Introduction to Photogrammetry and Remote Sensing (SGHG 1473) · Image enhancement • Information...
Transcript of Introduction to Photogrammetry and Remote Sensing (SGHG 1473) · Image enhancement • Information...
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Image enhancement
Introduction to Photogrammetry and Remote Sensing (SGHG 1473)
Dr. Muhammad Zulkarnain Abdul Rahman
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Image enhancement
• Enhancements are used to make it easier for visual interpretation and understanding of imagery
• Subtle differences in brightness value can be highlighted either by:
– Contrast modification or
– by assigning quite different colours to those levels (density slicing)
• Point operations change the value of each individual pixel independent of all other pixels
• Local operations change the value of individual pixels in the context of the values of neighboring pixels
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Image enhancement
• Information enhancement includes:
– Image reduction,
– Image magnification,
– Transect extraction,
– Contrast adjustments (linear and non-linear),
– Band rationing,
– Spatial filtering,
– Fourier transformations,
– Principle components analysis,
– Image sharpening, and
– Texture transformations
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Visualization
• Color spaces for visualization - Three approaches:
– Red-Green-Blue (RGB) space – based on additive principle of colors
• The way TV and computer screen operate
• 3 channel (R,G,B)
– Intensity-Hue-Saturation (IHS) space
– Yellow-Magenta-Cyan (YMC) space - based on subtractive principle of colors
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Contrast enhancement
• Materials or objects reflect or emit similar amounts of radiant flux (so similar pixel value)
• Only intended to improve the visual quality of a displayed image by increasing the range (spreading or stretching) of data values to occupy the available image display range (usually 0-255)
• Linear technique
– Minimum-maximum contrast stretch
– Percentage linear contrast stretch
– Standard devia=on contrast stretch
– Piecewise linear contrast stretch
• Non-linear technique
– Histogram equaliza=on
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Minimum-maximum contrast stretch
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Contrast Stretching of Predawn
Thermal Infrared Data of the
the Savannah River
Original
Minimum-
maximum
+1 standard
deviation
Jensen, 2011
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Piecewise linear contrast stretch
Characterised
by a set of user
specified break
points
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Histogram equalization
• In practice a perfectly uniform histogram cannot be achieved for digital image data
• To make sure that each bar in the image histogram has the same height
• Such a histogram has associated with it an image that utilises the available brightness levels equally and
• Should give a display in which there is good representation of detail at all brightness values
• The method of producing a uniform histogram is known generally as histogram equalization
• Reduces the contrast in the very light or dark parts of the image associated with the tails of a normally distributed histogram
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Jensen, 2011
Specific percentage
linear contrast stretch
designed to highlight the
thermal plume
Histogram Equalization
Contrast Stretching of Predawn Thermal
Infrared Data of the the Savannah River
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Band ratioing
, ,
, ,
, ,
i j k
i j ratio
i j l
BVBV
BV=
, ,
, ,
, ,
i j k
i j ratio
i j l
BVBV
BV=
where:
BVi,j,k is the original input brightness value in band k
BVi,j,l is the original input brightness value in band l
BVi,j,ratio is the ratio output brightness value
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Band
Ratioing of
Charleston,
SC Landsat
Thematic
Mapper
Data
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Band Ratio Image
Landsat TM
Band 4 / Band 3
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Spatial filtering
• Spatial Filtering to Enhance Low- and High-Frequency Detail and Edges
• 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 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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Spatial Convolution Filtering
• A linear spatial filter is a filter for which the brightness value (BVi,j,out) at location i,j in the output image is a function of some weighted average (linear combination) of brightness values located in a particular spatial pattern around the i,j location in the input image
• The process of evaluating the weighted neighboring pixel values is called two-dimensional convolution filtering.
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Spatial Convolution Filtering
• The size of the neighborhood convolution mask
or kernel (n) is usually 3 x 3, 5 x 5, 7 x 7, 9 x 9, etc.
• We will constrain our discussion to 3 x 3
convolution masks with nine coefficients, ci,
defined at the following locations:
c1 c2 c3
Mask template = c4 c5 c6
c7 c8 c9
1 1 1
1 1 1
1 11
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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
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Spatial Convolution Filtering: Low
Frequency Filter
1
1
1
1
1
1
1
1
1
9
1
5,
1 2 3 9
int
...int
9
i i
iout
c BV
LFFn
BV BV BV BV
=
×
=
+ + + =
∑
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