Principal Component Analysis (PCA) technique is useful in reducing dimensionality of a data set in order to obtain a simple dataset where characteristics of the original dataset that contributes most to its variance are retained. This method is to transform the original data set into a new dataset, which may better capture the essential information. Remote sensing images from orbiting satellites are gaining ground in recent years in inventory, mapping and monitoring of earth resources. These images are acquired in different wavelengths of the electromagnetic spectrum and therefore there exist correlation between the bands. The developed algorithm can not only reduce the dimensionality of remote sensing image but also extract helpful information for differentiating the target feature from other vegetation types more effectively. In this paper the usefulness and innovative of PCA in processing of multispectral remote sensing images have been tinted. It has been observed that PCA effectively summarize the dominant modes of spatial, spectral and temporal variation in data in terms of linear combinations of image frames. It provides maximum visual separability of image features thus improving the quality of ground truth collection and also turn to improving the image classification accuracy. Here, we propose a fast alternative to iterative PCA that makes it suitable for remote sensing applications while ensuring its theoretical convergence illustrated in the challenging problem of urban monitoring.