Remote sensing
Data acquisition
Image processing
  Colour composites
  Geometric corrections
  Radiometric corrections
  Contrast enhancement
  Filtering
  Classification
  Visual interpretation
  Post-classification
  Indices
  Principal Component Analysis
  Combination of images
  Geospatial maps
  Combination of images and other data: DEM and DTM
Radar
GIS
 
Contrast enhancement
 

Improving the picture by turning up the contrast

Most digital image display systems allow one to break each ‘primitive’ colour into 256 degrees of intensity. An image that uses this entire intensity scale, that is, that contains values coded from 0 to 255, has excellent contrast, for the colour range will extend from black to white and include fully-saturated colours. In contrast, an image that uses a narrow range of numerical values will lack contrast (it will look ‘greyish’).

The top image is poorly contrasted, and the graphs show that the numerical values observed cover only a few dozen values in the red and green spectral bands and even fewer in the blue bland. These graphs also show that the blue and green values are higher overall than the red values. This explains the image’s blue-green cast. The picture at the bottom shows the result of applying a contrast spread function to the image. This involves changing the numerical values of the original image according to a linear function specific to each of the R, G and B components (V'R = kR * VR + cR) so as to make use of all of the possible values (0-255).
The contrast spread function is almost always applied before remote sensing images are analysed. This is because the Earth-observing satellites’ sensors are set to be able to record very different lighting conditions, ranging from deserts and ice floes (highly reflective areas) to Equatorial rainforests and oceans (very dark areas).