Principal Component Analysis of Mul ti-Temporal Image Pairs

The PCA is statistical technique that transforms a multivariate data set consisting of inter-correlated variables into a data set consisting of variables that are uncorrelated linear combination. In our project principal component analysis “PCA” was applied for two set of original bands in two dates (bands 1, 5, and 7 in 1988 and bands 1, 5, and 7 in 1990). In this method the PCA of six channel data sets consisting of multi-temporal LANDSAT TM image pairs often generates higher order principal components that are related to the changes in brightness. Although the image produced by the first component summarizes the information’s that are common to all channels, we can see that the first principal component is dominated by the contribution of the infrared band (band 7) in1988. Our result also, show that over 73.5% and 83.7% of the variability lies in the direction defined by the first and second principal component images respectively.