Assessing Image Classification Accuracy with Principal Component Analysis Algorithm Case Study: Odeda LGA of Ogun State, Southwest Nigeria

The aim of this study is to assess image classification accuracy using the instrumentality of Principal Component Analysis (PCA). It is focused on evaluating the accruable benefits of Principal Component Analysis as part of an image preprocessing procedure for image classification. Land use land cover (LULC) and accuracy assessment datasets were obtained with remote sensing and geographic information system’s software. The principal component analysis was statistically used to assess the level of correlation amongst bands in Landsat 8. The image classification was premised on the Maximum Likelihood classifier for land use land cover analysis. To ascertain the accuracy of the classified images, the Producer’s accuracy, User’s accuracy and Kappa coefficient derivatives of accuracy assessment was calculated. The results revealed that the first three PCs of the raw Landsat data accounted for 99.37 % variance of the original Landsat data, while the last three PCs represented only 0.63% of the original data. The results of land use land cover based on raw bands composite were Forest (41%), Shrubs (33%) and Built-up (26%) respectively. On the other hand, land use land cover based on Principal Component Analysis showed Forest (39%), Shrubs (39%) and Built-up (22%) respectively. Comparing the results of Kappa coefficients of both LULC of raw bands’ composite was 0.88 while that of PCA was 0.91. Conclusively, there is a significant level of difference in the classification outputs of PCA derived classification and that of raw Landsat bands’ composite.