Decorrelation stretching. Remote Sensing

Decorrelation stretching, also known as principal component transformation or principal component analysis (PCA), is a method of image enhancement in remote sensing that is used to highlight subtle variations in an image that may not be easily visible using traditional techniques such as linear or piecewise linear stretching.

The basic idea behind decorrelation stretching is to transform the image data into a new set of variables, known as principal components, that are decorrelated, or not correlated, with one another. These principal components are derived from the original image data by finding the directions in which the data varies the most, and they are ranked in order of importance, with the most important components being ranked first.

By displaying the image in terms of these principal components, it is possible to highlight features in the image that may not be easily visible using traditional methods of image enhancement. This can be particularly useful for identifying subtle changes in land cover or for identifying features that are poorly represented in a single spectral band of the image.

Decorrelation stretching is a powerful tool for image enhancement, but it is important to use caution when applying it, as it can introduce visual artifacts or distort the appearance of the image if not used properly. It is also important to keep in mind that the principal components derived from the image data may not always be the most interpretable representation of the data, and it may be necessary to transform the image back into the original coordinate system in order to properly interpret the results.

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