1st International Conference on Computing and Machine Intelligence (ICMI-2021) February 19-20, 2021, Istanbul, Turkey -- Editorial Board Dr. Akhtar JAMIL Dr. Alaa Ali HAMEED -- ISBN: 9786050667578 -- Istanbul Sabahattin Zaim University Yayınları; No. 57.Remote sensing data has played vital role in land use/land-cover applications. Many machine learning methods
have been proposed to obtain different land cover classes. In this
paper, we investigated the capabilities of two classifiers with
object-based segmentation for land cover classification from
high resolution multi-spectral images. First, graph-based
minimal spanning tree segmentation was applied to segment the
original image pixels into objects. From each object a set of
spectral, spatial and texture features were extracted. These
features were then used to train and test the artificial neural
network (ANN) and support vector machine (SVM). The
proposed method was evaluated on a dataset consisting of high
resolution multi-spectral images with four classes (tea area,
other trees, roads and builds, bare land). The experiments
showed that ANN was more accuracy as it scored average
accuracy of 82.60% while SVM produced 73.66%. Moreover,
when postprocessing using majority analysis was applied, the
average accuracy improved to 86.18%
