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Use of Principal Component Analysis in Accuracy of Classification Maps (Case Study: North of Iran)

机译：主成分分析在分类图准确性中的使用（案例研究：伊朗北部）

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Objective: The main objective of this study was to investigate the role of principal component analysis to improve the accuracy of classification. Methodology: In the present study, PCA has been successfully applied in IRS images of north of Iran (Shafaroud) showing that the first principal components contain more variance of the information in the original four bands. Results: Classification was performed on two sets of data. In the first one, original bands of LISS III (b2-b3-b4-b5) and in the second classification, original bands of LISS III in combination with first component (pca1) was used. Classification was performed with five classes including sea, agriculture, settlements, broad leaf forest and needle leaf forest. In (b2-b3-b4-b5) and b2-b3-b4-b5 in composition with pca1, obtained overall accuracy and kappa coefficient were 99.27-98.94 and 99.37-99.09, respectively. Conclusion: The obtained results indicate that overall accuracy and kappa coefficient increases when pca 1 used along with main bands.
机译：目的：本研究的主要目的是研究主成分分析对提高分类准确性的作用。方法：在本研究中，PCA已成功应用于伊朗北部（Shafaroud）的IRS图像中，表明最初的主要成分在原始四个波段中包含更多的信息变异。结果：对两组数据进行了分类。在第一个中，使用LISS III的原始谱带（b2-b3-b4-b5），在第二个分类中，使用LISS III的原始谱带与第一成分（pca1）结合使用。分类分为五类，包括海洋，农业，居民区，阔叶林和针叶林。在具有pca1的成分（b2-b3-b4-b5）和b2-b3-b4-b5中，获得的总体准确度和kappa系数分别为99.27-98.94和99.37-99.09。结论：所得结果表明，将pca 1与主频带一起使用时，总体精度和kappa系数增加。

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