Decorrelation Stretch, Canonical Components & Hue, Saturation and Intensity (HIS) Transform (Especially for GATE-Geospatial 2022)

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Principal Components can be stretched and transformed back into Rai colours — a process known as decorrelation stretching.

Decorrelation Stretch

If the data are transformed into principal components space and are stretched within this space, then the three bands making up the RGB colour composite images are subjected to stretch will be at the right angles to each other. In RGB space the three-colour components are likely to be correlated, so the effects of stretching are not independent for each colour. The result of decorrelation stretch is generally an improvement in the range of intensities and saturation for each colour with the hue remaining unaltered. Decorrelation Stretch, like principal component analysis, can be based on the covariance matrix or the correlation matrix, the resultant value of the decorrelation stretch is also a function of the nature of the image to which it is applied. The method seems to work best on images of semi-arid areas and it seems to work least well where the area is covered by the image includes both land and sea.

Principle Component Stretch

Canonical Components

PCA is appropriate when little prior information about the scene is available. Canonical component analysis also referred to as multiple discriminant analysis, may be appropriate when information about particular features of interest is available. Canonical component axes are located to maximize the separability of different user-defined feature types.

Hue, Saturation and Intensity (HIS) Transform

Hues is generated by mixing red, green and blue light are characterized by coordinates on the red, green and blue axes of the colour cube. The hue-saturation-intensity hexagon model, where hue is the dominant wavelength of the perceived colour represented by angular position around the top of a hexagon, saturation or purity is given by distance from the central, vertical axis of the hexagon and intensity or value is represented by distance above the apex of the hexagon. Hue is what we perceive as colour. Saturation is the degree of purity of the colour and may be considered to be the amount of white mixed in with the colour. It is sometimes useful to convert from RGB colour cube coordinates to HIS hexagon coordinates and vice-versa.

Advantages of Hue, Saturation and Intensity (HIS) Transform

The “hue, saturation and intensity transform” is useful in two ways:

  • First as a method of image enhancement
  • Secondly as a means of combining co-registered images from different sources

The advantage of the HIS system is that it is a more precise representation of human colour vision than the RGB system. This transformation has been quite useful for geological applications.

The Hue-Saturation-Intensity Hexagon Model

Illustration 2 for The_huesaturationintensity_hexagon_model
Illustration 3 for The_huesaturationintensity_hexagon_model

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