidum, B. breve, B. longum and B. pseudocatenulatum, yielding classification rates above 90%. Glycosidase families GH5 44, GH32, and GH110 were characteristic of B. bifidum. The presence or absence of GH1, GH2, GH5 and GH20 was characteristic of B. adolescentis, B. breve and B. pseudocatenulatum, while families GH1 and GH30 were relevant in MAGs from B. longum. These characteristic profiles allowed discriminating bifidobacteria regardless of prebiotic exposure. Correlation analysis of glycosidase activities suggests strong associations between glycosidase families comprising HMOs-degrading enzymes, which are often found in MAGs from the same species. Mathematical models here proposed may contribute to a better understanding of the carbohydrate metabolism of some common bifidobacteria species and could be extrapolated to other microorganisms of interest in future studies.

Sign in / Sign up

Export Citation Format

Share Document