{"title":"支持向量机分类器在土耳其bey<e:1> ehir流域土地利用/覆被时空变化研究中的性能分析","authors":"Munevver Gizem Gumus, S. Durduran","doi":"10.9733/jgg.2021r0005.e","DOIUrl":null,"url":null,"abstract":": This study aimed to investigate the temporal change in land-use/cover in the Beyşehir-Kaşaklı Subbasin, which is one of the nine subbasins of the Konya Closed Basin and known as the largest closed basin in Turkey, using Remote Sensing and Geographic Information Systems techniques. For this purpose, in the study, Landsat Thematic Mapper, Enhanced Thematic Mapper, and Operational Land Imager digital satellite images obtained in the years 1984, 1990, 1996, 2000, 2006, 2012, and 2018 were used. The Support Vector Machines (SVM) method was applied as the classification method. In order to apply the SVM method, firstly, the kernel function and parameter set, giving the highest accuracy in the classification, were selected. In the study, four different kernel functions and different parameter sets were experienced as different from each other. Seventy-two different models in total were applied using different combinations of parameters. As a result of the trials of seventy-two different parameters, it was concluded that the method and algorithm giving the most accurate result with 83.81% classification accuracy and 0.7949 Kappa statistics were the polynomial function of SVMs. As a result of the classification process examined the period between 1984 and 2018 using the determined algorithm and parameters, it was detected that artificial surfaces increased by 418%, arable agricultural lands and pastures decreased by 14%, forests and semi-natural areas increased by 4%, and coastal wetlands on the coasts increased by 6%. On the other hand, the surface area of the water bodies in the region, which demonstrated a decreasing trend until the year 2003, was determined to increase by 3% with the establishment of Suğla Storage in 2003.","PeriodicalId":33920,"journal":{"name":"Journal of Geodesy and Geoinformation Science","volume":"70 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"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)\",\"authors\":\"Munevver Gizem Gumus, S. 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引用次数: 1
摘要
利用遥感和地理信息系统技术,对土耳其最大的封闭盆地——科尼亚封闭盆地9个子盆地之一的bey eir - ka aklyi子盆地土地利用/覆被的时空变化进行了研究。为此,本研究使用了1984年、1990年、1996年、2000年、2006年、2012年和2018年获得的Landsat Thematic Mapper、Enhanced Thematic Mapper和Operational Land Imager数字卫星图像。采用支持向量机(SVM)方法进行分类。为了应用支持向量机方法,首先选取分类精度最高的核函数和参数集;在研究中,经历了四种不同的核函数和不同的参数集,它们彼此不同。使用不同的参数组合,总共应用了72种不同的模型。通过72个不同参数的试验,得出分类准确率为83.81%,Kappa统计量为0.7949的最准确的方法和算法为支持向量机的多项式函数。通过使用确定的算法和参数对1984年至2018年期间进行分类,发现人工地表增加了418%,可耕地和牧场减少了14%,森林和半自然区域增加了4%,沿海湿地增加了6%。另一方面,该地区水体表面积在2003年之前呈下降趋势,2003年随着Suğla水库的建立,确定增加3%。
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)
: This study aimed to investigate the temporal change in land-use/cover in the Beyşehir-Kaşaklı Subbasin, which is one of the nine subbasins of the Konya Closed Basin and known as the largest closed basin in Turkey, using Remote Sensing and Geographic Information Systems techniques. For this purpose, in the study, Landsat Thematic Mapper, Enhanced Thematic Mapper, and Operational Land Imager digital satellite images obtained in the years 1984, 1990, 1996, 2000, 2006, 2012, and 2018 were used. The Support Vector Machines (SVM) method was applied as the classification method. In order to apply the SVM method, firstly, the kernel function and parameter set, giving the highest accuracy in the classification, were selected. In the study, four different kernel functions and different parameter sets were experienced as different from each other. Seventy-two different models in total were applied using different combinations of parameters. As a result of the trials of seventy-two different parameters, it was concluded that the method and algorithm giving the most accurate result with 83.81% classification accuracy and 0.7949 Kappa statistics were the polynomial function of SVMs. As a result of the classification process examined the period between 1984 and 2018 using the determined algorithm and parameters, it was detected that artificial surfaces increased by 418%, arable agricultural lands and pastures decreased by 14%, forests and semi-natural areas increased by 4%, and coastal wetlands on the coasts increased by 6%. On the other hand, the surface area of the water bodies in the region, which demonstrated a decreasing trend until the year 2003, was determined to increase by 3% with the establishment of Suğla Storage in 2003.