Improving land cover classification through contextual-based optimum-path forest

•A new contextual classifier based on optimum-path forest has been presented (OPF–MRF).•A meta-heuristic-based framework has been proposed to estimate the contextual-dependent parameter for OPF–MRF.•The proposed approach has been validated in the context of satellite image classification. Traditiona...

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Published inInformation sciences Vol. 324; pp. 60 - 87
Main Authors Osaku, D., Nakamura, R.Y.M., Pereira, L.A.M., Pisani, R.J., Levada, A.L.M., Cappabianco, F.A.M., Falcão, A.X., Papa, João P.
Format Journal Article
LanguageEnglish
Published Elsevier Inc 10.12.2015
Subjects
Algorithms
Classification
Contextual classification
Forests
Image classification
Land cover
Land cover classification
Magnetorheological fluids
Optimum-path forest
Satellite imagery
Statistical methods
Contextual classification
Optimum-path forest
Land cover classification
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Abstract •A new contextual classifier based on optimum-path forest has been presented (OPF–MRF).•A meta-heuristic-based framework has been proposed to estimate the contextual-dependent parameter for OPF–MRF.•The proposed approach has been validated in the context of satellite image classification. Traditional machine learning algorithms very often assume statistically independent data samples. However, this is clearly not the case in remote sensing image applications, in which pixels present spatial and/or temporal dependencies. In this work, it has been presented an approach to improve land cover image classification using a contextual approach based on optimum-path forest (OPF) and the well-known Markov random fields (MRFs), hereinafter called OPF–MRF. In addition, it is also introduced a framework to the optimization of the amount of contextual information used by OPF–MRF. Experiments over high- and medium-resolution satellite (CBERS-2B, Landsat 5 TM, Ikonos-2 MS and Geoeye) and radar (ALOS-PALSAR) images covering the area of two Brazilian cities have shown the proposed approach can overcome several shortcomings related to standard OPF classification. In some cases, the proposed approach outperformed traditional OPF in about 9% of recognition rate, which is crucial for land cover classification.
AbstractList Traditional machine learning algorithms very often assume statistically independent data samples. However, this is clearly not the case in remote sensing image applications, in which pixels present spatial and/or temporal dependencies. In this work, it has been presented an approach to improve land cover image classification using a contextual approach based on optimum-path forest (OPF) and the well-known Markov random fields (MRFs), hereinafter called OPF-MRF. In addition, it is also introduced a framework to the optimization of the amount of contextual information used by OPF-MRF. Experiments over high- and medium-resolution satellite (CBERS-2B, Landsat 5 TM, Ikonos-2 MS and Geoeye) and radar (ALOS-PALSAR) images covering the area of two Brazilian cities have shown the proposed approach can overcome several shortcomings related to standard OPF classification. In some cases, the proposed approach outperformed traditional OPF in about 9% of recognition rate, which is crucial for land cover classification.
•A new contextual classifier based on optimum-path forest has been presented (OPF–MRF).•A meta-heuristic-based framework has been proposed to estimate the contextual-dependent parameter for OPF–MRF.•The proposed approach has been validated in the context of satellite image classification. Traditional machine learning algorithms very often assume statistically independent data samples. However, this is clearly not the case in remote sensing image applications, in which pixels present spatial and/or temporal dependencies. In this work, it has been presented an approach to improve land cover image classification using a contextual approach based on optimum-path forest (OPF) and the well-known Markov random fields (MRFs), hereinafter called OPF–MRF. In addition, it is also introduced a framework to the optimization of the amount of contextual information used by OPF–MRF. Experiments over high- and medium-resolution satellite (CBERS-2B, Landsat 5 TM, Ikonos-2 MS and Geoeye) and radar (ALOS-PALSAR) images covering the area of two Brazilian cities have shown the proposed approach can overcome several shortcomings related to standard OPF classification. In some cases, the proposed approach outperformed traditional OPF in about 9% of recognition rate, which is crucial for land cover classification.
Author Papa, João P.
Nakamura, R.Y.M.
Pereira, L.A.M.
Levada, A.L.M.
Falcão, A.X.
Osaku, D.
Cappabianco, F.A.M.
Pisani, R.J.
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Keywords Contextual classification
Optimum-path forest
Land cover classification
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Snippet •A new contextual classifier based on optimum-path forest has been presented (OPF–MRF).•A meta-heuristic-based framework has been proposed to estimate the...
Traditional machine learning algorithms very often assume statistically independent data samples. However, this is clearly not the case in remote sensing image...
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StartPage 60
SubjectTerms Algorithms
Classification
Contextual classification
Forests
Image classification
Land cover
Land cover classification
Magnetorheological fluids
Optimum-path forest
Satellite imagery
Statistical methods
Title Improving land cover classification through contextual-based optimum-path forest
URI https://dx.doi.org/10.1016/j.ins.2015.06.020
https://search.proquest.com/docview/1778020691
Volume 324
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