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Linear contrast enhancement,
also referred to as a contrast stretching, linearly expands the original
digital values of the remotely sensed data into a new distribution. By
expanding the original input values of the image, the total range of sensitivity
of the display device can be utilized. Linear contrast enhancement also
makes subtle variations within the data more obvious. These types of enhancements
are best applied to remotely sensed images with Gaussian or near-Gaussian
histograms, meaning, all the brightness values fall within a narrow range
of the histogram and only one mode is apparent. There are three methods
of linear contrast enhancement:
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Figure 6-3.1
Minimum-Maximum Linear Contrast Stretch |
An algorithim can be used that matches the old minimum value to the new minimum value, and the old maximum value to the new maximum value. All the old intermediate values are scaled proportionately between the new minimum and maximum values. Many digital image processing systems have built-in capabilities that automatically expand the minimum and maximum values to optimize the full range of available brightness values.
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Figure 6-3.2
Percentage Linear Contrast Stretch |
Figure 6-3.3 contains TM images of Charleston, SC and their associated histograms. The first image (a) displays the low-contrasting data in band 4 under normal conditions with no contrast stretch. The minimum brightness value is 12 and the maximum value 43. The histogram shows how the data is densely clustered between these values. In the second image (b), all values between 12 and 43 are linearly stretched using a minimum-maximum linear contrast stretch so that these values now lie within the range of 0 to 255. The minimum value 12 becomes 0 and the maximum value 43 stretches to 255. The histogram associated with this image demonstrates a wider distribution than the first histogram. This results in a pure pixel contrast that optimizes the capabilities of the display device.
The third image (c) continues to stretch the data by applying a one standard deviation percentage linear contrast stretch. The information content of the pixels that saturate at 0 and 255 is lost, yet a more detailed analysis of certain aspects of the image may be enhanced for better interpretation. The slope of a percentage linear contrast stretch is much greater than for a simple min-max stretch (refer to Figure 6-3.1). Sometimes the same percentage does not need to be applied to each tail of the distribution. The fourth image shows how an analyst would enhance an image if only interested in deliniating wetlands around Charleston Harbor. When the values between 13 and 27 are linearly stretched to 0 and 255, all values below 13 become 0 (black) and all values above 27 are set to 255 (white). This enhancement yields additional information on the smooth cordgrass at the expense at of the rest of the water and upland cover.
| Figure 6-3.3 - TM Band 4 of Charleston, SC | |||
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| (a) Raw Data | (b) Stretch 1 | (c) Stretch 2 | (d) Stretch 3 |
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It is not necessary that the same percentage be applied to each tail of the histogram distribution. For example, image analysts often need to increase the contrast of an image only at specific portions of the electromagnetic spectrum. An analyst wanting to extract detailed marine information in an image may only be interested in values between 0 and 12. When these values are stretched to 0 and 255, subtle ocean variations can be more easily detected (see Figure 6-3.4b). A percentage stretch of the same image between values of 25 and 45 yields detailed vegetation information (see Figure 6-3.4c). This may be useful in the delineation of healthy vegetation. If an analyst is interested in image enhancement for urban features, a percentage linear stretch between the values 40 and 70 in the red gun and 15 to 45 in the green and blue guns will increase the contrast of these features (see Figure 6-3.4d).
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| (a) Data without Enhancements
Bands 4,3, and 2 |
(b) Enhanced for Ocean Contrast
Red 0-10 Green 0-12 Blue 0-12 |
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| (b) Enhanced for Vegetation Contrast
Red 25-45 Green 30-35 Blue 25-30 |
(c) Enhanced for Urban Contrast
Red 40-70 Green 15-45 Blue 15-45 |
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Figure 6-3.5
Piecewise Linear Contrast Stretch |
Figure 6-3.6 displays histograms generated using Erdas Imagine 8.2 image processing software. A normal linear contrast stretch compared to a piecewise linear contrast stretch. The white histogram is the histogram of the raw data values before any enhancement is applied. The red, green, and blue plots are the histograms of the displayed image after the linear stretch is applied. In the normal linear contrast stretch example, the minimum and maximum values are stretched to the values of 0 and 255 at a constant level of intenstity (defined by the black line). In the piecewise linear contrast stretch, several breakpoints are defined that increase or decrease the contrast of the image for a given range of values. The higher the slope, the narrower the range of values being input from the x-axis. This results in a wider output spread for those same values and thus, increases the contrast for that range of values. A low sloping line results in a lower contrast for the same range of values. Notice the series of linear steps in each histogram that stretches intervals of data at different levels of intensities.
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| Normal vs. Piecewise Linear Contrast Stretch |
