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Contrast Manipulation



1. Image Enhancement and Contrast Manipulation

Image enhancement in remote sensing is about improving the visual appearance and interpretability of satellite or aerial images. Contrast manipulation is a major part of image enhancement, involving changes to pixel brightness and contrast to highlight features of interest.

  • Brightness Values: Every pixel in a digital image has a brightness (or grayscale) value, representing the intensity level. These values generally range from 0 (black) to 255 (white) in an 8-bit image. Altering brightness changes the overall lightness or darkness of the image, which can help reveal hidden details.

  • Image Lookup Table (LUT): A LUT is a table mapping input pixel values to desired output values. By applying LUTs, you can quickly adjust contrast and brightness in a controlled manner. LUTs can also apply color transformations, convert grayscale images to color, or perform other types of image corrections.

  • Image Histogram: This is a graph showing the frequency of brightness values in an image. The x-axis represents brightness levels, while the y-axis represents the frequency of each level. The histogram is useful for identifying whether an image is underexposed (too dark) or overexposed (too bright) and for selecting contrast adjustment techniques accordingly.

  • Histogram Stretch: This is the most common form of contrast enhancement. Histogram stretching redistributes pixel values across the brightness range to increase contrast. For example, if pixel values range from 50 to 200, stretching can rescale these values to span from 0 to 255, giving more visual detail.

2. Linear Contrast Manipulation Techniques

Linear methods adjust pixel brightness values by adding or scaling them linearly. The adjustments are applied consistently across the entire image.

a. Minimum and Maximum Stretch

This technique takes the minimum and maximum pixel values in an image and scales them to the lowest and highest possible brightness values (e.g., 0 and 255). This enhances the contrast by using the full brightness range.

Example:

  • If the original pixel values range from 50 to 200, a minimum-maximum stretch would rescale 50 to 0 and 200 to 255, with intermediate values adjusted proportionally.

b. Average and Standard Deviation Stretch

This method uses the average and standard deviation of pixel values to set new brightness limits, often focusing on the central range of brightness values around the average.

Example:

  • For an image with an average pixel value of 128 and a standard deviation of 30, stretching might adjust brightness to cover a range from 98 to 158. This technique is useful when the image has a narrow brightness range.

c. Saturation Stretch or Percentage Stretch

In this method, a certain percentage of the darkest and brightest pixel values are "saturated" (clipped) at the high and low ends. For example, if a 2% saturation stretch is applied, the darkest 1% and brightest 1% of pixel values are clipped to 0 and 255, respectively. This increases contrast without distorting the image with extreme values.

d. Piecewise Stretch (Tail and Trim Stretch)

Piecewise stretching divides the histogram into multiple segments and stretches each segment separately. This approach allows different contrast levels for different parts of the image, making it useful when specific brightness ranges need separate enhancement.

Example:

  • For a histogram with three peaks (tri-modal), a piecewise stretch might apply different enhancements to each peak, so each feature (e.g., vegetation, water, urban) stands out better.

e. Bi-Modal or Tri-Modal Stretch

This stretch is applied to histograms with multiple peaks (bi-modal for two, tri-modal for three). Each peak corresponds to different features or land covers, like water and land. Separate contrast enhancements are applied to each peak to highlight these features.

3. Nonlinear Contrast Manipulation Techniques

Nonlinear methods are more complex and can adapt based on the image's histogram or specific features, enhancing contrast more selectively.

a. Histogram Equalization or Normalization

Histogram equalization redistributes pixel values across the full brightness range, aiming for a uniform histogram. This technique enhances the image's local contrast, especially in low-contrast areas, by "spreading" frequently occurring brightness values.

Example:

  • In a foggy landscape image, histogram equalization can increase contrast in areas with subtle brightness variations, making details clearer.

b. Reference Stretch

Reference stretch matches the histogram of the target image to that of a reference image. This technique is often used when analyzing multiple images or time-series data to ensure consistent contrast and brightness levels.

Example:

  • If analyzing vegetation growth over time, reference stretching can make sure each image in a series has similar contrast, so changes in vegetation are more visible.

c. Density Slicing

Density slicing assigns different colors or grayscale values to specific brightness ranges, effectively segmenting the image. This approach is particularly helpful for distinguishing land covers or features.

Example:

  • In satellite images, density slicing can assign green to vegetation, blue to water, and brown to built-up areas by applying specific brightness thresholds for each class.

d. Thresholding

Thresholding is a binary segmentation technique. Pixels with values above a certain threshold are assigned one brightness value (e.g., white), and those below are assigned another (e.g., black). It simplifies the image, isolating specific features like water bodies or built-up areas.

Example:

  • Setting a threshold to detect water bodies in satellite imagery: pixels with low reflectance (indicating water) are set to white, and non-water areas are set to black.

Summary Table for Contrast Manipulation Techniques

TechniqueDescriptionUse Case/Example
Minimum & Maximum StretchExpands contrast by stretching pixel values to full range (0-255).Enhances overall visibility in low-contrast images.
Average & Standard Deviation StretchAdjusts around average intensity, focusing on central brightness range.Useful for images with a narrow brightness range.
Saturation/Percentage StretchClips extreme values, limiting contrast to a percentage range.Avoids distortion by discarding extreme outliers.
Piecewise/Tail and Trim StretchDivides histogram into segments, enhancing each separately.Suitable for images with multiple important brightness ranges.
Bi-Modal/Tri-Modal StretchAdjusts contrast separately for each histogram peak, enhancing specific features.Useful for images with distinct land covers like water and land.
Histogram EqualizationRedistributes values for uniform contrast, improving visibility in low-contrast images.Increases visibility in hazy or foggy images.
Reference StretchMatches contrast to a reference image, ensuring consistency across images.Used for time-series analysis or comparing multiple images.
Density SlicingAssigns colors to specific brightness ranges, aiding in feature classification.Helps classify land cover types (e.g., vegetation, water).
ThresholdingBinarizes image based on a brightness threshold, isolating specific features.Detects water bodies, urban areas, etc.





Fyugp note,
PG and Research Department of Geography Government College Chittur 

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