ge 113 remote sensing topic 7. image enhancement · lecture notes in ge 113: remote sensing topic...

Topic 7. Image Enhancement

Division of Geodetic Engineering College of Engineering and Information Technology Caraga State University

GE 113 – REMOTE SENSING

Lecturer: Engr. Jojene R. Santillan [email protected]

Lecture Notes in GE 113: Remote Sensing TOPIC 7. IMAGE ENHANCEMENT

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Outline

• Part 1. Image Enhancement Concepts

• Part 2. Contrast Manipulation Techniques


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Expected Outcomes

• The students would be able to:

– Learn the concepts behind image enhancement

– Identify the various computer-assisted procedures of image enhancement

– Learn how to conduct the computer-assisted procedures through laboratory exercises

4 Lecture Notes in GE 113: Remote Sensing TOPIC 7. IMAGE ENHANCEMENT

PART 1. IMAGE ENHANCEMENT CONCEPTS


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Image Enhancement

• The goal is to improve the visual interpretability of an image by increasing the apparent distinction between the features in the scene.

• Why do we need a computer to do the enhancement? – Our eyes are poor at discriminating the slight

radiometric or spectral differences that may characterize such features

– With computers, these slight differences can be visually amplified to make them readily observable by our eyes.


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Types of Image Enhancement Operations

• Point Operations

– Brightness values of each pixel in an image data are modified independently

• Local Operations • Brightness values of each pixel in an image data are modified

based on neighboring brightness values

Note: Either form of enhancement can be performed on single-band images or on the individual components of multi-image composites.


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When are image enhancement techniques applied?

• Normally applied to image data after the appropriate image rectification and restoration procedures have been performed.

• Noise removal very important to conduct

prior to image enhancement

– Image enhancement techniques may enhance “noise” if they are not removed

the interpreter will end up analyzing

enhanced noise!


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Categories of Image Enhancement Techniques

• Contrast Manipulation Techniques – Discussed in detail in Part 2

• Spatial Feature Manipulation Techniques – Used to emphasize or deemphasize image data of various spatial

frequencies • Spatial frequency refers to the roughness of the tonal variations occurring in

an image

– These are “local” operations pixel values in an original image are modified on the basis of the gray scale/brightness/DN values of neighboring pixels

– Examples: Spatial filters

• Multi-image Manipulation Techniques – Enhancements involving multiple spectral bands of imagery – Examples:

• Spectral ratioing • Principal and canonical components transformation • Vegetation components transformation • Intensity-hue-saturation color space transformation

14 Lecture Notes in GE 113: Remote Sensing TOPIC 7. IMAGE ENHANCEMENT

PART 2. CONTRAST MANIPULATION TECHNIQUES


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Contrast Manipulation

• Focused on manipulating the brightness values/DNs of an image data to reveal specific or new information or to enhance existing image information

• Commonly used contrast manipulation procedures: – Gray-level thresholding – Level slicing – Contrast stretching

• These are all “point” operations


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Gray-level Thresholding

• A segmentation procedure

• An input image band is segmented into two classes:

– One class for those pixels having values below a defined gray level (DN)

– One class for those pixels above this value

• The result is a binary classification

• This binary classification can then be applied to a particular image band data to enable display of brightness variations in only a particular class


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Example: NIR Band of Landsat 7 ETM+

Histogram of DN values of NIR Band

DN Range: 0 – 30 water bodies

Gray-scale Thresholded Image: Class 1: 0 -30 (Water) Class 2: 31 – 255 (Others)

NIR Band of Landsat 7 ETM+ Showing only Class 1 (Water)

True Color Image Showing only Class 1 (Water)


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Level Slicing

• An enhancement technique whereby the DNs distributed along the x axis of an image histogram are divided into a series of intervals or “slices”.

• All of the DNs falling within a ‘slice’ are then displayed at a single DN in the output image


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Example: NIR Band of Landsat 7 ETM+

Histogram of DN values of NIR Band

“Sliced” NIR Band of Landsat 7 ETM+ (6 classes)


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Example: Sliced NIR Band (Water Portion only)


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Example: Level slicing the TIR Band of Landsat 7 to show land surface temperature (LST)

Image © http://www.mdpi.com/2072-4292/7/4/4268/htm


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Contrast Stretching (1)

• Recall: – An image can have DN values ranging from 0 to a

maximum value depending on its radiometric resolution: • E.g., an 8-bit image can have DNs ranging from 0 – 255 • A 12-bit image can have DNs ranging from 0 – 4095 • Etc.

– When the image data are visualized on a screen of a computer, they are displayed as brightness values for each screen pixel • A data pixel with a larger value is brighter than one with a

smaller value • However, unlike the image data, screen pixels can only

have 256 unique brightness values (i.e., 0 to 255). • This limitation prevents the data from being displayed with

brightness exactly equal to their real (DN) value


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• Stretching the image data refers to a method by which the data pixels are rescaled from their original values into a range that the monitor can display - namely, into integer values between 0 and 255.

• But what about contrast stretching?


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• The parameters of the stretch can be adjusted to maximize the information content of the display for the features of interest this process is referred to as contrast stretching.

• Contrast stretching changes contrast in the image

• Contrast = the relative differences in the brightness of the data values: – increasing an image's contrast means the dark pixels

will become darker, and the bright pixels will become brighter

– brightness difference between the two increases


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Contrast Stretching as an Image Enhancement Procedure

• Used to expand the narrow range of brightness values typically present in an input image over a wide range of values

• Contrast stretching results to an output image or image display that is designed to emphasize the contrast between features of interest.


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Types of Contrast Stretching (as implemented in various image processing software, e.g., Envi)

• Linear

• Linear 0-255

• Linear 2%

• Gaussian

• Equalization

• Square root

ALL OF THESE OPERATIONS RELY ON THE MANIPULATION OF THE IMAGE HISTOGRAMS


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What is a Histogram?

• a graphical representation of the distribution of numerical data.

• To construct a histogram, the first step is to "bin" the range of values—that is, divide the entire range of values into a series of intervals—and then count how many values fall into each interval.

• The bins are usually specified as consecutive, non-overlapping intervals of a variable.

• The bins (intervals) must be adjacent, and are usually equal size

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What is a Image Histogram?

• A type of histogram that acts as a graphical representation of the tonal (“DN”) distribution in a digital image.

• It plots the number of pixels for each tonal/DN value.

• By looking at the histogram for a specific image a viewer will be able to judge the entire tonal distribution at a glance.


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Linear Contrast Stretching • Sets the image minimum and maximum DN values to values of 0 and 255, and

stretches all other data values linearly between 0 to 255.

• Example: – If a band of an image has DN values ranging from 30 to 200, linear contrast stretching will

expand the range such that when displayed/outputted to an image file, the new DN values will range from 0 to 255:

• Screen value of 0 will be assigned to 30 • Screen value of 200 will be assigned to 255 • All other values will be linearly stretched

• Algorithm:

New DN = DN’ = [(DN – MIN) / (MAX – MIN) ] * 255

Where: DN = original DN of a pixel MIN = the image’s minimum DN value that will be assigned a new value of 0 MAX = the image’s maximum DN value that will be assigned a new value of 255


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Example: Linear Contrast Stretching

Original Band 1 Stretched


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Linear 0-255

• Sets the image’s DN value of 0 to a new value of 0, and the image’s DN value of 255 to a new value of 255

• “No stretching”


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Example: Linear 0-255



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Linear 2%

• Sets the highest and lowest 2% of the original image DN values to new values of 0 and 255, and it stretches all other data values linearly


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Example: Linear 2%



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Gaussian

• Sets: – the original image’s mean

DN value to a new value of 127,

– the DN value 3 standard deviations below the mean value to a new value of 0, and

– the DN value 3 standard deviations above the mean value to a new value of 255.

• Intermediate values are

assigned new value using a Gaussian curve


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Example: Gaussian



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Histogram Equalization

• Scales the original image DN values to equalize the number of DNs in each display histogram bin

• In this approach, image DN values are assigned to the display levels on the basis of their frequency of occurrence


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Example: Histogram Equalization



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Square root • takes the square of the input histogram and

applies a linear stretch



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• Questions or clarifications?


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References/Further Reading

• Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2008). Remote Sensing and Image Interpretation 6th Edition. United States of America: John Wiley & Sons, Inc.

• Online Tutorial: Fundamentals of Remote Sensing – “Image Enhancement”. Available at http://www.nrcan.gc.ca/earth-sciences/geomatics/satellite-imagery-air-photos/satellite-imagery-products/educational-resources/9389

http://www.nrcan.gc.ca/earth-sciences/geomatics/satellite-imagery-air-photos/satellite-imagery-products/educational-resources/9389
















ge 113 remote sensing topic 7. image enhancement · lecture notes in ge 113: remote sensing topic...

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