Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7, 195-198, 2014
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7/195/2014/
doi:10.5194/isprsarchives-XL-7-195-2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
19 Sep 2014
Crop Type Classification Using Vegetation Indices of RapidEye Imagery
M. Ustuner1, F. B. Sanli1, S. Abdikan2, M. T. Esetlili3, and Y. Kurucu3 1Yildiz Technical University, Faculty of Civil Engineering, Department of Geomatic Engineering, 34220 Istanbul, Turkey
2Department of Geomatics Engineering, Bulent Ecevit University, 67100 Zonguldak, Turkey
3Ege University, Faculty of Agriculture, Department of Soil Science and Plant Nutrition, 35100 Bornova-İzmir/ Turkey
Keywords: Vegetation indices, RapidEye, NDVI, NDRE, GNDVI, SVM Abstract. Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.
Conference paper (PDF, 1478 KB)


Citation: Ustuner, M., Sanli, F. B., Abdikan, S., Esetlili, M. T., and Kurucu, Y.: Crop Type Classification Using Vegetation Indices of RapidEye Imagery, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7, 195-198, doi:10.5194/isprsarchives-XL-7-195-2014, 2014.

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