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Scene Classification of Remotely Sensed Images using Ensembled Machine Learning Models

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Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 749))

Abstract

Classification of remote sensing images (RSIs) is a challenging task and has become an active research topic in the field of remote sensing community. Over the past six decades, variety of machine learning algorithms such as logistic regression (LR), K-nearest neighbours (K-NN), random forest (RF), support vector machine (SVM) and multilayer perceptron (MLP) has been applied for scene classification. In order to improve robustness over a single model, we have introduced a hybrid approach called as ensembling which is nothing but training multiple models instead of a single model and to combine predictions from these models. Five different ensemble methods, namely AdaBoost, bagging, majority voting, weighted voting and stacking, are evaluated in this paper. For evaluating the proposed approach, we have collected 8000 remote sensing images from PatternNet dataset and found that ensembling majority voting technique applied with MLP, SVM-linear, SVM-kernel and RF classifiers shows an out performance of 93.5% accuracy which is higher than the individual classifiers.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, India

    P. Deepan & L. R. Sudha

Authors
  1. P. Deepan
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  2. L. R. Sudha
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Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, India

    E. S. Gopi

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Deepan, P., Sudha, L.R. (2021). Scene Classification of Remotely Sensed Images using Ensembled Machine Learning Models. In: Gopi, E.S. (eds) Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication. Lecture Notes in Electrical Engineering, vol 749. Springer, Singapore. https://doi.org/10.1007/978-981-16-0289-4_39

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