University of Delaware
Department of Geography
GEOG474
Introduction to Environmental Remote Sensing
Image Enhancement
Contrast Enhancement
- Process that makes the image features stand out more
clearly by making optimum use of the colors available on the display or
output device.
- Involves changing the range of values in an image in
order to increase the contrast.
Contrast - range of brightness value
Linear contrast enhancement
- best applied to remotely sensed images with Gaussian or
near-gaussian histograms
- 3 methods
- minimum-maximum linear contrast stretch
- percentage linear contrast stretch
- piecewise linear contrast stretch
Minimum-maximum linear contrast stretch
- - original minimum and maximum vlaues of data are assigned to a newly
specified set of values that utilize the full range of available brightness
values
Percentage linear contrast stretch
- - uses a specified minimum and maximum vlaues that lie in a certain
percentage of pixesl from the mean of the histogram
- - a standard deviation from the mean if often used to push the tails
of histogram beyond the original minimum and maximum values
Piecewise linear contrast stretch
- - use when distribution of a histogram in an image is bi or trimodal
- - involves the identification of a number of linear enhancement steps
that expands the brightness ranges in the modes of the histogram
- - a series of small min-max stretches
Nonlinear digital techniques
Histogram Equalization - all pixel values of the image
are redistributed so there are approximately an equal number of pixels
to each of the user-specified output gray-scale classes (e.g., 256).
- - contrast is increased at the most populated range of
brightness values of the historgram (or "peaks")
- - it automatically reduces the contrast in very light
or dark parts of the image associated with the tails of a normally distributed
histogram
- - disadvantage, each value in the input image can have
several values in the output image, so that objects in original scene lose
their correct relative brightness values.
Spatial Filtering for Enhancement
Low and High Frequency Detail and Edges
- Spectral enhancement relies on changing the gray scale
representation of pixels to give an image more contrast for interpretation,
it applies the smae spectral transformation to all pixels with a given
gray scale in an image. Although this allows better interpretation of an
image by a user, it does not take full advantage of human recognition capabilities.
- When interpreting an image, we not only use brightness
information in the image, but also spatial relationships within the image
to make decisions as to the identification of features in the image. Exercise
5 will demonstrate the value of spatial characteristics in image interpretation.
- There are 3 main purposes that underlie spatial enhancement
techniques:
- to improve interpretability of image data
- to aid in automated feature extraction
- to remove and/or reduced sensor degradation
What are the major clues that allow us to recognize and
identify regions within an image?
Spatial enhancement techniques involve:
- - search of an image for linear edges
- - search for homogeneous regions, and
- - linking of the linear edges so that they enclose the
homogeneous regions
- Definition: Spatial enhancement is the mathematical
processing of image pixel data to emphasize spatial relationships.
- Use the concept of spatial frequency within an
image
- - manner in which gray-scale values change relative to
their neighbors within an image
- low spatial frequency - there is a slowly varying
change in gray scale in the image from one side of the image to the other
- high spatial frequency - the pixels values vary
radically for adjacent pixels in an image
Many natural and manmade featurese in images have high spatial
frequency:
- Geologic faults
- Edges of lakes
- Roads
- Airports
Spatial enhancement involves the enhancement of both low
and high frequency information within an image.
- Algorithms that enhance low frequency image information
emply a "blurring" filter that emphasizes low frequency parts
of an image while de-emphasizing the high frequency components.
- The enhancements of high frequency information within
an image is often called edge enhancement.
Spatial Convolution Filtering
Convolution involves the passing of a moving window over
an image and creating a new image where each pixel in the new image is
a function of the original pixel values within the moving window and the
coefficients of the moving window as specified by the user. The window,
a convolution operator, may be considered as a matrix (or mask) of coefficients
that are to be multiplied by image pixel values to derive a new pixel value
for a resultant enhanced image. This matrix may be of any size in pixels
and does not necessarily have to be square.
Examples
