Principal component analysis

Principal Component Analysis (PCA) is a statistical technique widely used in remote sensing and digital image processing for data reduction and enhancement. It transforms a set of correlated spectral bands into a new set of uncorrelated variables called principal components. These components are ordered in such a way that the first _____ _______ ____ _____ ___ _______ _______ _______ ________.
___ _____ ____ ___ ____ _____ _____ _________ ___ ________.
_________ _______ ____ __________ _____.
____ _________ ___ _________ ______ ________.
______ ______ ____ ____ _____ __________ ____ ___ ____.
___ ______ ____ _______ ______ _____ _____ ________ _______ __________ ______ _____.
______ _________ _________ _____ ____ _________ ________ _____ ____.
__________ ________ ________ ___ ________.
__________ _____ ____ ______ _________ ____ ____ ____ ______ ______ ___ ______.
_________ ______ _____ ________ __________ _____ ______ _______ ____ ____.
___ __________ ______ _______ _______ _________ ___.
________ _________ ___ _____ ___ ____ _____ ______.
___ _______ _______ ________ ______ ___.
___ ___ _________ ______ ___ _______ ______ _______ ____.
______ __________ __________ ________ ____ ____ _____ _________ ____ ______ _____ _______.
_________ ______ ________ _______ _________ _________ ____.
_______ ___ ____ ____ ___ _____ ____ _____ ______ ___ _____ ______.
_______ ______ _______ _____ ____ _________.
_________ ________ ______ ______ _______ ________.
________ _____ _________ _____ ______.
_________ ____ ________ __________ ______ ________ ___ _____ ______ ________ __________.
________ ________ _______ _______ ______ ___ _________ _______.
_________ _______ ______ _________ _______ _________ ______.
____ _________ __________ _________ __________ ______ ___ ______.
___ __________ ____ ______ ___.
Get Full Answer on WhatsApp