scholarly journals Fast estimation for robust supervised classification with mixture models

2021 ◽  
Author(s):  
Erwan Giry Fouquet ◽  
Mathieu Fauvel ◽  
Clément Mallet
Keyword(s):  
Mixture Models ◽  
Supervised Classification ◽  
Fast Estimation

Fast estimation of Gaussian mixture models for image segmentation

2011 ◽  
Vol 23 (4) ◽  
pp. 773-789 ◽  
Author(s):  
Nicola Greggio ◽  
Alexandre Bernardino ◽  
Cecilia Laschi ◽  
Paolo Dario ◽  
José Santos-Victor
Keyword(s):  
Image Segmentation ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Fast Estimation

scholarly journals A classification method for binary predictors combining similarity measures and mixture models

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Seydou N. Sylla ◽  
Stéphane Girard ◽  
Abdou Ka Diongue ◽  
Aldiouma Diallo ◽  
Cheikh Sokhna
Keyword(s):  
Mixture Models ◽  
Verbal Autopsy ◽  
Supervised Classification ◽  
Similarity Measures ◽  
Classification Method ◽  
Classification Rule ◽  
New Classification ◽  
New Family ◽  
Autopsy Data ◽  
Handwritten Digit

AbstractIn this paper, a new supervised classification method dedicated to binary predictors is proposed. Its originality is to combine a model-based classification rule with similarity measures thanks to the introduction of new family of exponential kernels. Some links are established between existing similarity measures when applied to binary predictors. A new family of measures is also introduced to unify some of the existing literature. The performance of the new classification method is illustrated on two real datasets (verbal autopsy data and handwritten digit data) using 76 similarity measures.

Post Hoc Investigation of Factor Mixture Models

2007 ◽  
Author(s):  
Danielle L. Cisler ◽  
Gitta H. Lubke
Keyword(s):  
Mixture Models ◽  
Post Hoc

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

PEMANFAATAN DATA PENGINDERAAN JAUH DAN TEKNOLOGI SIG UNTUK MENGIDENTIFIKASI BUILT UP AREA DAN KAITANNYA DENGAN RTRW DI KABUPATEN KENDAL

2019 ◽  
Vol 3 ◽  
pp. 521
Author(s):  
Mailendra Mailendra
Keyword(s):  
Supervised Classification ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm

Integrasi data penginderaan jauh dengan sistem informasi geografis telah banyak dikembangkan, dan salah satunya dalam melihat perkembangan lahan terbangun. Tujuan penelitian ini adalah untuk melihat perkembangan lahan terbangun dan kesesuaiannya dengan Rencana Pola Ruang Kabupaten Kendal. Kemudian metode yang digunakan yaitu metode supervised classification dengan memanfaatkan data citra landsat 5 TM dan landsat 8 OLI yang selanjutnya dihitung luas dari masing lahan terbangun berdasarkan data temporal tahun 1990, tahun 2015 dan tahun 2017. Setelah diketahui luas lahan terbangun selanjutnya dioverlay dengan peta rencana pola ruang Kabupaten Kendal untuk melihat sesuai atau tidaknya penempatan lahan terbangun tersebut. Adapun hasil penelitiannya yaitu setiap tahunnya lahan terbangun terus meningkat di Kabupaten Kendal, terjadi peningkatan yang cukup signifikan dalam dua tahun terakhir yaitu tahun 2015 hingga tahun 2017. Selanjutnya diperkirakan 88 % lahan terbangun tersebut telah sesuai dengan RTRW karena sudah berada pada kawasan budidaya.

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