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Principal component analysis (PCA) has been widely used in the reduction of the dimensionality of datasets, classification, feature extraction, etc. It has been combined with many other algorithms such as EM (expectation-maximization), ANN (artificial neural network), probabilistic models, statistical analysis, etc., and has its own developments, such as MPCA (moving PCA), MS-PCA (multi-scale PCA), etc. PCA and its derivatives have a wide range of applications, from face detection, to change analysis. Change detection with PCA shows, however, a major difficulty, that is, result interpretation. A new PCA method is developed, namely MB-PCA (multi-block PCA), in order to overcome this problem. Experimental results demonstrate the interest of the approach as a new way to use PCA.