LAND COVER MAPPING WITH ADVANCED CLASSIFICATION ALGORITHMS

Year 2018, Volume: 13 Issue: 3, 41 - 50, 23.07.2018

Çiğdem Şerifoğlu Yılmaz , Oguz Gungor , Hamdi Tolga Kahraman

Abstract

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%.

Keywords

Image Classification, Support Vector Machines, k-Nearest Neighbour, Artificial Neural Network

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