Dr. John R. Jensen Department of Geography University of South Carolina Columbia, SC 29208

Dr. John R. JensenDr. John R. JensenDepartment of GeographyDepartment of Geography

University of South CarolinaUniversity of South CarolinaColumbia, SC 29208Columbia, SC 29208

Dr. John R. JensenDr. John R. JensenDepartment of GeographyDepartment of Geography

University of South CarolinaUniversity of South CarolinaColumbia, SC 29208Columbia, SC 29208

Initial Display Alternatives and Initial Display Alternatives and Scientific VisualizationScientific Visualization

Initial Display Alternatives and Initial Display Alternatives and Scientific VisualizationScientific Visualization

Jensen, 2003Jensen, 2003Jensen, 2003Jensen, 2003

Scientific VisualizationScientific VisualizationScientific VisualizationScientific Visualization

Jensen, 2003Jensen, 2003

Input and Input and Output Output

RelationshipRelationship

Input and Input and Output Output

RelationshipRelationship


Digitized Digitized Panchromatic Panchromatic

Aerial Photograph Aerial Photograph of an Area in of an Area in

Charleston, SCCharleston, SC

Digitized Digitized Panchromatic Panchromatic

Aerial Photograph Aerial Photograph of an Area in of an Area in

Charleston, SCCharleston, SC


Displaying Remotely Sensed DataDisplaying Remotely Sensed Data


Various Class Intervals Used with Line Printer Brightness MapsVarious Class Intervals Used with Line Printer Brightness Maps


Natural BreaksNatural BreaksNatural BreaksNatural Breaks Equal SizeEqual SizeEqual SizeEqual Size Equal AreaEqual AreaEqual AreaEqual Area


SymbolSymbol Perceived Perceived grayness grayness

(%)(%)

SymbolSymbol Perceived Perceived grayness grayness

(%)(%)

.. 34.534.5 O=O= 54.454.4

XX 44.844.8 MWMW 55.955.9

X-X- 48.548.5 TVATVA 57.657.6

Z=Z= 50.550.5 HIXOHIXO 64.164.1

Symbolization and Perceived Grayness of Symbolization and Perceived Grayness of Line Printer Symbolization Based on Line Printer Symbolization Based on

Transmission Densitometer MeasurementsTransmission Densitometer Measurements

Symbolization and Perceived Grayness of Symbolization and Perceived Grayness of Line Printer Symbolization Based on Line Printer Symbolization Based on

Transmission Densitometer MeasurementsTransmission Densitometer Measurements

Perception of the Perception of the Range of Range of

Overprinted Overprinted Symbols Symbols

Produced Using a Produced Using a Line PrinterLine Printer

Perception of the Perception of the Range of Range of

Overprinted Overprinted Symbols Symbols

Produced Using a Produced Using a Line PrinterLine Printer


Perc

eive

d G

rayn

ess

Crossed-line ShadingCrossed-line ShadingCrossed-line ShadingCrossed-line Shading


Crossed-line ShadingCrossed-line ShadingCrossed-line ShadingCrossed-line Shading


RGB Color Coordinate SystemRGB Color Coordinate SystemRGB Color Coordinate SystemRGB Color Coordinate System


8-bit Digital Image Processing System8-bit Digital Image Processing System8-bit Digital Image Processing System8-bit Digital Image Processing System


An An 8-bit8-bit video image display system consists of several different components: video image display system consists of several different components:

The computer's central processing unit (CPU) accesses the remotely sensed The computer's central processing unit (CPU) accesses the remotely sensed data from a mass storage device such as a hard disk, CD, or DVD and transfers data from a mass storage device such as a hard disk, CD, or DVD and transfers the bytes of information to the image processor display memory (i.e., a the bytes of information to the image processor display memory (i.e., a graphics card). The image processor display memory typically consists of graphics card). The image processor display memory typically consists of > 64 megabytes of RAM. Each brightness value (> 64 megabytes of RAM. Each brightness value (BVBVi,j,ki,j,k) at row () at row (ii) and ) and

column (column (jj) of a single band () of a single band (kk) of imagery is stored in the display memory. ) of imagery is stored in the display memory. Each line of data stored in the display memory is scanned every Each line of data stored in the display memory is scanned every 1/301/30 second by second by a read mechanism. This is faster than the human eye can detect, therefore we a read mechanism. This is faster than the human eye can detect, therefore we do not perceive what is taking place. The brightness values encountered are do not perceive what is taking place. The brightness values encountered are passed through a passed through a color look-up tablecolor look-up table that is read by a that is read by a digital-to-analog (DAC) digital-to-analog (DAC) converterconverter. The red, green, and blue (RGB) analog output from the DAC . The red, green, and blue (RGB) analog output from the DAC is used to stimulate the RGB phospors at each pixel location on the video is used to stimulate the RGB phospors at each pixel location on the video monitor.monitor.

An An 8-bit8-bit video image display system consists of several different components: video image display system consists of several different components:

The computer's central processing unit (CPU) accesses the remotely sensed The computer's central processing unit (CPU) accesses the remotely sensed data from a mass storage device such as a hard disk, CD, or DVD and transfers data from a mass storage device such as a hard disk, CD, or DVD and transfers the bytes of information to the image processor display memory (i.e., a the bytes of information to the image processor display memory (i.e., a graphics card). The image processor display memory typically consists of graphics card). The image processor display memory typically consists of > 64 megabytes of RAM. Each brightness value (> 64 megabytes of RAM. Each brightness value (BVBVi,j,ki,j,k) at row () at row (ii) and ) and

column (column (jj) of a single band () of a single band (kk) of imagery is stored in the display memory. ) of imagery is stored in the display memory. Each line of data stored in the display memory is scanned every Each line of data stored in the display memory is scanned every 1/301/30 second by second by a read mechanism. This is faster than the human eye can detect, therefore we a read mechanism. This is faster than the human eye can detect, therefore we do not perceive what is taking place. The brightness values encountered are do not perceive what is taking place. The brightness values encountered are passed through a passed through a color look-up tablecolor look-up table that is read by a that is read by a digital-to-analog (DAC) digital-to-analog (DAC) converterconverter. The red, green, and blue (RGB) analog output from the DAC . The red, green, and blue (RGB) analog output from the DAC is used to stimulate the RGB phospors at each pixel location on the video is used to stimulate the RGB phospors at each pixel location on the video monitor.monitor.



R

G B

Color Density Slice of the Color Density Slice of the Thematic Mapper Band 4 Charleston, SC ImageThematic Mapper Band 4 Charleston, SC Image



8-bit Digital 8-bit Digital Image Image

Processing Processing SystemSystem




Color Class Color Class

IntervalInterval

Visual Visual

ColorColor

Color Lookup Color Lookup Table ValuesTable Values

RedRed, , GreenGreen, , BlueBlue

Brightness ValueBrightness Value

Low HighLow High

11 CyanCyan 0, 255, 2550, 255, 255 0 160 16

Shade of grayShade of gray 17, 17, 1717, 17, 17 17 1717 17



** ** ** **

** ** 59, 59, 5959, 59, 59 59 5959 59

22 RedRed 255, 0, 0255, 0, 0 60 25560 255

Class Intervals and Color Lookup Table Values for Color Density Class Intervals and Color Lookup Table Values for Color Density Slicing the Thematic Mapper Band 4 Charleston, SC ImageSlicing the Thematic Mapper Band 4 Charleston, SC Image

Class Intervals and Color Lookup Table Values for Color Density Class Intervals and Color Lookup Table Values for Color Density Slicing the Thematic Mapper Band 4 Charleston, SC ImageSlicing the Thematic Mapper Band 4 Charleston, SC Image





Color Density Slice of the Color Density Slice of the Thermal Infrared Image of the Savannah RiverThermal Infrared Image of the Savannah River



Color Class Color Class

IntervalInterval

Visual Visual

ColorColor

Color Lookup Color Lookup Table ValuesTable Values

RedRed, , GreenGreen, , BlueBlue

Apparent Apparent TemperatureTemperature

Low HighLow High

Brightness ValueBrightness Value Low HighLow High

1. Land1. Land graygray 127, 127 127127, 127 127 -3.0 11.6-3.0 11.6 0 730 73

2. River 2. River AmbientAmbient

Dark blueDark blue 0, 0, 1200, 0, 120 11.8 12.211.8 12.2 74 7674 76

3. +1 C3. +1 C Light blueLight blue 0, 0, 2550, 0, 255 12.4 13.012.4 13.0 77 80 77 80

4. 1.2 – 2.8 C4. 1.2 – 2.8 C GreenGreen 0, 255, 00, 255, 0 13.2 14.813.2 14.8 81 8981 89

5. 3.0 – 5.0 C5. 3.0 – 5.0 C YellowYellow 255, 255, 0255, 255, 0 15.0 17.015.0 17.0 90 10090 100

6. 5.2 – 10.0 C6. 5.2 – 10.0 C OrangeOrange 255, 50, 0255, 50, 0 17.2 22.017.2 22.0 101 125101 125

7. 10.2 – 20 C7. 10.2 – 20 C RedRed 255, 0 , 0255, 0 , 0 22.2 32.022.2 32.0 126 176126 176

8. > 20 C8. > 20 C WhiteWhite 255, 255,255255, 255,255 32.2 48.032.2 48.0 177 255177 255

Class Intervals and Color Lookup Table Values for Color Density Class Intervals and Color Lookup Table Values for Color Density Slicing the Pre-dawn Thermal Infrared Image of the Savannah RiverSlicing the Pre-dawn Thermal Infrared Image of the Savannah River

Class Intervals and Color Lookup Table Values for Color Density Class Intervals and Color Lookup Table Values for Color Density Slicing the Pre-dawn Thermal Infrared Image of the Savannah RiverSlicing the Pre-dawn Thermal Infrared Image of the Savannah River













Color Composite of Marco Color Composite of Marco Island, Florida Island, Florida

SPOT Imagery SPOT Imagery October 11, 1988October 11, 1988

Created using the band Created using the band substitution method:substitution method:

RR = SPOT band 3 (NIR) 20 m = SPOT band 3 (NIR) 20 mGG = SPOT band 4 (Pan) 10 m = SPOT band 4 (Pan) 10 mBB = SPOT band 1 (Green) 20 m = SPOT band 1 (Green) 20 m






3

1

3

1

jj

kk

rAbs

sOIF

3

1

3

1

jj

kk

rAbs

sOIF


Where Where sskk is the standard deviation for band is the standard deviation for band kk, and , and rrjj is the absolute is the absolute

value of the correlation coefficient between any two of the three value of the correlation coefficient between any two of the three bands being evaluated. The largest OIF will generally have the bands being evaluated. The largest OIF will generally have the most information (as measured by variance) with the least amount most information (as measured by variance) with the least amount of duplication (as measured by correlation) of duplication (as measured by correlation)

Where Where sskk is the standard deviation for band is the standard deviation for band kk, and , and rrjj is the absolute is the absolute

value of the correlation coefficient between any two of the three value of the correlation coefficient between any two of the three bands being evaluated. The largest OIF will generally have the bands being evaluated. The largest OIF will generally have the most information (as measured by variance) with the least amount most information (as measured by variance) with the least amount of duplication (as measured by correlation) of duplication (as measured by correlation)

Optimum Index FactorOptimum Index Factor Ranks the 20 three-band combinations that can Ranks the 20 three-band combinations that can

be made from six bands of Landsat TM data (not be made from six bands of Landsat TM data (not including the thermal-infrared band). including the thermal-infrared band).


be made from six bands of Landsat TM data (not be made from six bands of Landsat TM data (not including the thermal-infrared band). including the thermal-infrared band).

Band combination:Band combination:1,2,31,2,31,2,41,2,41,2,51,2,51,2,61,2,62,3,42,3,42,3,52,3,52,3,62,3,63,4,53,4,5

3,4,6 etc.3,4,6 etc.

Band combination:Band combination:1,2,31,2,31,2,41,2,41,2,51,2,51,2,61,2,62,3,42,3,42,3,52,3,52,3,62,3,63,4,53,4,5

3,4,6 etc.3,4,6 etc.

7.52512.0

27

8.0*8.0*8.0

3*3*3OIF 7.52

512.0

27

8.0*8.0*8.0

3*3*3OIF



be made from six bands of Landsat TM databe made from six bands of Landsat TM data


be made from six bands of Landsat TM databe made from six bands of Landsat TM data

22512.0

27

6.0*5.0*4.0

3*3*3OIF 225

12.0

27

6.0*5.0*4.0

3*3*3OIF

3

1

3

1

jj

kk

rAbs

sOIF

3

1

3

1

jj

kk

rAbs

sOIF

Band combination:Band combination:1,2,31,2,3





Computing the Computing the DistanceDistance (Length) of a Linear Feature (Length) of a Linear FeatureComputing the Computing the DistanceDistance (Length) of a Linear Feature (Length) of a Linear Feature

DistanceDistance is one of them most important geographic is one of them most important geographic measurements extracted from remotely sensed imagery. measurements extracted from remotely sensed imagery. Distance measurements are usually made using a Distance measurements are usually made using a rubber-band rubber-band tooltool that lets the analyst identify beginning and ending vertices that lets the analyst identify beginning and ending vertices of the line and their X- and Y-coordinates. If the remotely of the line and their X- and Y-coordinates. If the remotely sensed data has not been rectified to a standard map projection, sensed data has not been rectified to a standard map projection, then the X- and Y-coordinates will be in row and column space then the X- and Y-coordinates will be in row and column space ((i,ji,j). If the imagery has been geometrically rectified to a ). If the imagery has been geometrically rectified to a standard map projection then the X- and Y-coordinates will be standard map projection then the X- and Y-coordinates will be in longitude and latitude or some other coordinate system. One in longitude and latitude or some other coordinate system. One of the most commonly used map projections is the Universal of the most commonly used map projections is the Universal Transverse Mercator (UTM) projection with X-coordinates in Transverse Mercator (UTM) projection with X-coordinates in meters from a standard meridian and Y-coordinates in meters meters from a standard meridian and Y-coordinates in meters measured from the Equator. measured from the Equator.

DistanceDistance is one of them most important geographic is one of them most important geographic measurements extracted from remotely sensed imagery. measurements extracted from remotely sensed imagery. Distance measurements are usually made using a Distance measurements are usually made using a rubber-band rubber-band tooltool that lets the analyst identify beginning and ending vertices that lets the analyst identify beginning and ending vertices of the line and their X- and Y-coordinates. If the remotely of the line and their X- and Y-coordinates. If the remotely sensed data has not been rectified to a standard map projection, sensed data has not been rectified to a standard map projection, then the X- and Y-coordinates will be in row and column space then the X- and Y-coordinates will be in row and column space ((i,ji,j). If the imagery has been geometrically rectified to a ). If the imagery has been geometrically rectified to a standard map projection then the X- and Y-coordinates will be standard map projection then the X- and Y-coordinates will be in longitude and latitude or some other coordinate system. One in longitude and latitude or some other coordinate system. One of the most commonly used map projections is the Universal of the most commonly used map projections is the Universal Transverse Mercator (UTM) projection with X-coordinates in Transverse Mercator (UTM) projection with X-coordinates in meters from a standard meridian and Y-coordinates in meters meters from a standard meridian and Y-coordinates in meters measured from the Equator. measured from the Equator.


Once the coordinates of the beginning Once the coordinates of the beginning ((XX11, Y, Y11) and ending ) and ending

vertices vertices ((XX22, Y, Y22) are identified, it is a simple task to use the ) are identified, it is a simple task to use the

Pythagorean theorum which states that the hypotenuse of a Pythagorean theorum which states that the hypotenuse of a right triangle right triangle (c)(c) can be computed if we know the length of can be computed if we know the length of the other two legs of a right triangle (the other two legs of a right triangle (aa and and bb, respectively):, respectively):

Once the coordinates of the beginning Once the coordinates of the beginning ((XX11, Y, Y11) and ending ) and ending

vertices vertices ((XX22, Y, Y22) are identified, it is a simple task to use the ) are identified, it is a simple task to use the

Pythagorean theorum which states that the hypotenuse of a Pythagorean theorum which states that the hypotenuse of a right triangle right triangle (c)(c) can be computed if we know the length of can be computed if we know the length of the other two legs of a right triangle (the other two legs of a right triangle (aa and and bb, respectively):, respectively):

Computing the Distance (Length) of a Linear FeatureComputing the Distance (Length) of a Linear FeatureComputing the Distance (Length) of a Linear FeatureComputing the Distance (Length) of a Linear Feature

221

221

222

YYXXc

bac

221

221

222

YYXXc

bac

c

a

b

Marco Island, Florida SPOT 10 x 10 m Panchromatic DataMarco Island, Florida SPOT 10 x 10 m Panchromatic DataMarco Island, Florida SPOT 10 x 10 m Panchromatic DataMarco Island, Florida SPOT 10 x 10 m Panchromatic Data


Distance MeasurementDistance MeasurementDistance MeasurementDistance Measurement


Distance and Distance and Area Area

MeasurementMeasurement





This logic may be used to identify the length of the This logic may be used to identify the length of the longest axis of the mangrove island, where:longest axis of the mangrove island, where:

This logic may be used to identify the length of the This logic may be used to identify the length of the longest axis of the mangrove island, where:longest axis of the mangrove island, where:

Computing the Distance (Length) of a Linear FeatureComputing the Distance (Length) of a Linear FeatureComputing the Distance (Length) of a Linear FeatureComputing the Distance (Length) of a Linear Feature

mc

c

c

84.2263

786.1483913422.3641036

41.286101457.286223204.43334689.431437 22

mc

c

c

84.2263

786.1483913422.3641036

41.286101457.286223204.43334689.431437 22


Computing the Computing the AreaArea of an of an Area of Interest (AOI) PolygonArea of Interest (AOI) Polygon

Computing the Computing the AreaArea of an of an Area of Interest (AOI) PolygonArea of Interest (AOI) Polygon

The area of a rectangle on a remotely sensed image is The area of a rectangle on a remotely sensed image is computed simply by multiplying the values of its length and computed simply by multiplying the values of its length and width, i.e., width, i.e., A = l x wA = l x w. Another elementary area computation is . Another elementary area computation is that of a circle which is that of a circle which is A = A = rr22. Complications can arise, . Complications can arise, however, when the shape of the polygon varies from a however, when the shape of the polygon varies from a rectangle or circle. In the remote sensing literature, polygons rectangle or circle. In the remote sensing literature, polygons are also often referred to asare also often referred to as areas of interestareas of interest (AOIs). (AOIs).

The area of a rectangle on a remotely sensed image is The area of a rectangle on a remotely sensed image is computed simply by multiplying the values of its length and computed simply by multiplying the values of its length and width, i.e., width, i.e., A = l x wA = l x w. Another elementary area computation is . Another elementary area computation is that of a circle which is that of a circle which is A = A = rr22. Complications can arise, . Complications can arise, however, when the shape of the polygon varies from a however, when the shape of the polygon varies from a rectangle or circle. In the remote sensing literature, polygons rectangle or circle. In the remote sensing literature, polygons are also often referred to asare also often referred to as areas of interestareas of interest (AOIs). (AOIs).

Geographic Area of Interest (Geographic Area of Interest (AOIAOI) ) Geographic Area of Interest (Geographic Area of Interest (AOIAOI) )


Area MeasurementArea MeasurementArea MeasurementArea Measurement








To calculate the area of a polygon (or To calculate the area of a polygon (or AOIAOI) in remotely sensed ) in remotely sensed imagery, the analyst typically uses a rubber-band tool to identify imagery, the analyst typically uses a rubber-band tool to identify nn vertices at unique map (vertices at unique map (XX, , YY) or image (row and column) ) or image (row and column) coordinates. The “contribution” of each point (vertex) in the coordinates. The “contribution” of each point (vertex) in the polygon to the area is computed by evaluating the polygon to the area is computed by evaluating the XX-coordinate -coordinate of a vertex prior to the vertex under examination of a vertex prior to the vertex under examination (X(Xi-1i-1)) with the with the

XX-coordinate of the next vertex in the sequence -coordinate of the next vertex in the sequence (X(Xi+1i+1)) and and

multiplying the result by the multiplying the result by the YY-coordinate -coordinate (Y(Yii)) of the vertex of the vertex

under examination according to the following formula:under examination according to the following formula:

To calculate the area of a polygon (or To calculate the area of a polygon (or AOIAOI) in remotely sensed ) in remotely sensed imagery, the analyst typically uses a rubber-band tool to identify imagery, the analyst typically uses a rubber-band tool to identify nn vertices at unique map (vertices at unique map (XX, , YY) or image (row and column) ) or image (row and column) coordinates. The “contribution” of each point (vertex) in the coordinates. The “contribution” of each point (vertex) in the polygon to the area is computed by evaluating the polygon to the area is computed by evaluating the XX-coordinate -coordinate of a vertex prior to the vertex under examination of a vertex prior to the vertex under examination (X(Xi-1i-1)) with the with the

XX-coordinate of the next vertex in the sequence -coordinate of the next vertex in the sequence (X(Xi+1i+1)) and and

multiplying the result by the multiplying the result by the YY-coordinate -coordinate (Y(Yii)) of the vertex of the vertex

under examination according to the following formula:under examination according to the following formula:

Computing the Area of an Area of Interest (AOI)Computing the Area of an Area of Interest (AOI)Computing the Area of an Area of Interest (AOI)Computing the Area of an Area of Interest (AOI)

111

5.0

ii

n

ii XXYArea 11

1

5.0

ii

n

ii XXYArea

Computation of Computation of the Area of a the Area of a

Mangrove Mangrove Island Near Island Near

Marco Island, Marco Island, FL using SPOT FL using SPOT

10 x 10 m 10 x 10 m Panchromatic Panchromatic

Data Data

Computation of Computation of the Area of a the Area of a

Mangrove Mangrove Island Near Island Near

Marco Island, Marco Island, FL using SPOT FL using SPOT

10 x 10 m 10 x 10 m Panchromatic Panchromatic

Data Data








All data sets to be merged must be accurately registered to one All data sets to be merged must be accurately registered to one another and another and resampledresampled to the same pixel size. Several to the same pixel size. Several alternatives exist for merging the data sets, including:alternatives exist for merging the data sets, including:

1.1. Simple Simple band substitutionband substitution methods methods 2.2. Color space transformationColor space transformation and and substitutionsubstitution methods using methods using

various color coordinate systems.various color coordinate systems.3.3. SubstitutionSubstitution of the high spatial resolution data for of the high spatial resolution data for principal principal

component #1.component #1.

All data sets to be merged must be accurately registered to one All data sets to be merged must be accurately registered to one another and another and resampledresampled to the same pixel size. Several to the same pixel size. Several alternatives exist for merging the data sets, including:alternatives exist for merging the data sets, including:

1.1. Simple Simple band substitutionband substitution methods methods 2.2. Color space transformationColor space transformation and and substitutionsubstitution methods using methods using

various color coordinate systems.various color coordinate systems.3.3. SubstitutionSubstitution of the high spatial resolution data for of the high spatial resolution data for principal principal

component #1.component #1.

Merging Different Types of Remotely Sensed Merging Different Types of Remotely Sensed Data for Effective Visual DisplayData for Effective Visual Display











Intensity, Hue, Saturation (HIS) Color Coordinate SystemIntensity, Hue, Saturation (HIS) Color Coordinate SystemIntensity, Hue, Saturation (HIS) Color Coordinate SystemIntensity, Hue, Saturation (HIS) Color Coordinate System



Intensity-Hue-Saturation (HIS) SubstitutionIntensity-Hue-Saturation (HIS) Substitution::The vertical axis represents The vertical axis represents intensity (I)intensity (I) which varies from which varies from black (0) to white (255) and is not associated with any color. black (0) to white (255) and is not associated with any color. The circumference of the sphere represents The circumference of the sphere represents hue (H),hue (H), which is which is the dominant wavelength of color. Hue values begin with 0 the dominant wavelength of color. Hue values begin with 0 at the midpoint of red tones and increase counterclockwise at the midpoint of red tones and increase counterclockwise around the circumference of the sphere to conclude with 255 around the circumference of the sphere to conclude with 255 adjacent to 0. adjacent to 0. Saturation (S)Saturation (S) represents the represents the puritypurity of the color of the color and ranges from 0 at the center of the color sphere to 255 at and ranges from 0 at the center of the color sphere to 255 at the circumference. A saturation of 0 represents a completely the circumference. A saturation of 0 represents a completely impure color in which all wavelengths are equally impure color in which all wavelengths are equally represented and which the eye will perceive as a shade of represented and which the eye will perceive as a shade of gray that ranges from white to black depending on intensity. gray that ranges from white to black depending on intensity.

Intensity-Hue-Saturation (HIS) SubstitutionIntensity-Hue-Saturation (HIS) Substitution::The vertical axis represents The vertical axis represents intensity (I)intensity (I) which varies from which varies from black (0) to white (255) and is not associated with any color. black (0) to white (255) and is not associated with any color. The circumference of the sphere represents The circumference of the sphere represents hue (H),hue (H), which is which is the dominant wavelength of color. Hue values begin with 0 the dominant wavelength of color. Hue values begin with 0 at the midpoint of red tones and increase counterclockwise at the midpoint of red tones and increase counterclockwise around the circumference of the sphere to conclude with 255 around the circumference of the sphere to conclude with 255 adjacent to 0. adjacent to 0. Saturation (S)Saturation (S) represents the represents the puritypurity of the color of the color and ranges from 0 at the center of the color sphere to 255 at and ranges from 0 at the center of the color sphere to 255 at the circumference. A saturation of 0 represents a completely the circumference. A saturation of 0 represents a completely impure color in which all wavelengths are equally impure color in which all wavelengths are equally represented and which the eye will perceive as a shade of represented and which the eye will perceive as a shade of gray that ranges from white to black depending on intensity. gray that ranges from white to black depending on intensity.




Intensity-Hue-Saturation (IHS) SubstitutionIntensity-Hue-Saturation (IHS) Substitution::IHS values can be derived from the RGB values through the IHS values can be derived from the RGB values through the transformation equations: transformation equations:

Intensity-Hue-Saturation (IHS) SubstitutionIntensity-Hue-Saturation (IHS) Substitution::IHS values can be derived from the RGB values through the IHS values can be derived from the RGB values through the transformation equations: transformation equations:



I

BIS

BI

BGH

BGRI

33

I

BIS

BI

BGH

BGRI

33

Substitute Substitute IntensityIntensity data from the data from the IHS IHS transformationtransformation for for one of the bands, e.g., one of the bands, e.g., RRGGBB = = 44, , II, , 22

Substitute Substitute IntensityIntensity data from the data from the IHS IHS transformationtransformation for for one of the bands, e.g., one of the bands, e.g., RRGGBB = = 44, , II, , 22

Relationship Between RGB and IHS Color SystemsRelationship Between RGB and IHS Color SystemsRelationship Between RGB and IHS Color SystemsRelationship Between RGB and IHS Color Systems


Documents

Dr. John R. Jensen Department of Geography University of South Carolina Columbia, SC 29208