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<p>Satellite image analysis requires computational proficiency and integration of data visualization strategies. However, traditional techniques are unable to classify satellite images due to a lack of interoperability issues. Thus, the given paper introduces an ensemble machine learning (ML) approach, utilizing the Orange tool to undertake image classification tasks specifically with the EuroSAT dataset. Orange is a free and open-source program used for pre-processing, visualization, and modeling of data. The classifiers used in the proposed approach are Naïve Bayes (NB), Neural Networks (NN), and Random Forest (RF). The EuroSAT dataset consists of satellite images of land cover types across Europe, with several classes ranging from natural landscapes to urban areas. The results show that the Orange tool, combined with the proposed approach (NB + NN + RF), can effectively classify EuroSAT images with high accuracy and performance, making it a valuable tool for remote sensing and land use applications. The proposed approach achieves the highest classification accuracy, thereby surpassing existing ML techniques.</p>

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NB-NN-RF: an ensemble machine learning approach for classification of satellite images

  • 期刊论文

  • 发表日期：

    2025年7月2日

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  • Reena Thakur,
  • Pradnya Borkar,
  • Varsha Kothari,
  • Gaurav Sharma,
  • Vinay Kumar Singh,
  • Sagarkumar Badhiye,
  • Sudhir Shelke

摘要

Satellite image analysis requires computational proficiency and integration of data visualization strategies. However, traditional techniques are unable to classify satellite images due to a lack of interoperability issues. Thus, the given paper introduces an ensemble machine learning (ML) approach, utilizing the Orange tool to undertake image classification tasks specifically with the EuroSAT dataset. Orange is a free and open-source program used for pre-processing, visualization, and modeling of data. The classifiers used in the proposed approach are Naïve Bayes (NB), Neural Networks (NN), and Random Forest (RF). The EuroSAT dataset consists of satellite images of land cover types across Europe, with several classes ranging from natural landscapes to urban areas. The results show that the Orange tool, combined with the proposed approach (NB + NN + RF), can effectively classify EuroSAT images with high accuracy and performance, making it a valuable tool for remote sensing and land use applications. The proposed approach achieves the highest classification accuracy, thereby surpassing existing ML techniques.