Some images have requires some radiometric processing in order to increase the contrast of the image and ensure that features in the image are distinguishable.
This image shows an example of an image before any corrections.
Notice the image above which has reflectance values to only 190, which is why the image appear to be in a medium grey colour since it is lacking in the higher reflectance values which is represented in white and lighter grey pixels (e.g. 191 – 255).
Contrast stretching is used to increase the contrast in an image and to ensure that all the colours of the palette, ranging from black to white, are being used.
e.g. Simple linear stretch – this is the most simple type of stretch where we use the minimum and maximum data values as the stretch endpoints. This is easily achieved by using autoscaling in IDRISI. Note that this method does not change the data values stored in the file.
e.g. linear stretches with saturation – This method is similar to the simple linear stretch, but it adds saturation to the image. It sets a new maximum and minimum display value that are within the original data value range.
Destripping is used when there are ‘strips’ of missing data or inaccurate data which are often due to sensor error. This method uses average values from adjacent rows or columns of pixel values to provide an output value for the missing or inaccurate data, in order to reduce the ‘noise’ from the sensor errors and provide information (i.e. reflectance value) to the missing strips of data.
