The multispectral image data is usually strongly correlated from one band to the other. The level of a given picture element on one band can to some extent be predicted from the level of that same pixel in another band.
Principal component analysis is a pre-processing transformation that creates new images from the uncorrelated values of different images. This is accomplished by a linear transformation of variables that corresponds to a rotation and translation of the original coordinate system.
Principal component analysis operates on all bands together. Thus, it alleviates the difficulty of selecting appropriate bands associated with the band ratioing operation. Principal components describe the data more efficiently than the original band reflectance values. The first principal component accounts for a maximum portion of the variance in the data set, often as high as 98%. Subsequent principal components account for successively smaller portions of the remaining variance.
Principal component transformations are used for spectral pattern recognition as well as image enhancement. When used before pattern recognition, the least important principal components are dropped altogether. This permits us to omit the insignificant portion of our data set and thus avoids the additional computer time. The transformation functions are determined during the training stage. Principal component images may be analysed as separate black and white images, or any three component images may be colour coded to form a colour composite. Principal component enhancement techniques are particularly appropriate in areas where little a priori information concerning the region is available.
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