Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W1, 143-146, 2016
https://doi.org/10.5194/isprs-archives-XLII-2-W1-143-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
26 Oct 2016
DETERMINATION OF LAND COVER/LAND USE USING SPOT 7 DATA WITH SUPERVISED CLASSIFICATION METHODS
F. Bektas Balcik1 and A. Karakacan Kuzucu1,2 1ITU Civil Engineering Faculty, Geomatics Engineering, 34469 Maslak Istanbul, Turkey
2Buyukcekmece Municipality, 34535, Buyukcekmece, Istanbul, Turkey
Keywords: Land Cover / Land Use, Classification, Support Vector Machine, Maximum Likelihood Classification, Istanbul, SPOT 7 Abstract. Land use/ land cover (LULC) classification is a key research field in remote sensing. With recent developments of high-spatial-resolution sensors, Earth-observation technology offers a viable solution for land use/land cover identification and management in the rural part of the cities. There is a strong need to produce accurate, reliable, and up-to-date land use/land cover maps for sustainable monitoring and management. In this study, SPOT 7 imagery was used to test the potential of the data for land cover/land use mapping. Catalca is selected region located in the north west of the Istanbul in Turkey, which is mostly covered with agricultural fields and forest lands. The potentials of two classification algorithms maximum likelihood, and support vector machine, were tested, and accuracy assessment of the land cover maps was performed through error matrix and Kappa statistics. The results indicated that both of the selected classifiers were highly useful (over 83% accuracy) in the mapping of land use/cover in the study region. The support vector machine classification approach slightly outperformed the maximum likelihood classification in both overall accuracy and Kappa statistics.
Conference paper (PDF, 910 KB)


Citation: Bektas Balcik, F. and Karakacan Kuzucu, A.: DETERMINATION OF LAND COVER/LAND USE USING SPOT 7 DATA WITH SUPERVISED CLASSIFICATION METHODS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W1, 143-146, https://doi.org/10.5194/isprs-archives-XLII-2-W1-143-2016, 2016.

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