Remote sensed imagery: Landsat TM, Radarsat, and Magnetic; the IHS transformation
Last revised: February 13th 2000

	Satellite derived remote sensed images are representations of the variation in intensity of electromagnetic energy reflected from the Earth's surface. The specific image produced is determined by the wavelength of the electromagnetic energy that is being sensed, and the physical properties of the matter that reflects the energy. Aerial photographs use only the visible portion of the electromagnetic spectrum (5 x 10^-7  metres = .5 microns), whereas Landsat TM and SPOT images record the Earth's reflectivity at seven different wavelengths in the visible and infrared range, and radar images record the reflectivity of wavelengths in the non-visible range of 1 to 10 cm (microwaves).
	Landsat and SPOT are passive remote data acquisition systems in as much as they rely on sunlight reflected off the Earth to image the Earth's surface. Radar systems are active systems that send their own microwave signals down to the Earth. The longer wavelength of radar is better suited for penetration of clouds, dust or smoke, and data can also be collected in darkness.
	Other remote sensed images can also be generated by interpolating point data collected by magnetometer or gravimeter surveys.

	Web courses in remote sensing can be accessed at:

http://www.ccrs.nrcan.gc.ca/ccrs/eduref/tutorial/indexe.html
and
http://rst.gsfc.nasa.gov/TofC/table.html

Humans perceive colour by the relative intensity of the stimuli of the red, green, and blue cones of the eye. Any colour can be produced by adding together in various combinations the radiation emanating from the red, green and blue portions of the visible part of the electromagnetic spectrum (see APPENDIX A). For example, the colour yellow would be generated by a combination of 100% red, 100% green and 0% blue, the colour orange by the combination of 100% red and 50% green, and the colour olive by the combination 50% red and 50% green. In this respect, olive can be considered a dark yellow (yellow mixed with black).
If only the red cone of the eye - or the red phosphor of a video monitor (TV screen) - is being excited, the shade of red seen would reflect the relative proportion of red added to black, where the relative intensity (brightness) of the red radiation is usually measured on an 8-bit scale of 0 (black) to 255 (pure red). Where all three visible wavelengths are of equal value and are at maximum intensity (255), the eye perceives the combination as the colour white, whereas at less than maximum intensity the eye sees some shade of grey, ranging down to black at an intensity value of 0.
In the case of a computer generated image, the brightness values of a single image, which would appear grey if sent to all three RGB guns of the video monitor, can be converted to colour values through the use of a palette (also known as a Colour Look Up Table), where each brightness value ranging from 0 to 255 is assigned a colour from a range of 256 colours representing some arbitrary combination of the various shades of red, green and blue. A single image composed of readings of the red visible band could be assigned a red scale palette, allowing the variation in brightness of the image to be displayed as shades of red varying from pure red (255,0,0) to dark red (e.g. 127,0,0) to black (0,0,0). Such images are said to be 'pseudocoloured'.
When three separate red, green, and blue images (TM bands 3, 2, 1, respectively) are sent to the red, green and blue guns of the display device at the same time, a colour composite image will be displayed that will replicate the image as seen directly by the human eye. However, if an infrared image band is substituted for one of the visible bands, the resulting 'false colour' image will no longer resemble the image seen by the naked eye; in this case, we fake seeing the infrared wavelength by substituting it for one of the visible wavelengths.

The image display is also influenced by the size of the cells making up the image. In the case of TM images, the size of the cell (resolution) is 30 metres, whereas for SPOT images it is 10 metres. RADARSAT images have variable resolution but the maximum resolution is 8 metres.

	No matter what kind of electromagnetic data is being imaged, the image produced is merely a raster representation of a matrix of numerical values, where the attribute of each cell in the matrix represents the average intensity of reflection of the electromagnetic radiation at the location represented by the cell. The range and frequency of reflectance values can be examined in the form of a histogram in most GIS software packages. (In IDRISI, the histogram for an image is displayed by selecting HISTO in the DISPLAY menu.)

	The display characteristics of the image can be improved by using a modification technique called CONTRAST STRETCH. For example, if an image is composed of reflectance values of 10 to 19 (a range of only 10 values equal to 4% of the total range of 255 values of the grey scale), the image will appear virtually black because we are unable to discriminate between the different degrees of blackness in this part of the grey scale (the contrast ratio - brightest/darkest is low). However if the image is 'stretched' so that the value of 10 is given a value of 0 (black) and the value 19 a value of 255 (white), the intermediate values between 10 and 19 will be spread through the full range of grey, and because of the dramatic increase in the contrast ratio, the degree of variation will now be more easily visible to the human eye.
	The image can also be 'density sliced', which means that the total range of values from 0 to 255 can be displayed as, for example, only 16 colours, each representing a range of 16 values, or 8 ranges of 32 values each.

	The Landsat TM image bands used in this exercise include bands 3, 4 and 5. Band 3 (0.63-0.69 microns) is the visible Red band and matches a chlorophyll absorption band that is important for discriminating vegetation types, i.e. low reflectance values for vegetation and higher values for rocks. Band 4 (0.76-0.9 microns; reflected infrared) is useful for mapping total biomass (trees, bushes, grass) content and shorelines, i.e. high reflectance from vegetation, and total absorption of infrared wavelengths by water. Band 5 (1.55-1.75 microns; reflected infrared) indicates moisture content between vegetation types and between vegetation and rocks/dry soil, the latter having a higher reflectance. Band 7 will detect minerals with a high bonded OH content, such as clays and serpentine (see Sabins, Chapter 11, Mineral Exploration)

	COLOUR MODELS
	
	In the RGB COLOUR MODEL digital images can be displayed in one of three ways:
	
	1) as a greyscale image, with the same pixel value being sent to all three colour guns of the display device (the monitor).
	2) as a pseudocoloured image, where the colour of each cell corresponds to that of a numbered tile in a colour palette or Look Up Table (LUT).
	3) as a colour composite of three bands of data (three images), where the intensity of the value of the same cell in three different images is mapped separately to the red, green, and blue guns of the display device.

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IDRISI COMPOSIT Notes
1. COMPOSIT is designed to produce color composite images to be displayed with the IDRISI Color Composite 256 palette. To create the composite image from three input bands, each of the three bands is stretched to 6 levels (6 * 6 * 6 = 216). The composite image consists of color indices where each index = blue + (green * 6) + (red * 36) assuming a range from 0-5 on each of the three bands. For example a pixel with RGB values of 1, 5, 3 would have an index of 3 + (5 * 6) + (1 * 36) = 69.  The Color Composite 256 palette colors correspond to the mix of blue, green and red in the stretched images.
2. The Color Composite 256 palette contains composite colors from color index 0 through color index 215 (the maximum value in an IDRISI composite image). The first six index values range from black (0) to pure blue (5). The second set of index values ranges from green at 20% (6) to 20% green plus 100 blue (11). The third set begins with green at 40% and again ranges to 40% green plus pure blue. And so on! Colors 216-255 include colors that may be useful for displaying features, such as roads, that are rasterized onto the color composite image.  Use Palette Workshop to see these particular colors.
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	4) A fourth method of displaying images uses the Intensity - Hue - Saturation (IHS) MODEL, where three-colour RGB mixtures can be described in terms of Hue, two-colour mixtures (dominant wavelength) of Red and Green, Green and Blue, or Blue and Red; Saturation, the degree of mixing of Hue with white light (intermediate values of saturation represent pastel shades); and Intensity, the proportion of white light relative to black. The RGB and HIS models are simply different representations of the same colour space and consequently RGB images can be transformed into HIS images and vice versa. It is easier to think of colour in terms of IHS than in terms of RGB. For example, adding equal proportions of green and blue to red in the proportions 25% (127), 25% (127), 50% (255), respectively, is the same as adding white to red in the proportion 75% to 25% (25% red + 25% green + 25% blue = 75% white), the resulting colour in both cases being a pastel pink. Again, it is easier to image pastel orange as a mixture of red and yellow made pastel by adding white, than it is to think of it as a mixture of red green and blue. Saturated colours are made more or less brighter by changing the intensity, that is, by adding/subtracting black.

	The IHS transformation is commonly used to merge several bands of remote sensed data, e.g. high resolution SPOT panchromatic images with low resolution Landsat images, or, Landsat and Radarsat images, or, Radarsat with magnetic or gravity data.

IDRISI COLSPACE notes:

1. For more information on the conversion between RGB and HLS see Foley, J.D., A. van Dam, S.K. Feiner, J.F. Hughes, (1990)  Computer Graphics: Principles and Practice (New York:  Addison-Wesley), Chapter 13.

2. The HLS double-hexcone model used puts red at the 0 degree point (as is most common) rather than the older Tektronix convention of blue.

3. In this implementation, all images remain in byte binary format. Thus for hue, 0 represents 0 degrees and 255 represents 360. Likewise lightness and saturation both range from 0-255.

4. To merge SPOT panchromatic and multispectral data, use the original multispectral images in COLSPACE, with RGB to HLS option, to create Hue, Lightness and Saturation images. Use EXPAND with an expansion factor of 2 in X and Y to change the resolution of the Hue and Saturation images to match that of the panchromatic image. Run COLSPACE again, with HLS to RGB option, giving the Hue and Saturation images you have expanded when asked for Hue and Saturation, and giving the panchromatic band when asked for Lightness. Use COMPOSIT to composit the resulting Red, Green and 
Blue bands. Use the Color Composite palette when viewing the result.

In the current exercise we will examine the characteristics of:
Landsat TM bands 3 (visible red), 4 (photo infrared), and 5 (mid reflected infrared) (Scnbnd3.img, etc)
Radarsat for the month of June (Scn0406.img)
Magnetic vertical field gradient (Scnvg.img)
	Each image is 1700 columns by 1385 rows, and the cell resolution is 30 metres.
	The object of this exercise is to integrate the radar, Landsat TM and magnetic data into a colour composite 'fused' image.

`	PROCEDURE

The following exercise uses IDRISI as the raster Geographic Information System and Image Processing software; see:

http://www.clarklabs.org/

The sample material for the Sudnury region was purchased from Radarsat International,

http://www.radarsatinaction.com/classroom/class.htm

Files supplied RADARSAT:
	PCI .pix files
0406raw.pix RADARSAT - June 04, 1996
0406lee.pix RADARSAT - June 04 Lee filtered
1104raw.pix RADARSAT - April 11, 1996
1104lee.pix RADARSAT - April 11, 1996 Lee filtered
all1024.pix RADARSAT - April 11 and June 04, 1996, Landsat TM June, and aeromagnetic data (1024x1024)
Magdata.pix aeromagnetic data (2 channels)
Rdrtmamg.pix RADARSAT - April 11 and June 04, 1996, Landsat TM June and aeromagnetic data.
	IDRISI files:
Scnbnd3.img/.doc	Landsat TM Band 3
Scnbnd4.img/.doc	Landsat TM Band 4
Scnbnd5.img/.doc	Landsat TM Band 5
Scnmag.img/.doc	Total Magnetic field
Scn0406.img/.doc	RADARSAT - June 4 1996
Scn1104.img/.doc	RADARSAT - April 11 1996
Scnvg.img/.doc		Vertical field gradient

NOTE: the coordinate positions shown by the PCI pix files are incorrect by kilometres, and the magnetic images are incorrectly registered in both the PCI and IDRISI data sets. The descriptions of the files on the CDROM provided by RADARSAT says  that files Rdrtmamg.pix and all1024.pix are 7 channel files of RADASAT April 11  and June 04, 1996, Landsat TM June bands 3, 4, and 5, and 1024x1024  aeromagnetic data. These PCI images provides coordinate locations as lat  longs and UTMs. The UTM location of the circular island called Galliard Island in  east‑south-central Ramsey Lake, Sudbury) is approxiately 498550 E, 5153392  N, whereas the Map Sudbury 41‑I (1:250000) shows this location (to within 125 metres) at 504350 E, 5146500 N. The PCI location is therefore off by more  than 5 km East and North, whereas the south end of Wanpaitei Lake is off by 13 km. The Rdrtmamg.pix and all1024.pix data sets show the southern high intensity magnetic stripe on the vertical magnetic channel passing through the northern part of Ramsey Lake and the northern high intensity stripe as passing through Whitewater Lake to the south of Whitson Lake. However, in the Radarsat Geology Handbook the two magnetic stripes bracket these two lakes, and it would appear that the PCI magnetic image is translated and rotated clockwise out of position relative to the image in the Handbook. As geologists we also know that the southern magnetic strip marks the norite unit of the Sudbury Irruptive, as indicated on the geological map of Sudbury, showing the impossibility of its location as shown on the PCI and the other images. Since there are no control points for the magnetic image there is no easy way of georeferencing the mag data to the radarsat/Tm data. The claim by Radarsat (p. 8-12, RadarSat Training Manual) that "Strong correlations can be seen between the topographic details provided by RADARSAT data and the patterns and details supplied by the aeromagnetic data." should therefore be treated with due scepticism. (CCRS quote: "The georeferencing information included in the original files is definitely wrong and cannot be used as is.")

Copy all the files in //public/Es300B/radar to a folder called 'yourintialsrsat' in your area in //Earthnt/users. 

	Load IDRISI and set the ENVIRONMENT to //Earthnt/users/yourname/'yourintialsrsat.

Note: remember that all raster image files in IDRIS have the extension .img. When entering the name of an image file it is not usual to give the extension. If  you do not understand this, 'ask''!

	A) Producing an image histogram

	Display the radar image Scn0406.img with the grey scale palette. The image is almost black.
	In the Composer menu selection box click "Properties" and then click "Image Histogram", or, in the Display menu click Histo, enter Scn0406 as the input file name , and then click OK. Examine the histogram. Can you now explain why the image is so dark?

	Wait for a verbal explanation of the meaning of the histogram image

	B) Producing a stretched image

	In the Display menu, click STRETCH, and enter Scn0406 as the input image file, and 0406sts as the name of the output image file. Click the 'Linear with saturation' button, set the saturation to 2.5% (rather than the default 5%), and then click OK.
	Display the image 0406sts. Click Properties in COMPOSER, then click the Image Histogram button.

	Create a stretched image for each of Scnband3.img, Scnband4.img, and Scnband5.img, naming them (in the image output box) bnd3sts.img, bnd4sts.img, bnd5sts.img, respectively. (bnd3sts=band3stretchedsaturated)

	C) Producing a density sliced image

	Display image bnd5sts.img with the IDRISI256 palette. It should be mostly green with some shades of red. Now redisplay the image using the IDRISI16 palette, and with the 'Autoscale image for display' button selected. The image that will be displayed will be a 'density sliced' image, with the full range of colours from 0 to 255 reduced to a range of 15 colours. Examine the IDRISI16 palette to understand how this is accomplished. Note how the mine tailings show up in black to blue colours.

	D) To create a colour composite TM image.

	The Red band (band 3) and band 5 are most likely to emphasize rocks relative to vegetation whereas band 4 will be more strongly reflected by vegetation than rocks. Band 5 will nevertheless also provide a fairly strong vegetation reflectance (Sabins, 1997, Remote Sensing, Fig 3-1). 

If band 5 is sent to the red gun, band 3 to the blue gun, and band 4 to the green gun, vegetation will therefore appear in shades of green -red (yellowish) whereas sandstones  should show in shades of salmon-pink. On the other hand, if band 5 is sent to the blue gun and band 3 to the red gun, vegetation will appear green-blue, and sandstone indigo blue.
	Create a colour composite image for the stretched TM images - in the Display menu click COMPOSIT, enter the 'Stretched' TM image names (bnd3sts for blue, bnd4sts for green and bnd5sts for red), and an output name of c3s4s5sl. Click the Simple Linear (NOT the Linear with saturation) button, and then OK. Display the composite image c3s4s5sl using the 'Colour Composite' palette.
Zoom (use the rectangle tool) the image from column 1350, row 1000 to column 1700, row 600.
	Use the Query tool to determine the values of the various dominant colours. Then open the Composite colour palette (DISPLAY -> Colour palette -> File -> Open -> Permanent  Library -> COMPOSIT) and display the colour corresponding to one of the values determined in the Query operation. Analyse  the relative proportion of red, green and blue in the colour displayed, and translate into relative proportions of the 3, 4, and 5 TM bands. Do the same for some of the other prominent colours in the composite image.
Repeat the operation to make an image c5s4s3sl.img.
Compare the two images; note the patches of intense red in c5s4s3sl.img and of intense blue in c3s4s5sl.img for the relatively recently formed smelter waste products and tailings ponds, and the muddy edges of some lakes. This indicates very strong reflectance of only band 3 (a high rock to vegetation index), and very little input from band 4 (no  vegetation) and band 5 (no vegetation and high water).

E) To create colour composite TM/RADAR and TM/Magnetic images.

	Create an image crs4s5sl.img composed of radar band 0406st as blue, bnd4sts as green and bnd5sts as red. Zoom (use the rectangle tool) the images from column 1350, row 1000 to column 1700, row 600.
	Create an image crs4s3sl composed of radar band 0406st as blue, bnd4sts as green and bnd3sts as red. Zoom (use the rectangle tool) the images from column 1350, row 1000 to column 1700, row 600, and compare this image with crs4s5sl. Image crs4s5sl has more yellow colour because of the strong vegetation reflectance of band 4 sent to the green gun is mixed with the vegetation reflectance in band 5 sent to the red gun (Red + Green = yellow!!). In contrast band 3 has very low reflectance values for vegetation, and the vegetation signal is therefore dominated by band 4 sent to the green gun. (Compare the histograms for bnd3sts and bnd5sts).

	Create an image cvg4s5l.img as a magnetic overlay on the TM data, with bnd4sts as green, bnd5sts as red, and Scnvg.img as blue.

	F) Carrying out an HIS transformation (fusion) on the radar, TM5, and vertical gradient data sets.

	Fetch the module COLSPACE via Analysis -> Image Processing -> Transformation -> COLSPACE.

	RADARSAT and TM images
	In the COLSPACE menu select HLS to RGB, and enter bnd4sts as 'Hue', 0406sts as 'Light', and bnd5sts as 'Sat', and give the names 'Red', 'Green', and 'Blue' as the output names.
	Create a colour composite image h5sra4sl.img from the 'Red', 'Green', and 'Blue' images and display the resulting image with the colour composite palette. The variation in intensity of bnd4sts will now show as a change in hue, the radarsat image as a change in intensity (black-white), and bnd5sts as a change in saturation of bnd4sts. Note the differentiation of topography, vegetation (red = highly vegetated) and rock (blue). 

	Vertical Magnetic Gradient and RADARSAT
	This time select SCNVG as 'hue' and 'sat', and 0406sts as 'light', and output to blue, green and red. Create a colour composite image hvrasvl.img and display the image. In this case positive magnetic anomalies will show up as saturated hues whereas weak anomalies will show as weak pastel shades more obviously showing the grey shades of the radarsat image.

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IMPORTANT: IF YOU HAVE COMPLETED THE EXERCISE, PLEASE DELETE THE 'YOURINITIALS'RSAT DIRECTORY FROM YOUR AREA.
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	APPENDIX - Remote Sensing Resource material

Radarsat Geology Handbook

1)	Comparison of Satellite Imaging Systems
		Comparison: Optical and radar Data
		The RADARSAT Satellite
2)	The RADARSAT Satellite
		Understanding Radar Imagery
		RADARSAT's State of the Art Features
		Unique Characteristics of SAR Data
3)	Visual Interpretation of RADARSAT Imagery
		Structural Interpretations
		Lithologic Interpretations
		Geologic Applications Guidelines
4)	Image Enhancement of RADASAT Data
		Hardcopy products
		Digital Products
5)	Value-added RADARSATProducts
		Radar Imagery Manipulation
		Data Integration

	Remote sensing by F.F. Sabins 

1)	Introduction to concepts and systems
		Units of measure; electromagnetic energy/spectrum; image characteristics; vision; remote sensing systems; spectral reflectance curves; multispectral imaging systems;
hyperspectral imaging systems; sources of information.
2)	Photographs from aircraft and satellites
3)	Landsat images
4)	Earth resource and environmental satellites
5)	Thermal Infrared images
6)	Radar Technology and terrain interraction
7)	Satellite radar systems and images
8)	Digital image processing
9)	Meteorologic, oceanographic and environmental applications
10)	Oil exploration
11)	Mineral exploration
12)	Land use and land cover: geogaphic information systems
13)	Natural hazards
14)	Comparing image types
	Basic geology for remote sensing

	Introductory Digital Image Processing - a Remote Sensing Perspective : Jensen.

	Chapter 1 Introduction to Digital Image Processing of Remotely Sensed Data
	Major Characteristics of Remote Sensing systems, Table 1-2; p. 13 Image Processing considerations, Fig. 1-3;.
	Chapter 2 Remote Sensing Data Acquisition Alternatives
	p. 26-37 Characteristics of Multispectral Remote Sensing systems, Table 2-2; 2-4; p. 40-42 Characteristics of Thematic Mapper Spectral Bands, Table 2-6; Fig. 2-24, 2-25
	p. 60-61 Digital Image Data Formats
	Chapter 3 Image Processing System Considerations
	Chapter 4 Initial Statistics Extraction
	Chapter 5 Initial Display alternative and Scientific Visualization
	Fig 5-8, 5-9; 5-11;
	p. 101 RGB to IHS Transformation and Back Again, Fig 5-14,15;
	Chapter 6 Image Preprocessing: radiometric and geometric correction
	Fig. 6-1, 6-3; 6-6,7; 
	Chapter 7 Image Enhancement
	p. 152 Band Ratioing
	Chapter 8 Thematic Information Extraction: Image Classification
	Chapter 9 Digital Change Detection
	Chapter 10 Geographic Information Systems
	p. 282 Data Structure; Vector Data Model; Raster Data Model; Quadtree Raster Data Model; DEM; TIN; (TOSCA).

Geographic Information Systems for Geoscientists - Bonham-Carter

Chapter 1
	p. 1-3. GIS is simply a computer system for managing spatial data. GIS have capabilities for data capture, input, manipulation, transformation, visualization, combination, query, analysis, modelling and output. The ultimate purpose is to provide support for making decisions based on spatial data.
	Use a GUI or a command language.
	Data is geocoded, which means it is geographically located.
	Different geocoded data sets are spatially registered, that is they overlap correctly.
	There may be a straight line constant ratio relationship between two elements, but the spatial distribution of the primary values may be nodal or random or linear contoured or patterned, etc. Or the ratio may be bimodal both graphically and spatially.
	p. 3 Visualization reveals spatial patterns.
	p. 4 Spatial query allows the asking of the questions :
	What are characteristics of this location?
	Whereabouts do these characteristics occur? (Where do gold and sulphur occur together?)
	p. 5 Combination merges spatial datasets, e.g. a geological map and a satellite image.
	p. 5 Analysis e.g. trend surface analysis.
	p. 6 Prediction e.g. what are the parameters defining sites of gold mineralization.
	p. 14-22 A Model GIS Study for Mineral Potential Mapping.
	p. 32-39 Raster and Vector Spatial Data Models
	p. 39-43 Attribute Data.
	p. 43-49 The Relational model.
	p. 52-68 Raster Structures.
	p. 68-81 Vector Data Structures (TOSCA)
	p. 87-90 Map Projections.
	p. 95-101 Digitizing.
	p. 103-108 Coordinate Conversion.
	p. 120-126 Colour, Colour lookup tables.
	p. 186-202 Map Reclassification.
	p. 204-210 Operations on Spatial Neighbourhoods.
	p. 267 271 Map Analysis, Types of Models.
	p. 272 Boolean Logic Models, Landfill Site Selection, Mineral Potential Evaluation.

	PCI - Using PCI Software, V. 1, 2

	1. User Interfaces
	2. Video Display: concepts and use
	3. Database Management
	4. Projections
	5. Importing data
	6. Preprocessing and Geometric correction
	7. Enhancements: contrast manipulation
	8. Enhancements: spatial filtering
	9. Enhancements: multi-image manipulations
	10. Image Classification
	11. Working with vectors
	12. Working with Attribute data
	13. Generating and Working with DEMs
	14. Orthorectification and DEM extraction
	15. Hyperspectral Analysis
	16. SAR (Synthetic Aperture) analysis
	17. Spatial Modelling: Raster GIS
	18. Data Presentation
	19. Exporting and Archiving Data
	App. A. DCP: Display Control Program
	App. B Glossary of Terms

	ER Mapper 5.0 Tutorial
	Table of contents
1. Introduction ot ERMAPPER -1
2. User interface basics - 11
3. Creating an algorithm -27
4. Working with data layers - 43
5. Viewing image data values - 59
6. Enhancing image contrast - 67
7. Using spacial filters - 89
8. Using formulas - 101
9. Geolinking images - 119
10. Writing images to disk - 137
11. Colourdraping images (Translucent layers) - 145
12. Mosaocking images - 159
13. Virtual datasets - 175
14. 3-D perspective viewing - 191
15. Thematic raster overlays - 201
16. Composing maps - 213
17. Unsupervised classification - 229
18. Supervised classificiation - 241
19. Raster to vector conversion - 259
A System setup - 269
B Reference texts - 271
Index - 273

	Procedures
Data import
	Raster data includes satellite and aerial images, DTM’s and geophysical data
	An ERM data set has two files: a binary BIL file and a header .ers file.
	Vector data is stored as lines, points and polygons; as an ASCII data file and an .erv ASCII header file.

Image display
	Display format is called colour mode, RGB or HSI
	Also involves the display of statistical information about the image, e.g. histograms.
	
Image registration/rectification
	Removal of geometric errors, alignment with real world projections, and the geometric alignment of two or more images.
	
Image mosaicking
	assembly of several adjacent images into a single image.

Image enhancement (processing)
	Image merging, e.g. TM and SPOT; colour draping, e.g. vegetation over gravity as z; contrast enhancement; filtering; formula processing (algebraic manipulation); classification.

Dynamic Links overlays
	Links to other file formats without need to convert files.

Annotation and map composition
	Add vector data by drawing directly on screen.

Data export and hardcopy printing

COLOR (INFOPEDIA) - White light is composed of electromagnetic vibrations, the wavelengths of which are evenly distributed from 35 to 75 millionths of an inch). If the intensity of these vibrations is strong, the light is white; if the intensity is less, the light is grey; and if the intensity is zero, the light is nonexistent or black. Light composed of vibrations of a single wavelength in the visible spectrum differs qualitatively from light of another wavelength. This qualitative difference is perceived subjectively as hue. Light with a wavelength of 0.000075 cm (0.000030 in) is perceived as red, and light of 0.000035 cm (0.000014 in) wavelength is perceived as violet. The quality of the intermediate wavelengths is perceived as blue, green, yellow, or orange, moving from the wavelength of violet to that of red. See Wave Motion . 
The color of light of a single wavelength or of a small band of wavelengths is known as a pure spectral color or hue. Such pure colors are said to be fully saturated and are seldom encountered outside the laboratory. An exception is the light of the sodium- vapor lamps used on some modern highways, which is almost fully saturated spectral yellow. The wide variety of colors seen every day are colors of lower saturation, that is, mixtures of light of various wavelengths. Hue and saturation are the two qualitative differences of physical colors. The quantitative difference is brilliance, the intensity or energy of the light. 
Primary Colors. 
The human eye does not function like a machine for spectral analysis, and the same color sensation can be produced by different physical stimuli. Thus a mixture of red and green light of the proper intensities appears exactly the same as spectral yellow, although it does not contain light of the wavelengths corresponding to yellow. Any color sensation can be duplicated by mixing varying quantities of red, blue, and green. These colors, therefore, are known as the additive primary colors. If light of these primary colors is added together in equal intensities, the sensation of white light is produced. A number of pairs of pure spectral colors called complementary colors also exist; if mixed additively, these will produce the same sensation as white light. Among these pairs are certain yellows and blues, greens and blues, reds and greens, and greens and violets. 
Most colors seen in ordinary experience are caused by the partial absorption of white light. The pigments that give color to most objects absorb certain wavelengths of white light and reflect or transmit others, producing the color sensation of the unabsorbed light. 
The colors that absorb light of the additive primary colors are called subtractive primary colors. They are red, which absorbs green; yellow, which absorbs blue; and blue, which absorbs red. Thus, if a green light is thrown on a red pigment, the eye will perceive black. These subtractive primary colors are also called the pigment primaries. They can be mixed together in varying amounts to match almost any hue. If all three are mixed in about equal amounts, they will produce black. An example of the mixing of subtractive primaries is in color photography (q.v.) and in the printing of colored pictures in magazines, where red, yellow, black, and blue inks are used successively to create natural color. Edwin Herbert Land, an American physicist and inventor of the Polaroid Land camera, demonstrated that color vision depends on a balance between the longer and shorter wavelengths of light. He photographed the same scene on two pieces of black-and-white film, one under red illumination, for long wavelengths, and one under green illumination, for short wavelengths. When both transparencies were projected on the same screen, with a red light in one projector and a green light in the other, a full-color reproduction appeared. The same phenomenon occurred when white light was used in one of the projectors. Reversing the colored lights in the projectors made the scene appear in complementary colors.