Comparison of Object and Pixel-Based Classifications For Mapping Crops Using Rapideye Imagery: A Case Study Of Menemen Plain, Turkey

Year 2018, Volume: 5 Issue: 2, 231 - 243, 01.08.2018

M. Tolga Esetlili , Filiz Bektas Balcik , Fusun Balik Sanli Kaan Kalkan Mustafa Ustuner , Cigdem Goksel , Cem Gazioğlu , Yusuf Kurucu

https://doi.org/10.30897/ijegeo.442002

Cited By: 21

Abstract

With the latest
development and increasing availability of high spatial resolution sensors,
earth observation technology offers a viable solution for crop identification
and management. There is a strong need to produce accurate, reliable and up to
date crop type maps for sustainable agriculture monitoring and management. In
this study, RapidEye, the first high-resolution multi-spectral satellite system
that operationally provides a Red-edge channel, was used to test the potential
of the data for crop type mapping. This study was investigated at a selected
region mostly covered with agricultural fields locates in the low lands of
Menemen (İzmir) Plain, TURKEY. The potential of the three classification
algorithms such as Maximum Likelihood Classification, Support Vector Machine
and Object Based Image Analysis is
tested. Accuracy assessment of land cover maps has been performed
through error matrix and kappa indexes. The results highlighted that all
selected classifiers as highly useful (over 90%) in mapping of crop types in
the study region however the object-based approach slightly outperforming the
Support Vector Machine classification by both overall accuracy and Kappa
statistics. The success of selected methods also underlines the potential of
RapidEye data for mapping crop types in Aegean region.

Keywords

crop type mapping, Pixel-based classification, Object-based classification

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