Preparing Remote Sensing Data for
Natural Resources Mapping
(image enhancement, rectifications …)
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Why is this important?
What are the major considerations?
Examples of digital remote sensing processing
Follow up exercises
Image Enhancement:
Example: Displaying an image without applying “stretch”
Visually Enhanced Image
Original Image
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Image Enhancement:
Example: Displaying an image without applying “stretch”
Image Enhancement
Image enhancement is the process of making an image more
interpretable for a particular application. Enhancement
makes important features of raw remotely sensed data more
interpretable to human eyes.
Enhancement techniques are often used instead of
classification techniques for feature extraction - studying
and locating areas and objects on the ground and deriving
useful information from images.
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QuickBird 2.5m data
(Band 4,2,1 in RGB)
The techniques with ERDAS IMAGINE include:
1. Data correction - radiometric and geometric correction
2. Radiometric enhancement - enhancing images based on
the values of individual pixels
3. Spatial enhancement - enhancing images based on the
values of individual and neighboring pixels
4. Spectral enhancement - enhancing images by
transforming the values of each pixel on a multi-band
basis
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Image enhancement may be
performed temporarily when
an image is displayed, or
permanently on the image data
in the data file.
An example of simple image
Reduction (zooming out):
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Image enhancement may be
performed temporarily when
an image is displayed, or
permanently on the image data
in the data file.
An example of
image magnification
(Zooming in):
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ERDAS Imagine Tool
Spatial Profile - Transect
ERDAS Imagine Tool
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Radiometric Enhancement
• Radiometric enhancement deals with individual values of
pixels in the image.
• Radiometric enhancement of a multi-band image can be
considered as a series of independent, single-band
enhancement.
• Radiometric enhancement usually does not bring out the
contrast of every pixel in an image. Contrast can be lost
between some pixels, while gained on others.
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Linear Contrast Stretch
A linear contrast stretch is a simple way to improve the visible
contrast of an image. It is often necessary to contrast-stretch
raw image data, so that darker pixels can be seen on the display.
Linear Contrast Stretch
In most raw data, data file values fall within a narrow range—
usually a range much narrower than the display device is capable
of displaying. That range can be expanded to utilize the total
range of the display device (usually 0 to 255).
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Linear Contrast Stretch
Contrast enhancement (or contrast stretching) expands the
original input brightness values to make use of the total range or
sensitivity of the input device (e.g., output level of 0-255).
Linear contrast enhancement is best applied to remotely sensed
images with Gaussian or near-Gaussian histograms.
Standard Deviation Contrast Stretch
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Percentage
Linear Contrast
Stretch
To specify min k
and max k that lie a
certain percentage
of pixels from the
mean of the
histogram.
+1 Standard
Deviation
Contrast Stretch
Linear Contrast Enhancement:
Minimum- Maximum Contrast Stretch
where:
- BVin is the original input brightness value
- quantk is the range of the brightness values that can be
displayed on the CRT (e.g., 255),
mink is the minimum value in the image,
maxk is the maximum value in the image, and
BVout is the output brightness value
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Linear Contrast Enhancement:
Minimum- Maximum Contrast Stretch
All other original brightness values between 5 and
104 are linearly distributed between 0 and 255.
Min.-Max.
Contrast Stretch
+1 Standard
Deviation
Contrast Stretch
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Original
Min.-Max.
Contrast Stretch
Minimum-maximum
+1 Standard
Deviation
Contrast Stretch
+1 standard deviation
Piecewise Linear Contrast Stretch
A piecewise linear contrast stretch allows for the enhancement
of a specific portion of data by dividing the lookup table into
three sections: low, middle, and high. Or, the selective pieces of
the histogram are linearly contrast stretched.
It enables the user to create
a number of straight line
segments which can
simulate a curve. The user
can enhance the contrast or
brightness of any section in
a single color at a time. This
technique is very useful for
enhancing image areas in
shadow or other areas of
low contrast.
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A lookup table is an ordered set of numbers, which is used to
perform a function on a set of input values. To display or print an
image, lookup tables translate file values into brightness values.
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Nonlinear Contrast Stretch
A nonlinear spectral
enhancement can be used to
gradually increase or decrease
contrast over a range, instead
of applying the same amount
of contrast (slope) across the
entire image.
Usually, nonlinear
enhancements bring out the
contrast in one range while
decreasing the contrast in
other ranges.
Histogram Matching
Histogram matching is the process of determining a lookup table
that will convert the histogram of one image to resemble the
histogram of another.
Landsat TM (August 1995 and 1998, Bands 4,3,2)
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Histogram matching is useful for matching data of the same or adjacent scenes that
were scanned on separate days, or are slightly different because of sun angle or
atmospheric effects. This is especially useful for mosaicking or change detection.
Histogram Matching
To achieve good results with histogram matching, the two input
images should have similar characteristics:
1. The general shape of the histogram curves should be similar.
2. Relative dark and light features in the image should be the
same.
3. For some applications, the spatial resolution of the data
should be the same.
4. The relative distributions of land covers should be about the
same, even when matching scenes that are not of the same
area. If one image has clouds and the other does not, then the
clouds should be “removed” before matching the histograms.
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Spatial Enhancement
While radiometric enhancements operate on each pixel
individually, spatial enhancement modifies pixel values based
on the values of surrounding pixels.
A characteristics of remotely sensed images is a parameter
called spatial frequency, defined as the number of changes in
brightness value per unit distance for any particular part of an
image.
Spatial enhancement deals largely with spatial frequency, which
is the difference between the highest and lowest values of a
contiguous set of pixels.
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Spatial Enhancement
Spatial frequency is defined as “the number of changes in
brightness value per unit distance for any particular part of an
image”
1. Zero spatial frequency - a flat image, in which every pixel has
the same value
2. Low spatial frequency - an image consisting of a smoothly
varying gray scale
3. Highest spatial frequency - an image consisting of a
checkerboard of black and white pixels
Spatial Enhancement
Spatial frequency in remotely sensed imagery may be enhanced
or subdued using two different approaches:
- Spatial convolution filtering based primarily on the use of
convolution masks, and
- Fourier analysis which mathematically separates an image
into its spatial frequency components.
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Filtering is a broad term, referring to the altering of spatial or
spectral features for image enhancement. Convolution filtering is
one method of spatial filtering.
Convolution Filtering
• Convolution filtering is the process of averaging small sets of
pixels across an image.
• Convolution filtering is used to change the spatial frequency
characteristics of an image.
A Spatial convolution kernel is a matrix of numbers that is used
to average the value of each pixel with the values of surrounding
pixels in a particular way. The numbers in the matrix serve to
weight this average toward particular pixels. These numbers are
often called coefficients, because they are used as such in the
mathematical equations.
The size of the neighborhood convolution mask or kernel (n) is
usually 3 x 3, 5 x 5, 7 x 7, or 9 x 9.
An example of 3 x 3 kernel:
Mask template =
c1
c4
c7
c2
c5
c8
c3
c6
c9
1
1
1
1
1
1
1
1
1
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Spatial Convolution Filtering
The coefficients, c1, in the mask are multiplied by the
following individual brightness values (BVi) in the input
image:
c1 x BV1 c2 x BV2 c3 x BV3
Mask template =
c4 x BV4 c5 x BV5 c6 x BV6
c7 x BV7 c8 x BV8 c9 x BV9
The primary input pixel under investigation at any one time
is BV5 = BVi,j
Various Convolution Mask Kernels
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Low-pass filter: is to de-emphasize or block the high
spatial frequency details.
The simplest low-pass filter evaluates a particular input
pixel brightness value, BVin, and the pixels surrounding the
input pixel, and output a new brightness value, BVout, that is
the mean of this convolution.
Spatial Convolution Filtering: Low Frequency Filter
1
1
1
1
1
1
1
1
1
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Low-pass filter:
170
(Averaged)
3
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9
Low Pass Filter
4
36
45
5
9
9
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Spatial Convolution Filtering: High-pass (Frequency) Filter
High-pass filtering is applied to imagery to remove the
slowly varying components and enhance the highfrequency local variations. One high-frequency filter
(HFF5,out) is computed by subtracting the output of the
low-frequency filter (LFF5,out) from twice the value of the
original central pixel value, BV5:
High-pass Filter
Is applied to remove the slowly varying components and enhance
the high-frequency local variations.
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Spatial Convolution Filtering: Edge Enhancement
For many remote sensing Earth science applications, the
most valuable information that may be derived from an
image is contained in the edges surrounding various objects
of interest. Edge enhancement delineates these edges and
makes the shapes and details comprising the image more
conspicuous and perhaps easier to analyze. Edges may be
enhanced using either linear or nonlinear edge
enhancement techniques.
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Compass gradient masks may be used to perform two-dimensional,
discrete differentiation directional edge enhancement.
Compass gradient masks may be used to perform two-dimensional,
discrete differentiation directional edge enhancement.
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Compass gradient masks may be used to perform two-dimensional,
discrete differentiation directional edge enhancement.
Spatial Convolution Filtering: Directional FirstDifference Linear Edge Enhancement
The result of the subtraction can be either negative or
possible, therefore a constant, K (usually 127) is added
to make all values positive and centered between 0 and
255.
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Resolution Merge
Resolution merge resamples low spatial resolution data to a
higher spatial resolution based on the finer spatial information
provider and retains spectral characteristics carried by the
original data.
For example, Landsat TM sensors have six spectral bands with
30-m spatial resolution and a panchromatic band with 10-m
spatial resolution. Integrating the two types of images can yield
a new dataset with 10-m resolution and retain the spectral
characteristics of the sensors.
Resolution Merge
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Resolution Merge
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