EGU21-14386
https://doi.org/10.5194/egusphere-egu21-14386
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Analysing Temporal Effects on Classification of SAR and Optical Images

Ahmet Batuhan Polat1, Ozgun Akcay1, and Fusun Balik Sanli2
Ahmet Batuhan Polat et al.
  • 1Canakkale Onsekiz Mart University, Faculty of Engineering, Geomatics Engineering, Canakkale, Turkey (abpolat@comu.edu.tr, akcay@comu.edu.tr)
  • 2Yildiz Technical University, Faculty of Civil Engineering, Geomatics Engineering, Istanbul, Turkey (fbalik@yildiz.edu.tr)

Obtaining high accuracy in land cover classification is a non-trivial problem in geosciences for monitoring urban and rural areas. In this study, different classification algorithms were tested with different types of data, and besides the effects of seasonal changes on these classification algorithms and the evaluation of the data used are investigated. In addition, the effect of increasing classification training samples on classification accuracy has been revealed as a result of the study. Sentinel-1 Synthetic Aperture Radar (SAR) images and Sentinel-2 multispectral optical images were used as datasets. Object-based approach was used for the classification of various fused image combinations. The classification algorithms Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighborhood (kNN) methods were used for this process. In addition, Normalized Difference Vegetation Index (NDVI) was examined separately to define the exact contribution to the classification accuracy.  As a result, the overall accuracies were compared by classifying the fused data generated by combining optical and SAR images. It has been determined that the increase in the number of training samples improve the classification accuracy. Moreover, it was determined that the object-based classification obtained from single SAR imagery produced the lowest classification accuracy among the used different dataset combinations in this study. In addition, it has been shown that NDVI data does not increase the accuracy of the classification in the winter season as the trees shed their leaves due to climate conditions.

How to cite: Polat, A. B., Akcay, O., and Balik Sanli, F.: Analysing Temporal Effects on Classification of SAR and Optical Images, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14386, https://doi.org/10.5194/egusphere-egu21-14386, 2021.

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