Classication of Agricultural Fields in Satellite Images Using Two-Dimensional Hidden Markov Models

Image segmentation is a key competence for many real life applications such as precision agriculture. In this work we present an approach to classify agricultural fields in noisy satellite images. We start with the Markovian neighborhood hypothesis from where on we derive a general two-dimension...

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Autores principales: Baumgartner, J., Giménez, J., Pucheta, J., Flesia, A. G.
Formato: conferenceObject
Lenguaje:Inglés
Publicado: 2021
Materias:
Satellite farming
pattern recognition
image segmentation
hidden Markov models
Acceso en línea:http://hdl.handle.net/11086/21531
Aporte de:
Repositorio Digital Universitario (UNC) de Universidad Nacional de Córdoba
id I10-R14111086-21531
record_format dspace
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-141
collection Repositorio Digital Universitario (UNC)
language Inglés
topic Satellite farming
pattern recognition
image segmentation
hidden Markov models
spellingShingle Satellite farming
pattern recognition
image segmentation
hidden Markov models
Baumgartner, J.
Giménez, J.
Pucheta, J.
Flesia, A. G.
Classication of Agricultural Fields in Satellite Images Using Two-Dimensional Hidden Markov Models
topic_facet Satellite farming
pattern recognition
image segmentation
hidden Markov models
description Image segmentation is a key competence for many real life applications such as precision agriculture. In this work we present an approach to classify agricultural fields in noisy satellite images. We start with the Markovian neighborhood hypothesis from where on we derive a general two-dimensional hidden Markov model (2D-HMM). To make the 2D-HMM feasible we apply the Path-Constrained Variable-State Viterbi Algorithm (PCVSVA) which allows us to approximate the optimal hidden state map. We evaluate the PCVSVA for a Landsat image of the province of C´ordoba, Argentina and a synthetic satellite image. In both cases we use Cohen’s κb coefficient to compare the PCVSVA and the solution obtained by maximum likelihood (ML) to show the effectiveness of 2D-HMM of solving image segmentation tasks.
format conferenceObject
author Baumgartner, J.
Giménez, J.
Pucheta, J.
Flesia, A. G.
author_facet Baumgartner, J.
Giménez, J.
Pucheta, J.
Flesia, A. G.
author_sort Baumgartner, J.
title Classication of Agricultural Fields in Satellite Images Using Two-Dimensional Hidden Markov Models
title_short Classication of Agricultural Fields in Satellite Images Using Two-Dimensional Hidden Markov Models
title_full Classication of Agricultural Fields in Satellite Images Using Two-Dimensional Hidden Markov Models
title_fullStr Classication of Agricultural Fields in Satellite Images Using Two-Dimensional Hidden Markov Models
title_full_unstemmed Classication of Agricultural Fields in Satellite Images Using Two-Dimensional Hidden Markov Models
title_sort classication of agricultural fields in satellite images using two-dimensional hidden markov models
publishDate 2021
url http://hdl.handle.net/11086/21531
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AT gimenezj classicationofagriculturalfieldsinsatelliteimagesusingtwodimensionalhiddenmarkovmodels
AT puchetaj classicationofagriculturalfieldsinsatelliteimagesusingtwodimensionalhiddenmarkovmodels
AT flesiaag classicationofagriculturalfieldsinsatelliteimagesusingtwodimensionalhiddenmarkovmodels
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