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Journals / Nature Sciences / 2026 / Cilt: 13 Sayı: 3

LAND COVER MAPPING WITH ADVANCED CLASSIFICATION ALGORITHMS

Çiğdem Şerifoğlu Yılmaz(Karadeniz Technical University)0000-0002-9738-5124
Oguz Gungor(Karadeniz Technical University)0000-0002-3280-5466
Hamdi Tolga Kahraman(Karadeniz Technical University)0000-0001-9985-6324
Pages
41–50
Publication Date
2018-07-23
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The SVM algorithm classified the imagery with the best classification accuracy of 72.38%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Remote sensing technologies are used in many applications to extract information from the surface of the earth.
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Key Findings & Data:This, of course, has encouraged the remote sensing community to develop more advanced classification algorithms to further increase classification accuracy.
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Conclusion & Impact:The SVM algorithm classified the imagery with the best classification accuracy of 72.38%.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Image Classification · Support Vector Machines · k-Nearest Neighbour

Özet

Remote sensing technologies are used in many applications to extract information from the surface of the earth. Image classification, which is one of the most widely-used ways of information extraction, is a controversial topic in remote sensing. This is because all classification algorithms introduced in the literature cause classification errors to some extent. Simple classification algorithms like Minimum Distance, Parallelpiped and Mahalanobis Distance commit a large amount of classification errors. This, of course, has encouraged the remote sensing community to develop more advanced classification algorithms to further increase classification accuracy. This study uses sophisticated classification algorithms Support Vector Machines (SVM), k-Nearest Neighbour (kNN) and Artificial Neural Network (ANN) to classify a WorldView-2 multispectral image in order to produce land cover maps. The accuracies of the produced thematic maps were evaluated with randomly-selected control points. The SVM algorithm classified the imagery with the best classification accuracy of 72.38%.

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Cite This Article

Yılmaz, Ç. Ş., Gungor, O., Kahraman, H. T. (2018). LAND COVER MAPPING WITH ADVANCED CLASSIFICATION ALGORITHMS. Nature Sciences, Cilt: 13 Sayı: 3, 41–50. https://ajindex.com/articles/1657571

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