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Performance Evaluation of Classical Classifiers and Deep Learning Approaches for Polymers Classification Based on Hyperspectral Images

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Advances in Computational Intelligence (IWANN 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12862))

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Abstract

Plastics are very valuable material for their desirable characteristics being one of them, their durability. But this characteristic turns plastics into an environmental problem when they end in the environment, and they become one source of contamination that can last for centuries. Thus, the first step for effective recycling is to identify correctly the types of plastics. In this paper, different classical classifiers as Random Forest, KNN, or SVM are compared with 1-D CNN and LSTM to classify plastics from hyperspectral images. Also, Partial Least Squares Discriminant Analysis has been included as the baseline because is one of the most widely used classifiers in the field of the Chemometrics community. The images were preprocessed with several techniques as Standard Normal Variate or Savitzky-Golay Polynomial Derivative to compare their effectiveness with raw data with the classifiers. The experiments were carried out using hyperspectral images with a 240 bands spectrum, and six types of polymers were considered (PE, PA, PP, PS, PVC, EPS). The best results were obtained with SVM+RBF and 1-D CNN with an accuracy of 99.41% and 99.31% respectively, preprocessing the images previously with Standard Normal Variate. Also, PCA and t-SNE methods were tested for dimensionality reduction, but they don’t improve the classifier performance.

This work was supported in part by the project IMPLAMAC (MAC2/1.1a/265) financed by the Interreg MAC (European Fund to Regional Development, Macaronesian Cooperation).

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Authors and Affiliations

  1. Universidad de Las Palmas de Gran Canaria, Instituto Universitario SIANI, 35017, Las Palmas, Spain

    Javier Lorenzo-Navarro

  2. Department of Chemical Materials, Environmental Engineering, Sapienza University of Rome, Rome, Italy

    Silvia Serranti, Giuseppe Bonifazi & Giuseppe Capobianco

Authors
  1. Javier Lorenzo-Navarro
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  2. Silvia Serranti
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  3. Giuseppe Bonifazi
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  4. Giuseppe Capobianco
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Corresponding author

Correspondence to Javier Lorenzo-Navarro .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Málaga, Málaga, Spain

    Gonzalo Joya

  3. Technical University of Catalonia, Barcelona, Spain

    Andreu Català

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Lorenzo-Navarro, J., Serranti, S., Bonifazi, G., Capobianco, G. (2021). Performance Evaluation of Classical Classifiers and Deep Learning Approaches for Polymers Classification Based on Hyperspectral Images. In: Rojas, I., Joya, G., Català, A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12862. Springer, Cham. https://doi.org/10.1007/978-3-030-85099-9_23

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  • DOI: https://doi.org/10.1007/978-3-030-85099-9_23

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