Ali Shojaeeian; Sadegh Mokhtari Chelche; Leila Keshtkar; Esmaeil Soleymani rad
Abstract
Nowadays, remote sensing data is able to provide the latest information for the study of land cover and land uses. These images are of high importancedue to the presentation of timely information, diversity of forms, being digital and the possibility of processing in the preparation of user maps.Determining ...
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Nowadays, remote sensing data is able to provide the latest information for the study of land cover and land uses. These images are of high importancedue to the presentation of timely information, diversity of forms, being digital and the possibility of processing in the preparation of user maps.Determining the land cover will be of great help to the area managers to make decisions. In this regard, the purpose of this researchis to compare the efficiency of parametric (least distant and box) and nonparametric (supporting vector machine) methods in land cover classification by using Landsat 8 satellite images in part of Dezful city. The nature of this research has been developmental-practical and its method has been descriptive-analytical. For this purpose, satellite data including Landsat 8 satellite images (13/8/2013) were prepared and analyzed using ENVI software. The efficiency of each classification method was investigated by calculating the two general accuracy and kappa coefficient. The results of the comparison of the methods used in the research showed that the SVM algorithm, especially the three linear, radial and polynomial kernels, had a better and more desirable accuracy than the parametric methods with 97.15%, 95.89% and 95.63% respectively. This study confirms the efficiency and more desirable capability of SVM algorithms in the classification of remote sensing images compared with parametric methods.
