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Figure 2.4 (a) Principal component images derived from the SPOT image shown in Figure 2.3. (b) Histograms of corresponding principal component images. The histograms show that the variance of the first principal component exceeds that of the second, which in turn is greater than the variance of the third component.

As noted earlier, PCA can be based either on the matrix of variances and covariances or the matrix of correlations between the spectral bands of an image set, which can be derived either from sample data or from all the pixels of the image set. PCA based on the covariance matrix is also sometimes called unstandardised PCA, while standardised PCA uses the

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