Comparison of Land Cover Types Classification Methods Using Tiangong-2 Multispectral Image

Shoreline Extraction from WorldView2 Satellite Data in the Presence of Foam Pixels Using Multispectral Classification Method

“…The poorest results by far were obtained with ML classification. This conclusion was also confirmed by Yu et al [26]. Our results show that the best methods to obtain the land /ocean maps, and then the shoreline extraction, are the SVM (19% and −2% on the erosion and 24% and 13% on the accretion) and the NN classification methods (45% and 43% on the erosion and −35% and 16% on the accretion).…”

“…Many classification methods have been developed for land classification. Yu et al [26] compared Euclidean Distance (ED), Maximum Likelihood (ML), Spectral Angle Mapper (SAM), and Support Vector Machine (SVM) classification methods to map land cover types using Tiangong-2 multispectral satellite data (CNSA (Chinese National Space Administration), [27]). For the classification of land cover types of the Qinghai Lake area (China), the overall classification accuracy of the SVM technique was found to be the highest, followed by the SAM, ED, and ML performance results.…”

The performance analyses of support vector machine classifiers for examination of the temporal change of land-use/cover in the Beyşehir Basin in Turkey (1984-2018)

“…In the classification process, low spatial resolutions, the used classification methods, and the assignment of a large number of classes result in pixel complexities and reducing the classification accuracy in large application areas (Ustuner et al, 2015). Among these classification methods, Maximum Likelihood Classification, Neural Network Analysis, Support Vector Machine (SVM) algorithms, and object-based classifications are the most well-known and most practiced supervised classifications in the literature (Otukei & Blaschke, 2010;Srivastava, Han, Rico-Ramirez, Bray, & Islam, 2012;Topaloglu, Sertel, & Musaoglu, 2016;Yu, Lan, Zeng, & Zou, 2019). In the study, in order to determine the kernel function and parameter set giving the highest classification accuracy, four different kernel functions (Radial, Linear, Polynomial, and Sigmoid) and different parameter sets (polynomial degree, error parameter, bias, and Gamma value) were experienced within the SVM method as different from each other, and seventy-two different models in total were applied using different combinations of parameters.…”