A new approach to segmentation of remote sensing images with hidden markov models
In this work, we present a new segmentation algorithm for remote sensing images based on two-dimensional Hidden Markov Models (2D-HMM). In contrast to most 2D-HMM approaches, we do not use Viterbi Training, instead we propose to propagate the state probabilities through the image. Therefore, we denote our algorithm Complete Enumeration Propagation (CEP). To evaluate the performance of CEP, we compare it to the standard 2D-HMM approach called Path Constrained Viterbi Training (PCVT). As both algorithms are not restricted to a certain emission probability, we evaluate the performance of seven probability functions, namely Gamma, Generalized Extreme Value, inverse Gaussian, Logistic, Nakagami, Normal and Weibull. The experimental results show that our approach outperforms PCVT and other benchmark algorithms. Furthermore, we show that the choice of the probability distribution is crucial for many segmentation tasks.
Main Authors: | , , , |
---|---|
Format: | conferenceObject biblioteca |
Language: | eng |
Published: |
2014
|
Subjects: | Algorithm, 2D-HMM, Complete Enumeration Propagation, Path Constrained Viterbi Training, |
Online Access: | http://hdl.handle.net/11086/29331 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
dig-unc-ar-11086-29331 |
---|---|
record_format |
koha |
spelling |
dig-unc-ar-11086-293312022-11-16T18:02:53Z A new approach to segmentation of remote sensing images with hidden markov models Baumgartner, Josef Scavuzzo, Marcelo Rodríguez Rivero, Cristian Pucheta, Julián Algorithm 2D-HMM Complete Enumeration Propagation Path Constrained Viterbi Training In this work, we present a new segmentation algorithm for remote sensing images based on two-dimensional Hidden Markov Models (2D-HMM). In contrast to most 2D-HMM approaches, we do not use Viterbi Training, instead we propose to propagate the state probabilities through the image. Therefore, we denote our algorithm Complete Enumeration Propagation (CEP). To evaluate the performance of CEP, we compare it to the standard 2D-HMM approach called Path Constrained Viterbi Training (PCVT). As both algorithms are not restricted to a certain emission probability, we evaluate the performance of seven probability functions, namely Gamma, Generalized Extreme Value, inverse Gaussian, Logistic, Nakagami, Normal and Weibull. The experimental results show that our approach outperforms PCVT and other benchmark algorithms. Furthermore, we show that the choice of the probability distribution is crucial for many segmentation tasks. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6868456 Fil: Baumgartner, Josef. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Investigación Matemática Aplicada a Control; Argentina. Fil: Scavuzzo, Marcelo. Comision Nacional de Actividades Espaciales; Argentina. Fil: Rodríguez Rivero, Cristian. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Investigación Matemática Aplicada a Control; Argentina. Fil: Pucheta, Julián. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Laboratorio de Investigación Matemática Aplicada a Control; Argentina. Sistemas de Automatización y Control 2022-11-03T14:55:36Z 2022-11-03T14:55:36Z 2014 conferenceObject 978-1-4799-4269-5 http://hdl.handle.net/11086/29331 eng Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ Electrónico y/o Digital |
institution |
UNC AR |
collection |
DSpace |
country |
Argentina |
countrycode |
AR |
component |
Bibliográfico |
access |
En linea |
databasecode |
dig-unc-ar |
tag |
biblioteca |
region |
America del Sur |
libraryname |
Biblioteca 'Ing. Agrónomo Moisés Farber' de la Facultad de Ciencias Agropecuarias |
language |
eng |
topic |
Algorithm 2D-HMM Complete Enumeration Propagation Path Constrained Viterbi Training Algorithm 2D-HMM Complete Enumeration Propagation Path Constrained Viterbi Training |
spellingShingle |
Algorithm 2D-HMM Complete Enumeration Propagation Path Constrained Viterbi Training Algorithm 2D-HMM Complete Enumeration Propagation Path Constrained Viterbi Training Baumgartner, Josef Scavuzzo, Marcelo Rodríguez Rivero, Cristian Pucheta, Julián A new approach to segmentation of remote sensing images with hidden markov models |
description |
In this work, we present a new segmentation algorithm for remote sensing images based on two-dimensional Hidden Markov Models (2D-HMM). In contrast to most 2D-HMM approaches, we do not use Viterbi Training, instead we propose to propagate the state probabilities through the image. Therefore, we denote our algorithm Complete Enumeration Propagation (CEP). To evaluate the performance of CEP, we compare it to the standard 2D-HMM approach called Path Constrained Viterbi Training (PCVT).
As both algorithms are not restricted to a certain emission probability, we evaluate the performance of seven probability
functions, namely Gamma, Generalized Extreme Value, inverse Gaussian, Logistic, Nakagami, Normal and Weibull.
The experimental results show that our approach outperforms PCVT and other benchmark algorithms. Furthermore, we
show that the choice of the probability distribution is crucial for many segmentation tasks. |
format |
conferenceObject |
topic_facet |
Algorithm 2D-HMM Complete Enumeration Propagation Path Constrained Viterbi Training |
author |
Baumgartner, Josef Scavuzzo, Marcelo Rodríguez Rivero, Cristian Pucheta, Julián |
author_facet |
Baumgartner, Josef Scavuzzo, Marcelo Rodríguez Rivero, Cristian Pucheta, Julián |
author_sort |
Baumgartner, Josef |
title |
A new approach to segmentation of remote sensing images with hidden markov models |
title_short |
A new approach to segmentation of remote sensing images with hidden markov models |
title_full |
A new approach to segmentation of remote sensing images with hidden markov models |
title_fullStr |
A new approach to segmentation of remote sensing images with hidden markov models |
title_full_unstemmed |
A new approach to segmentation of remote sensing images with hidden markov models |
title_sort |
new approach to segmentation of remote sensing images with hidden markov models |
publishDate |
2014 |
url |
http://hdl.handle.net/11086/29331 |
work_keys_str_mv |
AT baumgartnerjosef anewapproachtosegmentationofremotesensingimageswithhiddenmarkovmodels AT scavuzzomarcelo anewapproachtosegmentationofremotesensingimageswithhiddenmarkovmodels AT rodriguezriverocristian anewapproachtosegmentationofremotesensingimageswithhiddenmarkovmodels AT puchetajulian anewapproachtosegmentationofremotesensingimageswithhiddenmarkovmodels AT baumgartnerjosef newapproachtosegmentationofremotesensingimageswithhiddenmarkovmodels AT scavuzzomarcelo newapproachtosegmentationofremotesensingimageswithhiddenmarkovmodels AT rodriguezriverocristian newapproachtosegmentationofremotesensingimageswithhiddenmarkovmodels AT puchetajulian newapproachtosegmentationofremotesensingimageswithhiddenmarkovmodels |
_version_ |
1756010475843223552 |