Image enhancement is used to enable a greater level of image interpretation and understanding (National Resources Canada, 2016). Enhancement is improved by using a greater brightness range, thereby producing an image of high contrast. There are a few ways in which this contrast stretching can be achieved, all be re-scaling the original brightness values to the screen brightness values of a minimum of 0 and maximum of 255 (Natural Resource Canada, 2016).
Linear stretch:
One way in which the contrast of an image can be enhanced is by a linear stretch. This is the simplest method (Natural Resource Canada, 2016), and works by applying the lowest brightness value of the image to the lowest display value of 0, and repeating this for the highest to 255 (Natural Resource Canada, 2016). This thereby allows the whole of the brightness range of the screen to be accessed. It will result in making the dark areas of the image darker, and the light areas lighter, which will make visual interpretation easier (Natural Resource Canada, 2016). However, this method may not always be appropriate, for example if the data is not uniformly distributed, the contrast stretch will not be as effective. Below is an example of a linear stretch to enhance the contrast of the wetland and estuary areas of the image. The original image displayed as a false colour composite is on the right, whilst the linear stretched image is on the left. The wetland can be seen in much greater detail, revealing some small tributary-like features that are difficult to interpret on the original image.
Hong Kong Harbour (right); Linear stretch of the right image (left)
Histogram equalisation:
This method uses a greater representation across the image histogram, rather than just the maximum and minimum values on the linear stretch. Through this method, areas with more values that frequently occur on the original histogram will be of better detail than areas with low frequency values (Natural Resource Canada, 2016). Below is an example of the same false colour composite image (on the right) and this image after a histogram equalisation for image enhancement (on the left). This contrast stretch also seems to provide more detail on the wetland and estuary areas of the image, however, the surrounding area on the image is not compromised and is also presented in greater detail.
Hong Kong harbour (right); Histogram equalisation of right image (left)
Guassian Stretch:
This method is used when the light and dark areas of an image need to be enhanced. The mean data value will be set to 127, whilst the values three standard deviations below and above the mean will be assigned to display values 0 and 255 respectively (ENVI, 2016). Below, the same image indicated in the previous two examples (shown on the right) is presented after a guassian stretch (on the left). Although this does improve the detail from the original image, it does not appear to be quite as effective as the linear stretch or histogram equalisation for this example.
Hong Kong harbour (right); Guassian stretch of the right image (left)
References:
ENVI (2016) Laboratory Exercises in Image Processing: Contrast Stretching / Harrisgeospatial. Available at: http://www.harrisgeospatial.com/Portals/0/EasyDNNNewsDocuments/Repository/ContrastStretching.pdf
Natural Resources Canada (2016) Image Enhancement. Available at: http://www.nrcan.gc.ca/earth-sciences/geomatics/satellite-imagery-air-photos/satellite-imagery-products/educational-resources/9389
Richards, J.A. (2013) Remote Sensing Digital Image Analysis: An Introduction. (5th Edition). Australia: Springer.
9321836 