A comparative analysis of SVM, K-NN, and decision trees for high resolution satellite image scene classification

Samia Bouteldja; Assia Kourgli

doi:10.1117/12.2557563

31 January 2020 A comparative analysis of SVM, K-NN, and decision trees for high resolution satellite image scene classification

Samia Bouteldja, Assia Kourgli

Author Affiliations +

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114331I (2020) https://doi.org/10.1117/12.2557563
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

In this paper, we evaluate and compare the performance of three machine learning classifiers: Support Vector Machines (SVM), Decision Trees (DT) and K-Nearest Neighbor (K-NN) for high resolution satellite image scene classification.This study aims at providing insights into the selection of the appropriate classifier and highlighting the importance of the appropriate setting of the classifier parameters. We illustrate these issues through applying scene classification to UC-Merced high resolution satellite image dataset. Image features are obtained through the SURF descriptor and BOVW model.

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Samia Bouteldja and Assia Kourgli "A comparative analysis of SVM, K-NN, and decision trees for high resolution satellite image scene classification", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114331I (31 January 2020); https://doi.org/10.1117/12.2557563

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