Evaluating the performance of a Random Forest Kernel for land cover classification

Azar Zafari (Corresponding Author), Raul Zurita-Milla, Emma Izquierdo-Verdiguier

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2 Citations (Scopus)
18 Downloads (Pure)

Abstract

The production of land cover maps through satellite image classification is a frequent task in remote sensing. Random Forest (RF) and Support Vector Machine (SVM) are the two most well-known and recurrently used methods for this task. In this paper, we evaluate the pros and cons of using an RF-based kernel (RFK) in an SVM compared to using the conventional Radial Basis Function (RBF) kernel and standard RF classifier. A time series of seven multispectralWorldView-2 images acquired over Sukumba (Mali) and a single hyperspectral AVIRIS image acquired over Salinas Valley (CA, USA) are used to illustrate the analyses. For each study area, SVM-RFK, RF, and SVM-RBF were trained and tested under different conditions over ten subsets. The spectral features for Sukumba were extended by obtaining vegetation indices (VIs) and grey-level co-occurrence matrices (GLCMs), the Salinas dataset is used as benchmarking with its original number of features. In Sukumba, the overall accuracies (OAs) based on the spectral features only are of 81.34%, 81.08% and 82.08% for SVM-RFK, RF, and SVM-RBF. Adding VI and GLCM features results in OAs of 82.%, 80.82% and 77.96%. In Salinas, OAs are of 94.42%, 95.83% and 94.16%. These results show that SVM-RFK yields slightly higher OAs than RF in high dimensional and noisy experiments, and it provides competitive results in the rest of the experiments. They also show that SVM-RFK generates highly competitive results when compared to SVM-RBF while substantially reducing the time and computational cost associated with parametrizing the kernel. Moreover, SVM-RFK outperforms SVM-RBF in high dimensional and noisy problems. RF was also used to select the most important features for the extended dataset of Sukumba; the SVM-RFK derived from these features improved the OA of the previous SVM-RFK by 2%. Thus, the proposed SVM-RFK classifier is as at least as good as RF and SVM-RBF and can achieve considerable improvements when applied to high dimensional data and when combined with RF-based feature selection methods.

Original languageEnglish
Article number575
Pages (from-to)1-20
Number of pages20
JournalRemote sensing
Volume11
Issue number5
DOIs
Publication statusPublished - 1 Mar 2019

Fingerprint

land cover
support vector machine
vegetation index
AVIRIS
matrix
benchmarking
image classification
experiment
time series
remote sensing
valley

Keywords

  • Image classification
  • Random forest
  • Random forest kernel
  • Support vector machine
  • Very high spatial resolution satellite images
  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-GOLD

Cite this

@article{3a6964f48b874621b2ccc53b614ca7f9,
title = "Evaluating the performance of a Random Forest Kernel for land cover classification",
abstract = "The production of land cover maps through satellite image classification is a frequent task in remote sensing. Random Forest (RF) and Support Vector Machine (SVM) are the two most well-known and recurrently used methods for this task. In this paper, we evaluate the pros and cons of using an RF-based kernel (RFK) in an SVM compared to using the conventional Radial Basis Function (RBF) kernel and standard RF classifier. A time series of seven multispectralWorldView-2 images acquired over Sukumba (Mali) and a single hyperspectral AVIRIS image acquired over Salinas Valley (CA, USA) are used to illustrate the analyses. For each study area, SVM-RFK, RF, and SVM-RBF were trained and tested under different conditions over ten subsets. The spectral features for Sukumba were extended by obtaining vegetation indices (VIs) and grey-level co-occurrence matrices (GLCMs), the Salinas dataset is used as benchmarking with its original number of features. In Sukumba, the overall accuracies (OAs) based on the spectral features only are of 81.34{\%}, 81.08{\%} and 82.08{\%} for SVM-RFK, RF, and SVM-RBF. Adding VI and GLCM features results in OAs of 82.{\%}, 80.82{\%} and 77.96{\%}. In Salinas, OAs are of 94.42{\%}, 95.83{\%} and 94.16{\%}. These results show that SVM-RFK yields slightly higher OAs than RF in high dimensional and noisy experiments, and it provides competitive results in the rest of the experiments. They also show that SVM-RFK generates highly competitive results when compared to SVM-RBF while substantially reducing the time and computational cost associated with parametrizing the kernel. Moreover, SVM-RFK outperforms SVM-RBF in high dimensional and noisy problems. RF was also used to select the most important features for the extended dataset of Sukumba; the SVM-RFK derived from these features improved the OA of the previous SVM-RFK by 2{\%}. Thus, the proposed SVM-RFK classifier is as at least as good as RF and SVM-RBF and can achieve considerable improvements when applied to high dimensional data and when combined with RF-based feature selection methods.",
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author = "Azar Zafari and Raul Zurita-Milla and Emma Izquierdo-Verdiguier",
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Evaluating the performance of a Random Forest Kernel for land cover classification. / Zafari, Azar (Corresponding Author); Zurita-Milla, Raul; Izquierdo-Verdiguier, Emma.

In: Remote sensing, Vol. 11, No. 5, 575, 01.03.2019, p. 1-20.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Zafari, Azar

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N2 - The production of land cover maps through satellite image classification is a frequent task in remote sensing. Random Forest (RF) and Support Vector Machine (SVM) are the two most well-known and recurrently used methods for this task. In this paper, we evaluate the pros and cons of using an RF-based kernel (RFK) in an SVM compared to using the conventional Radial Basis Function (RBF) kernel and standard RF classifier. A time series of seven multispectralWorldView-2 images acquired over Sukumba (Mali) and a single hyperspectral AVIRIS image acquired over Salinas Valley (CA, USA) are used to illustrate the analyses. For each study area, SVM-RFK, RF, and SVM-RBF were trained and tested under different conditions over ten subsets. The spectral features for Sukumba were extended by obtaining vegetation indices (VIs) and grey-level co-occurrence matrices (GLCMs), the Salinas dataset is used as benchmarking with its original number of features. In Sukumba, the overall accuracies (OAs) based on the spectral features only are of 81.34%, 81.08% and 82.08% for SVM-RFK, RF, and SVM-RBF. Adding VI and GLCM features results in OAs of 82.%, 80.82% and 77.96%. In Salinas, OAs are of 94.42%, 95.83% and 94.16%. These results show that SVM-RFK yields slightly higher OAs than RF in high dimensional and noisy experiments, and it provides competitive results in the rest of the experiments. They also show that SVM-RFK generates highly competitive results when compared to SVM-RBF while substantially reducing the time and computational cost associated with parametrizing the kernel. Moreover, SVM-RFK outperforms SVM-RBF in high dimensional and noisy problems. RF was also used to select the most important features for the extended dataset of Sukumba; the SVM-RFK derived from these features improved the OA of the previous SVM-RFK by 2%. Thus, the proposed SVM-RFK classifier is as at least as good as RF and SVM-RBF and can achieve considerable improvements when applied to high dimensional data and when combined with RF-based feature selection methods.

AB - The production of land cover maps through satellite image classification is a frequent task in remote sensing. Random Forest (RF) and Support Vector Machine (SVM) are the two most well-known and recurrently used methods for this task. In this paper, we evaluate the pros and cons of using an RF-based kernel (RFK) in an SVM compared to using the conventional Radial Basis Function (RBF) kernel and standard RF classifier. A time series of seven multispectralWorldView-2 images acquired over Sukumba (Mali) and a single hyperspectral AVIRIS image acquired over Salinas Valley (CA, USA) are used to illustrate the analyses. For each study area, SVM-RFK, RF, and SVM-RBF were trained and tested under different conditions over ten subsets. The spectral features for Sukumba were extended by obtaining vegetation indices (VIs) and grey-level co-occurrence matrices (GLCMs), the Salinas dataset is used as benchmarking with its original number of features. In Sukumba, the overall accuracies (OAs) based on the spectral features only are of 81.34%, 81.08% and 82.08% for SVM-RFK, RF, and SVM-RBF. Adding VI and GLCM features results in OAs of 82.%, 80.82% and 77.96%. In Salinas, OAs are of 94.42%, 95.83% and 94.16%. These results show that SVM-RFK yields slightly higher OAs than RF in high dimensional and noisy experiments, and it provides competitive results in the rest of the experiments. They also show that SVM-RFK generates highly competitive results when compared to SVM-RBF while substantially reducing the time and computational cost associated with parametrizing the kernel. Moreover, SVM-RFK outperforms SVM-RBF in high dimensional and noisy problems. RF was also used to select the most important features for the extended dataset of Sukumba; the SVM-RFK derived from these features improved the OA of the previous SVM-RFK by 2%. Thus, the proposed SVM-RFK classifier is as at least as good as RF and SVM-RBF and can achieve considerable improvements when applied to high dimensional data and when combined with RF-based feature selection methods.

KW - Image classification

KW - Random forest

KW - Random forest kernel

KW - Support vector machine

KW - Very high spatial resolution satellite images

KW - ITC-ISI-JOURNAL-ARTICLE

KW - ITC-GOLD

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