Principal Component Analysis

Published: 2021-06-29 06:44:39
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Principal Component Analysis and KrigingIntroductionRemote sensing data can be used to assist in a multitude of spatial queries. They provide both visual and statistical data that can be used to describe the area that has been remotely sensed. Landsat 8 is a satellite which carries two sensors, namely the operational land imager sensor and thermal infrared sensor. Principal Component AnalysisUsing SAGA GIS, the principal component analysis can be applied to the 7 Landsat bands. The analysis creates 7 components and shows it variance and corresponding Eigen vectors and Eigen values. [pic 1]Figure 1: Prinicipal Component Analysis ResultsFigure 1 shows the results after performing the principal component analysis. It calculated the expected variance, explained cumulative variance, Eigen value and Eigen vectors for the seven components[pic 2]Figure 2.2: Component 2Figure 2.3: Component 3Figure 2.4: Component 4Figure 2.5: Component 5Figure 2.6: Component 6Figure 2.7: Component 7Figure 2.1 to Figure 2.7 shows the visual representation of each component. This is created during the principal component analysis process in SAGA GIS. Results of Principal Component AnalysisIt can be seen that the components which represent the majority of the variance in the data is component one, which represents 66.62%, component two represents 30.42%  and component three represents 1.99% of the explained variance. The first three components cumulatively represent 99.03% of the explained variance. This is also reiterated in the visual representation of the components. It can be seen that component one and component two have the most defined images whereas the images for components four to seven become progressively less defined. This shows that components one and two represent the most variance because their images will provide the most information content compared to the rest of the bands.From the values of the Eigen vector matrix, the bands which are predominantly represented in each of the components can be determined:Component 1: Band 6 (0.6921) and Band 5 (0.557) Component 2: Band 5(0.9401)Component 3: Band 4 (0.6181) and Band 6 (0.5281)Component 4: Band 7 (0.7301)Component 5:  Band 4 (0.6556)Component 6: Band 3 (0.7953)Component 7: Band 1 (0.7230) and Band 2 (0.6863)[pic 3]Figure 3: Natural Colour Composite ImageFigure 3 shows a colour composite image created using band 2, band 3 and band 4. Band 2 represents blue; band 3 represents green and band 4 represented. This was created using the RGB composite tools in SAGA GIS.[pic 4]Figure 4: False Colour CompositeFigure 4 shows a false colour composite created using the first three components. Component one represents red, component two represent green and component three represents blue.

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