Abstract
In this study, the success of different satellite images and classification approaches in land cover (LC) classification were compared. A total of six satellite images, including two passive (Landsat 8 OLI (L8) and Sentinel-2 (S2)) satellite images and four fused satellite images from active (Sentinel-1(S1)-VH and VV polarization) and passive satellite images (L8-S1-VH, L8-S1-VV, S2-S1-VH and S2-S1-VV) were used in the classification in the study. For this purpose, L8, S2, L8-S1-VH, L8-S1-VV, S2-S1-VH and S2-S1-VV satellite images were classified according to three ((Maximum Likelihood Classification (MLC), Support Vector Machine (SVM) and Artificial Neural Networks (ANN)) different image classification approaches using the forest cover types map as gorund data. The results obtained from classification methods were evaluated based on overall accuracies (OA) and kappa coefficients (KC). When the classification successes obtained from the three classification methods are evaluated, it was observed that the KC ranged from 0.66 to 0.95 and the OA ranged from 76.82% to 96.67. The results indicated that the highest OA was displayed by MLC (ranged 85.33% to 96.67%), closely followed by SVM (ranged 80.11% to 91.93%), and finally ANN (ranged 76.82% to 89.92%). In addition, a comparison of classification performance using three utilized classification algorithms was performed. The S1-VH; S1-VV and, S2 and L8 fused images classified with an MLC algorithm produce the most accurate LC map, indicating an OA of 92.00%, 94.00%, 96.67%, 93.33% and a KC of 0.90, 0.93, 0.95, 0.92 for S2 and L8, respectively. Thus, it can be concluded that fused of satellite images improve the accuracies of LC classification.
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