TCA And PCA

Principal Components and Tasselled Cap Transformations

Project Summary: 

Image classification is a useful technique for obtaining information by categorizing all pixels in a digital raster image into several “themes”. Among different classification techniques, Principal Components Analysis (PCA) and Tasseled Cap Transformation (TCA) are used to compress image information for image analysis purpose. In this assignment Landsat 7 Image has been used for PCA and TCA analysis. In this assignment, PCA has been applied using principal Component Analysis tool and TCA has been applied using the Raster calculator tool of PCI Geomatica. The results show that the RGB combination image of PCA and TCA is almost same. The PC1 and PC2 is similar to TC brightness and TC greenness. But there is a difference between PC3 and TC wetness.

Results: 

 

Loadings PC1 PC2 PC3 PC4 PC5 PC6
Band 1 0.599727 -0.597500 0.514843 0.046685 0.110932 -0.059132
Band 2 0.745017 -0.467524 0.465573 0.009689 0.026291 0.093750
Band 3 0.728307 -0.569635 0.362937 -0.077148 -0.081119 -0.023480
Band 4 0.814056 0.568476 0.115292 0.016122 -0.004708 -0.003039
Band 5 0.927949 -0.160576 -0.328297 -0.067790 0.023239 0.001729
Band 7 0.811837 -0.488516 -0.280233 0.150037 -0.030689 -0.002198
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