COLOR AND ITS USES by Russell A. Ambroziak, U.S. Geological Survey Color is part of our senses and some scientists do not consider the human sensory system a scientific instrument. Some claim that only instrument data are useful in research. What they mean is only instrument data are quantified measurements of reality and the mind is only useful for relative assessments of the world around us. A color image is a 5-dimensional graph -- nothing more -- nothing less, and that makes it a powerful tool for scientific analysis. If these scientists never drew a graph or looked at a color image then the subject would end and there would be little need for this presentation. We do, however, draw graphs and create color images which cannot be analyzed by any instrument except the human mind. We use color all the time, but since few of us have ever been introduced to color theory we tend not to think of it as a scientific discipline. If we are going to use color we should spend some time trying to understand it because the application of color theory can: 1. shorten the time necessary to produce high quality graphics and color images, 2. increase the information content of graphics and images by a. increasing perceptual resolution or b. increasing the dimensionality of the data display and 3. in some cases produce analytical results which cannot be achieved in any other way. WHAT IS COLOR? Every seeing person with color vision knows what color is but no one seems to be able to describe it. While artists understand and use color as one of the main tools of the trade most scientists dismiss color as an unnecessary subject area to understand. To the artist, color is a science but, to the scientist, color is an art. This makes the scientific study of color rather rare. Many scientists use color but, since few understand color science, the results are based more on luck than on design. The scientific application of color can produce better results in less time than the typical trial and error methods used daily by many scientists. Now that the PC on our desk can produce 16,777,216 colors we had better spend some time learning how to best utilize them. Color has many definitions and it is much more complex than the physical properties of the incoming electromagnetic radiation, their reflection, and absorption. Color is what we see. Color is 1 percent physical, 9 percent biological and 90 percent psychological. This may be an exaggeration but not much of one. Before getting into the terms and parts of color theory 1 lets take a minute to understand this point. Color is not a property of light or objects but rather it is the perception of animals. Different animals see color in very different ways. Color, as we know it, is only perceived by primates and it may have something to do with the search for fruit. Regardless of the reason for color vision it only exists in primates and is basically the same for all primates with "normal" color vision. I don't think we need definitions for color (since everybody knows what it is) but here are a few. Many of these definitions contain terms which may not be familiar but they will be discussed later. Color is that aspect of visual perception by which an observer may distinquish differences between two structure-free fields of the same size and shape, such as may be caused by differences in the spectral composition of the radiant energy converned in the observation. In this sense, the term color is sometimes referred to as "perceived color" to distinquish it from color in the sense of "psychophysical color".)Wyszecki and Stiles, 1982, p487) Color (in the psychophysical sense) is that characteristic of a visible radiant power by which an observer may distinguish differences between structure-free fields of view of the same size and shape, such as may be caused by differences in the spectral composition of the radiant energy concerned in the observation. Psychophysical color is specified by the tristimulus values of the radiation power (color stimulus) entering the eye. (Wyszecki and Stiles, 1982, p723) Color is the attribute of visual perception that can be described by color names : white, grey, black, yellow, orange, brown, red green, blue, purple, and so on, or combinations of such names. (Billmeyer and Saltzman, 1981, p 1986) Color is any of manifold phenomena of light (as red, brown, pink, gray, green, blue, white) or of visual sensation of perception that enables one to differentiate objects even though the objects may appear otherwise identical (as in size, form, or texture). (Webster's Third New International Dictionary) Color is a relative, conical coordinate system in Reimannian space which makes up three of the seven dimensions of primate perception related to vision and not dealing with location (x,y,z) nor time (t). (Ambroziak, 1988) If you didn't know what color was before reading these definitions you wouldn't have gained much by reading them. If you can see color you know what it is and if you can't see color you will never know. Color has something to do with seeing but not with location or time. Rather than spend a lot of effort discussing the definition its best to try to discuss its parts and leave the definition alone. Just as income does not equate to savings, the spectrum of light coming from an object does not equate to color. Savings 2 is related to income and spectra are related to color but they are not the same. The spectrum of light coming from an object is purely physical and quantifiable. The light enters the eye and activates biological sensors which loosely correspond to red, green and blue (RGB) which are also quantifiable. From this point on, the phenomenon of color is psychological and quantification is not possible. The light coming from an object is the product of the spectrum of the incoming light and the reflective properties of the object. The cones in the eye respond with three values for each spectrum received called "tristimulus values". These values are the sum of the products of the incoming spectrum and the wave length dependent observer functions for each of the three sensors in the eye. There are an infinite number of spectra which can produce identical tristimulus values. This produces the phenome- non of metamerism -- colors that appear identical even though their spectra are very different. This causes problems for color matching using different dyes or colorants. A color may be perfectly matched under one light source but appear very different when the light source changes. Artists realize this and try to paint under light from a north window as a standard. Paintings done under artificial light may change drastically when viewed under natural light or different artificial light and the effect of the work may be ruined. If this is not enough to confuse the issue of color, the optic nerve takes the output from all of the sensors and encodes parts of the information for transmission to the brain. At this point much of the absolute information is lost and relative information is enhanced. We also go from a three hue red-green-- blue (RGB) system to a four hue intensity-hue-saturation (IHS) system. The primary sensors in the eye are red, green and blue but the primary hues (those which do not appear to be the combination of other hues) are red, yellow, green and blue. At this juncture in the process, color at a point becomes more a function of the color of surrounding points than the color (tristimulus values) of the point itself. Colors are more quantifiable when they are viewed against a neutral, 18 percent grey, background then when they are mixed. I have seen trained observers mistake a shade of orange for green on an image which contained a considerable amount of red. The points that need to be understood to get some handle on the science of color are: 1. the three dimensions of color space are the conical dimensions of intensity, hue and saturation (IHS), 2. color space is Reimannian, not euclidian which makes distances in color space very difficult to estimate, 3. color space can usefully be described by use of a chromaticity diagram, 3 4. absolute quantification of color by the human visual system is not possible, and 5. color does not exist apart from its perception by an animal's nervous system (usually human). The "Desert Island" Experiment To understand the IHS coordinate system, a mental experiment called the "desert island" experiment has been described by Judd and Wyszecki (1975). The hypothesis is that an individual with normal color vision and no knowledge of color science is stranded on a desert island with nothing to do. The beaches of the island are covered with pebbles of all imaginable colors. To pass the time, the castaway decides to arrange the many different colored pebbles into orderly patterns. A first step might be to separate those which are white, grey or black from those which are red, orange, yellow, green, blue, etc. This would be a separation of the achromatic (black, grey or white) pebbles from the chromatic ones. The achromatic pebbles could then be arranged from black through increasing brightness of grey to white. Intensity, lightness, and value are all terms used to describe this characteristic of color. The next step might be to separate all of the chromatic pebbles into piles of similar hue. Hues are commonly defined as red, orange, yellow, green, blue, violet, and magenta. These piles could then be compared to the achromatic arrangement and further ordered by their intensities. Finally, our observer would notice that not all of the reds of the same intensity are the same color. Some of the bright red pebbles would be tomato red and others would be pink. Both pink and red are of the same hue and can be of the same intensity, but they are obviously not the same color. This quality is referred to as saturation, chroma or colorfulness. If a large number of pebbles are arranged in a large three-dimensional array so that the color differences between them are nearly constant, our observer would have constructed the Munsell color space (Wyszecki and Stiles, 1982). We can liken the Reimannian color space to the Euclidean cone. The three dimensions of color space are the intensity, hue and saturation. The conical-like shape arises from the way the three axes affect each other's resolution. Intensity, bounded on only one end at black, is at the point of the cone. The color black has only intensity, which is equal to zero, with no hue or saturation possible. The intensity axis extends in some direction toward infinity with a saturation of zero at all points on the axis. This axis of intensity, which is black and proceeds in the direction of grey, then white, is orthogonal to both saturation and hue. Saturation is measured by 4 the distance from the intensity axis to the edge of the cone, where saturation is equal to one. The geodesic distance covered by a saturation change from zero to one is a function of the hue and intensity. The greater the intensity, the greater the geodesic distance along hue to the edge of the cone. Hue, which is measured by the direction of the color from the intensity axis, can be represented by an angle. Hue is unbounded in the sense that there is no point where hue stops naturally. Rather, it is best represented as the cross-sectional circumference on the cone with the hues constantly fading into one another. The result can be thought of as a Euclidean, conical coordinate system in which there are ellipsoids of various size and orientation that give the size of a jpcd (just perceptible color difference) in any direction. Convenient labels on the hue axis are the colors red, yellow, green, cyan, blue and magenta marked at 60 degree intervals. The geodesic distances between these colors varies, but since there is no attempt to have distances represented by Euclidean distance on the diagram this is not a problem. The actual distances are alluded to by the ellipsoids. Continuing with the desert island experiment, if a color- blind scientist were now to arrive on the island with instruments which measured the amounts of red, green and blue light reflected by each pebble, he would be confused by our first observer's choice of arrangement. The scientist might become so confused that he would build his own scientific arrangement to show the first observer where she went wrong. The most logical arrangement for the color-blind scientist to use would be to chose one of the three primary colors, such as red, and form piles of pebbles which all reflected the same amount of red. These piles could be arranged in a sequence of increasing red reflectance. Each of these piles could be further arranged into a two-dimensional array of increasing blue in one direction and increasing green in a direction at right angles to the blue axis. The resultant arrays could then be stacked, and an alternate three dimensional color arrangement could be made. When the scientist's work was finished and our first observer is called to see the "correct" arrangement of colored pebbles, she would be just as confused as the color-blind scientist was when he saw her arrangement. There would be a pattern of some sort in the arrangement of the scientist's pebbles, but the pattern would not be recognized for what it is. If the scientist had red, green and blue filters through which our first observer could view the RGB color coordinate system, she would immediately see why he arranged them as he did. Through the three filters, the monochromatic intensities would appear as planes of equal intensity which are perpendicular to each of the three axes. As obvious as the arrangement appeared through the filters, the arrangement would still remain confusing without the filters. 5 People can see patterns of IHS naturally but require instruments or filters to see RGB patterns. If images are to be viewed by people, then it is the IHS patterns which will be seen and not the RGB patterns. More about this later. It is a key point. Color Space is Reimannian We measure distance in color space by just perceptible color differences (jpcd). These are computed from the results of color matching experiments by trained observers with "normal" color vision. A test color is displayed and the observer tries to match it by combining primary colors. The standard deviation of the errors is a consistent measure of distance in color space. It defines the distance one must move to be able to detect a difference one out of three times. When color is represented in Euclidean space, the color distance between two neighboring points in color space is given by (ds * ds) = (dU1 * dU1) + (dU2 * dU2) + (dU3 * dU3) where: s = distance between points which are just perceptibly different U = tristimulus values of the eye. An alternate form can be assumed which has different properties than ordinary Euclidian space (Wyszecki and Stiles, 1982) (ds * ds) = g11*(dU1*dU1) + (2*gl2*dU1*dU2) + (2*dU2*dU2) + + (2*g23*dU2*dU3) + g33*(dU3*dU3) + (2*g31*dU3*dU1) where: g = metric coefficients which are continuous functions of U so that ds > 0 and are derived from the variance/covariance matrix of color matching errors. The space in which ds is the distance element is known as three-dimensional Reimannian space. If it is possible to transform the tristimulus coordinates (U) into coordinates V so (ds * ds) = (dVl * dV1) + (dV2 * dV2) + (dV3 * dV3), that then Reimannan space is the same as Euclidian space. This reduction, however, is not usually possible,and in the case of color transformation, it certainly is not. The importance of this concept lies in its ability to describe geodesic lines between two points in color space. In color space, the path between two points which contains the fewest just perceptible color differences (jpcd) is the geodesic line. In Euclidean space, these are straight lines but, in the case of color space, they are generally curved which means color perception space is not Euclidean (Wyszecki and Stiles, 1982). 6 Although the perceptual color coordinate system (IHS) can be mapped into Euclidean color space (RGB), it cannot be done in such a way that distances between points are independent of location. In order to map IHS into Euclidean space so the relative distances are independent of location, six-dimensional Euclidean space is required. If fewer dimensions are used, gaps will appear in the space. Unfortunately, the concept of six- dimensional space is of little value to assist in visualization of the relative position of colors. A glance at table 1 will illustrate this point. A triangle can be defined by three colors such as yellow, green, and white. Now try to draw this triangle using the distances on table 1. The distance from yellow to green is 73.4 jpcd (just perceptible color distance) while the sum of the other two sides (yellow to white and white to green) is 26.7 + 40.7 = 67.4 which is shorter than yellow to green. --------------------------------------------------------------- KEY COLOR DISTANCES FOR CRT IMAGES --------------------------------------------------------------- IHS Variable CRT Colors* --------------------------- ---------------------------------- R-Y Y-G G-C C-B B-M M-R ---- ---- ---- ---- ---- ---- Changing Hue 83.0 73.4 31.0 73.1 130.6 59.2 (saturation maximum) R-W Y-W G-W C-W B-W M-W ---- ---- ---- ---- ---- ---- Changing Saturation 78.2 26.7 40.7 49.7 103.3 67.1 (hue constant) --------------------------------------------------------------- color x y color x y --------- ----- ----- --------- ----- ------ * R = red = 0.62 0.345 Y = yellow = 0.44 0.463 G = green = 0.26 0.58 C = cyan = 0.205 0.165 B = blue = 0.15 0.08 M = magenta = 0.385 0.213 W = white = 0.333 0.333 --------------------------------------------------------------- Table 1 --------------------------------------------------------------- The Chromaticity Diagram It is possible, however, to map two IHS dimensions into three RGB dimensions without distortion. This means that, if one of the IHS coordinates is held constant, the resulting two-dimen- sional sub-space can be mapped in three-dimensional RGB space. These diagrams are as difficult to construct as they are to use 7 and are, therefore, of limited value. Measurement of color differences must remain mathematical once the values of "g" are determined. Much of the description of color is done in Euclidean space, but one must always be aware that it is not an accurate description of color distances, just as a flat map of the world is not an accurate description of the distances between features on the surface of a planet. One representation of IHS space in RGB is the chromaticity diagram which holds intensity constant over the surface if intensity is considered to be the sum of the tristimulus values. The chromaticity diagram is flat and, therefore, cannot be a true representation of the color space. The major problem with this diagram is that it is in RGB space with no attempt made to make relative distances between colors in IHS space consistent. With all of its flaws, it is still a useful tool to understand the mapping of RGB into IHS, and it provides insight into the use of color in remote sensing. The chromaticity diagram uses chromaticity values to describe hue and saturation for a constant intensity. These chromaticity values are the tristimulus values divided by the sum of all three tristimulus values. x = Ul / (Ul + U2 + U3 ) y = U2 / (Ul + U2 + U3 ) z = U3 / (Ul + U2 + U3 ) Since the sum of the three chromaticity coordinates is unity, only two of them need be defined to specify the color. The intensity of the color is not specified by the chromaticity coordinates, and the magnitude of U2 is usually also specified in order to identify the actual colors on the diagram. The description of two-dimensional Reimannian color space for the chromaticity diagram has been attempted by many research- ers. The most commonly used is that of MacAdam (1942) as analyzed by Silberstein and MacAdam (1945). Observers were shown colors at various points on the chromaticity diagram and asked to match the color. The standard deviations of the errors of these color matches can be used to determine the coefficients of Reimannian space. g11 = 1 / [ (sx * sx) * (1 - r * r) ] gl2 = r / [ (sx * sx) * (sy * sy) * (1 - r * r) ] g22 = 1 / [ (sy * sy) * (1 - r * r) ] where: g = metric coefficients s = standard deviation r = correlation coefficient of x and y errors. 8 As with three-dimensional Reimannian space, distances are measured using metric coefficients. ds * ds = g11*(dx * dx) + g12*(dx * dy) + g22*(dy * dy) The MacAdam metric coefficients can be represented on the chromaticity diagram as ellipses of equal uncertainty of recog- nizing color differences. The distance across the ellipse in any direction is proportional to the distance one must move on the chromaticity diagram to obtain a just perceptible color differe- nce (jpcd). These calculations are performed for 25 different points on the chromaticity diagram and expressed as geometric constants of the ellipses (table 2). The major semiaxis is given by "a", the minor semiaxis by "b", and the angle between the x- axis and the major semiaxis by theta where theta is less than 180 degrees. ---------------------------------------------------------------- MACADAM ELLIPSES OBSERVED AND REGRESSION APPROXIMATION ---------------------------------------------------------------- Color Cente Observed Regression ------------------- --------------------- ------------------- N x y 1000a 1000b theta 1000a 1000b theta --- --------------- ------- ------- ----- ------ ------ ----- 1 0.160 0.057 0.85 0.35 62.5 0.82 0.35 62.7 2 0.187 0.118 2.2 0.55 77.0 1.94 0.50 76.0 3 0.253 0.125 2.5 0.50 55.5 2.93 0.56 55.7 4 0.150 0.680 9.6 2.3 105.0 9.58 2.30 104.8 5 0.131 0.521 4.7 2.0 112.5 4.70 2.03 114.1 6 0.212 0.550 5.8 2.3 100.0 5.97 2.28 99.6 7 0.258 0.450 5.0 2.0 92.0 4.73 1.95 88.4 8 0.152 0.365 3.8 1.9 110.0 3.80 1.85 107.4 9 0.280 0.385 4.0 1.5 75.0 3.87 1.55 77.3 10 0.380 0.498 4.4 1.2 70.0 4.39 1.26 72.8 11 0.160 0.200 2.1 0.95 104.0 2.34 1.03 105.1 12 0.228 0.250 3.1 0.90 72.0 2.63 0.79 73.9 13 0.305 0.323 2.3 0.90 58.0 3.01 1.11 64.9 14 0.385 0.393 3.8 1.6 65.5 3.67 1.48 65.8 15 0.472 0.399 3.2 1.4 51.0 3.25 1.31 43.5 16 0.527 0.350 2.6 1.3 20.0 2.74 1.36 24.0 17 0.475 0.300 2.9 1.1 28.5 2.55 1.23 26.6 18 0.510 0.236 2.4 1.2 29.5 2.64 1.15 26.6 19 0.596 0.283 2.6 1.3 13.0 2.52 1.28 12.4 20 0.344 0.284 2.3 0.90 60.0 2.51 0.89 53.9 21 0.390 0.237 2.5 1.0 47.0 2.32 0.82 42.5 22 0.441 0.198 2.8 0.95 34.5 2.85 1.03 35.8 23 0.278 0.223 2.4 0.55 57.5 2.37 0.56 58.5 24 0.300 0.163 2.9 0.60 54.0 2.76 0.58 50.5 25 0.365 0.153 3.6 0.95 40.0 3.43 0.95 43.7 ---------------------------------------------------------------- Table 2 ---------------------------------------------------------------- 9 Qualitative vs. Quantitative One strength of the human mind is that it can process qualitative data. The human mind cannot quantify anything though vision which makes it useless for quantification of the information contained in images, but for the analysis of qualitative information it is unequaled. The assumption often is that the mind can quantify, but it cannot, and confusion results. The mind can only see relatives, and any attempt to make the mind quantify will be defeated. This is the basis for many optical illusions. Qualitative perception means that the appearance of a single pixel is more a function of the pixels surrounding it than the quality of the pixel itself. The human mind, in the case of color, does not even receive quantitative data to analyze. The eye encodes color information in such a way as to enhance differences between adjacent colors at the expense of the measured magnitude of either color. The perceived color of a pixel is more a function of the color of the pixels surrounding it than it is a function of its own quantitative color as measured by instrument. The cones of the eye which sense color appear to transmit signals to the brain which are a function of the brightness of the light striking them as well as of the light striking neighboring cones. Although the functional relationship s not completely understood, the form of the function can be approximated by describing the limiting cases. The purely quantitative case would be for each cone to transmit a fixed frequency pulse for each retinal illuminance value. The totally qualitative case would be for a cone always to transmit the same frequency if surrounding cones where exposed to identical retinal illuminance. The true response lies somewhere between these two extremes but experiments indicate that the true function is closer to the purely qualitative case than to the quantitative case. A hypothetical function for qualitative vision is postulated here and tested by visual inspection of the results. Two identical discs are divided in half by a line through the center and colored white on one side and black on the other. When they are spun rapidly they will both appear uniformly grey because the eye, with its limited response time, will integrate over the entire diameter of the disk. If the angle of the black is increased from 180 degrees to say 190 degrees for the inner 1/2 of the disk, the center will appear uniformly darker than the outer portion of the disk when spun because there is now more black in the inner portion of the disk. The hypothetical function is now applied to the disk with two different shades of grey and a second disk is made using this function, it is then spun rapidly and compared to the first disk. 10 The hypothetical function is: X+r Y+r I*(X,Y) = 100N / [ {I(x,y)/[((x-X)*(x-X)) x=X-r y=Y-r + ((y-Y)*(y-Y))]}] (1) excluding the point x = X and y = Y X+r Y+r where: N = I(X,Y) {1/[((x-X)*(x-X)) x=X-r y=Y-r + ((y-Y)*(y-Y))]} I*(X,Y) = ganglion cell output signal response I(x,y) = retinal illuminance X,Y = retinal coordinates of the cone directly over the ganglion cell being estimated x,y = retinal coordinates of the cones surrounding the cell being estimated r -- defined below Equation (1) modifies the ganglion cell output resulting from the stimulus of a single cone by considering all cones in a square of dimensions 2r x 2r centered around the cone in question. This function was created here to illustrate a point and is not meant to explain the true working of the eye. The result, if normalized in such a way that all cones in the square have the same retinal illuminance, is 100. This is totally qualitative since the resultant magnitude of "I*" is dependent only on the relative magnitudes of "I" surrounding the cone and is totally independent of the absolute magnitude of "I" of the cone being estimated. The magnitude of "I*" is reduced if surrounding cones have a higher magnitude of "I", and the reduction, or suppression, is proportional to the illuminance and to the inverse of the square of the distance to the suppressing cone. Examinations of the retina in primates show the existence of nerve cells which appear to connect cones horizontally (Wyszecki, 1982). Although the true function of these cells in not known, it is certainly possible that they do act in a manner similar to that described by equation (1). If the eye is a quantitative instrument, then each of the two disks would appear to be colored differently and would appear as they are. Disk 1 would appear to contain two uniformly grey sections, one slightly darker than the other, and disk 2 would appear to be the same color most of the area with some shading near the mid-point of a radius line. When the disks are stationary this is exactly what is seen. While spinning, the 11 shading of disk 2 is invisible and both halves look uniform and of different brightness. --------------------------------------------------------------- HYPOTHETICAL GANGLION CELL OUTPUT RESPONSE --------------------------------------------------------------- X I(x,y) I*(X,Y) I**(X,Y) Retinal I = I(x,y) I = I*(X,Y) Illuminance (r = 5) (r = 5) -------- ------------- -------------- --------------- Column 1 Column 2 Column 3 -------- ------------- -------------- --------------- I 90 100.0 100.0 * * * * 20 90 100.0 100.0 21 90 100.0 100.0 22 90 100.0 100.1 23 90 100.0 100.3 24 90 100.0 100.7 25 90 99.5 100.4 26 90 98.7 100.0 27 90 97.6 99.4 28 90 95.8 97.9 29 90 91.9 92.7 30 110 107.7 107.2 31 110 103.7 101.9 32 110 102.0 100.5 33 110 101.1 100.0 34 110 100.4 99.7 35 110 100.0 99.4 36 110 100.0 99.8 37 110 100.0 99.9 38 110 100.0 100.0 39 110 100.0 100.0 * * * * 59 110 100.0 100.0 --------------------------------------------------------------- Table 3 --------------------------------------------------------------- The results of this experiment indicate that the retina of the eye is more of a qualitative than quantitative instrument. When coupled with the action of the iris, which adjusts to maintain constant scene retinal illuminance, the eye must be thought of as totally qualitative. If the signal transmitted from the eye to the brain is qualitative and if it must be decoded by the brain to produce what is called vision, then vision must also be qualitative. If the mind never receives the quantitative information, there is no way for it to analyze quantitative information. 12 The preceding experiment used grey light to show the qualitative nature of the eye. In grey light, the relative intensities of each of the three primary colors are held constant and are nearly equal. If the cones respond in a particular way to light when all three types of cones are stimulated identically, one might postulate that they will react in the same manner when stimulated individually. To test this hypothesis, the experiment was repeated changing only one primary color along the x-axis of the two disks. The results of the color test of the qualitative nature of the eye are identical to the intensity test except it is hue, rather than intensity, which appears to change between the two halves of the disks. For the color test, each of the three primary colors is treated differently. Blue is set to zero and green is set to 100 everywhere. Red is varied in the two disks in the same way grey was in the first experiment. The result is again to similar disks when spinning. This experiment proves that, regardless of how the eye-mind system actually sees color, the color of a point on an image is as much, or more, a function of the color of surrounding points as it is a function of the color of that particular point. Colors on an image are qualitative and any attempt to make them appear quantitative will likely be defeated by the eye. Is There Color If No One Sees It? The perception of color is a complex mix of physics, physiology and psychology consisting of (1) a light source, (2) a reflecting surface, (3) the eye, (4) the optic nerve, and (5) the mind. Each of these five parts influences what is called color and the way it is perceived. For satellite image analysis, there are at least two light sources -- the sun which illuminates the original scene and the light which illuminates the image. It is the image which is of interest to the image analyst, but its connection to reality must be kept in mind at all times. In the case of soft copy images (images on a CRT), there is no reflection from an image. The phosphors on the color monitor give off visible light directly. Regardless of the source of the light, color vision begins in the eye where three types of cone receptors respond to light of various wavelengths in different ways. For light with a wave length of 380 to 770 nm, two or more of these cones are sensitive at each wave length over most of the visible range (Wyszecki and Stiles, 1982). The sensitivity of each type of cone at each wave length is known as the observer functions, x, y, and z for red, green, and blue, respectively. The product of the spectral power distribution of the incoming light and the observer functions integrated over the visible range of the spectrum result in three numbers, called tristimulus values, which describe the response of the eye to a particular color. These values define the color as seen by the eye and form the basis for color space definitions (Billmeyer and Saltzman, 1981). 13 What the retina receives is encoded by the optic nerve for transmission to the brain. At this point relative measures are enhanced at the expense of absolutes and we go from three to four primary hues. The processing n the brain is complex and poorly understood but some things are known. The brain does not process all of the incoming information, but rather selects information according to its needs and experience with the type of scene being analyzed. When you say you don't notice things that surround you every day its because the brain does not process them. The brain is looking for changes and unusually shapes in the scene and color makes up 3 of 5 dimensions we work with. Studies of the activity of cells in the brains of monkeys while they were trying to correctly identify the shapes of red or green symbols indicates that only the color of interest was being processed by the brain. If the green symbol was the one of interest the brain actually turned off the red signal to reduce the amount of data that needed to be analyzed. This is why we can so quickly spot objects of a given color in a scene of many colors. In this process of analysis all objects seem to maintain their color. We regard objects as possessing color even though much of the variation in tristimulus values comes from the changing light source. This is because it is the object we must identify to survive and the brain adjusts the incoming stimulus to preserve the color of the objects in the scene. White paper always look white regardless of the light source. Each type of animal sees color in a very different way according to the needs of the species. Color may be related to the incoming light but it is not color until it is processed by a brain. This is part of the reason that it is so difficult to define color; color is what you see color to be. SOME USEFUL DEFINITIONS Color that aspect of visual perception by which an observer may distinguish differences between two structure-free fields of view of the same size and shape, such as may be caused by differences in the spectral composition of the radiant energy concerned in the observation. In this sense, the term color is sometimes referred to as "perceived color" to distinguish it from color in the sense of "psychophysical color". (Wyszecki and Stiles, 1982, p487) (in the psychophysical sense) that characteristic of a visible radiant power by which an observer may distinguish differences between two structure-free fields of view of the same size and shape, such as may be caused by differences in the spectral 14 composition of the radiant energy concerned in the observation. Psychophysical color is specified by the tristimulus values of the radiation power (color stimulus) entering the eye. (Wyszecki and Stiles, 1982, p723) the attribute of visual perception that can be described by color names: white, grey black, yellow, orange, brown, red, green, blue, purple, and so on, or combinations of such names. (Billmeyer and Saltzman, 1981, p186) any of manifold phenomena of light (as red, brown, pick, grey, green, blue, white) or of visual sensation or perception that enables one to differentiate objects even though the objects may appear otherwise identical (as in size, form, or texture). (Webster's Third New International Dictionary). a relative, conical coordinate system in Reimannian space which makes up three of the seven dimensions of primate perception related to vision and not dealing with location (x,y,z) nor time. (Ambroziak, 1988) hue the attribute of color perception denoted by blue, green, yellow, red, purple, and so on. (Wyszecki and Stiles, 1982, p487) the property of color most like the colors of the spectrum or that property of color which does not involve the apparent addition nor subtraction of white or black. (Ambroziak, 1988) intensity the attribute of a visual sensation according to which a given visual stimulus appears to be more or less bright; or, according to which the area in which the visual stimulus is presented appears to emit more or less light. (Wyszecki and Stiles, 1982, p487) The attribute of color which appears to be reduced by the addition of black. (Ambroziak, 1988) observer functions the sensitivity as a function of wavelength of each of the three color sensors in the eye (Ambroziak, 1988) metamerism color stimuli with the same tristimulus values but different spectral radiant power distributions. (Wyszecki and Stile, 1982, p184) 15 colors which look the same to the eye but different to instruments. (Ambroziak, 1988) saturation the attribute of visual sensation which permits a judgement to be made of the degree to which a chromatic stimulus differs from an achromatic stimulus regardless of their brightness. (Wyszecki and Stiles, 1982, p487) the attribute of color which appears to be reduced by the addition of white. (Ambroziak, 1988) tristimulus values the amount of the three primary color stimuli required to give by additive mixture a color match with the color stimulus considered. (Wyszecki and Stiles, 1982, p723) the relative amount of red, green and blue seen by the human eye. (Ambroziac, 1988). unique (or primary) hues hues that cannot be further described by the use of the names of the hue names other than its own (also referred to as unitary hue). There are four unique hues, each of which shows no perceptual similarity to any of the others; they are: red, green, yellow, and blue. The hueness of a light (color stimulus) can be described as a combination of two unique hues; for example, orange is yellowish-red or reddish yellow. (Wyszecki and Stiles, 1982, p487) hues which do appear to be combinations of other hues. (Ambroziak, 1988) REFERENCE Ambroziak, Russell A., 1986: Real-Time Crop Assessment Using Color Theory and Satellite Data, University of Delaware, Ph.D. Dissertation, 205 pp. Billmeyer, Fred W. and Max Saltzman, 1981: Principles of Color Technology, New York: John Wiley and Sons, 240 pp. Judd, Deane B. and G. Wyszecki, 1975: Color in Business, Science and Industry, New York: John Wiley and Sons. Wyszecki, Gunter and W. S. Stiles, 1982: Color Science: Concepts and Methods, Quantitative Data and Formulae, New York: John Wiley and Sons, 950 pp. 16 HOW DO YOU USE COLOR? A simple answer is that you use color like any other graph. Color can be thought of as another way to graph information and what is true for graphs is true for color images. The main use of a graph is to see shapes and relationships not to measure or quantify. The equations or data used to create the graph contain the quantitative information but the graph is created for the mind to do qualitative analysis of shapes and forms. Color can do the same thing and give a 5-dimensional graph. If color is so important, why doesn't everybody learn about it and use it? A partial answer to this question is the lack of available control over a color system by scientists for analytical work. To do reasonable analytical color work you need a minimum of 256 colors taken from 16,777,216 palette. Until a few years ago, this was only possible with equipment costing hundreds of thousands of dollars, and the use of these systems required a good working knowledge of color theory. For the most part, color use came from photography, and digital imagery applications of color were based on photographic presentations such as false color-IR or on pseudo-color. Color control was in the hands of the print, paint, and fine art community and color scientists supported these industries. Hard copy of color is still extremely difficult to produce for the physical scientist but the new color boards for PC's have opened up a new dimension for analytical work using color as a tool is soft copy for a few hundred dollars. There are two basic ways of using color which come under a variety of headings, but they boil down to the display of results of analysis and the analysis of data. The use of color for the display of data includes color tagging, color coding, color slicing and pseudo-color. The use of color for analysis includes false color, the ACCS (Ambroziak Color Coordinate System), some applications of IHS and RGB to raw data, and almost all photographic applications. The most common application of color by non-color scientists has been and continues to be the first, that is, display rather than analysis. Almost all color analysis by non-color scientists has been based on photographic products. The Use of Color for Display On any image or graph, you have 2-dimensions to work with (x and y), but adding color you can tag points and add a third dimension. In order for this to work well, the colors must be different enough from each other to be uniquely identified. Another way to say this is that their distances in color space must be maximized. A list of such color might be: 17 --------------------------------------------------------------- REASONABLE COLORS FOR USE IN TAGGING --------------------------------------------------------------- # color intensity hue saturation ------ ------------- ------------- ----------- ------------ 1 red max red max 2 green max green max 3 blue max blue max 4 yellow max yellow max 5 magenta max blue-red max 6 cyan max blue-green max 7 black zero none none 8 orange max red-yellow max 9 white max none zero 10 grey middle none zero 11 brown middle yellow max 12 forest green middle green max 13 pink max red middle 14 rose middle red middle 15 navy blue middle blue max ---------------------------------------------------------------- Table 4 ---------------------------------------------------------------- A quick glance at these colors gives one the feeling that they are reasonable choices for a color display. These colors vary with the medium used to produce them, e.g., a flat bed plotter does not have the range of color that a CRT can produce. The problem arises when you try to add another color. It can be done but it may be too close to one of the above colors to be distinguishable everywhere on the graphic. Ten of the 15 colors have maximum saturation and three have zero or no saturation. Only two have any gradation of saturation and both of these are red. This might be a place to add a color, so lets try it. Yellow looks so much like white already that mixing the two to create another color is out of the question. Cyan is only slightly better than yellow, in the real world, and no better on a CRT, so half saturated cyan is out, also. Green is a little better but not enough to do much good. Blue, magenta, and orange all have good resolution in the direction of saturation but present other problems. Orange and magenta tend to become confused with red when saturation is dropped and unsaturated blue is almost undistinguishable from cyan on most systems. The point of all of this is to say that the order of magnitude of the number of colors that you can use to tagging is 15 -- seven if you are using a plotter and maybe 20 if you have a CRT, but at 20 colors, you're pushing with any display medium. If you can display 15 values at any point, you have a color display system which can give an x and y positional value and tag the point with one of about 15 values. Which is a two dimensional histogram of sorts with 15 bins each labeled by a readily distinguishable color. This a common type of system for 18 display, since you can get all of this on a CRT with 4 bits of data. It is not the small number of colors which presents the major problem for application of this system to analytical problems but it is the lack of direction or order to the colors. Once the colors are picked, any random order is about the same as any other order. If you want to display pressure in psuedocolor using the above scale you can divide the range of data into 15 chunks and color the map with ease. The problem is that higher (or lower) values do not seem to move in any logical direction. Is white above or below blue? If you have developed a climate classification system and wish to analyze the geographic extent of 15 categories of climate psuedocolor is an ideal choice for displaying your results. You would probably want to vary the color choices, but the basic idea would be the same -- no direction, just tags. This application can be used for any number of problems, such as, separating various lines on a graph, detecting slight density changes in monochromatic images, or mapping any type of non-sequential categories. The Use of Color for Analysis The main reason for displaying data in image form for analysis is to use the minds ability to detect non-geometric patterns and textures. We, humans, are very good at this sort of thing and computers tend to be less than inept at spotting and identifying fractals. We can easily identify all kinds of patterns on images at a glance, such as clouds, river valleys, man-made structures, etc. Not only can we identify them, but we can see more than the image contains. Visual perception is the result of what you see times your experiences. If you have no idea what your looking at, you will see very little, but it you know a lot about the situation, you will see much more. A weather forecaster and a non scientist looking at the GOES image on the evening news see very different images and get different information from it. If you're going to do analysis you want to display as much data as possible, and display it in the clearest manner for the mind to interpret. You want magnitude and direction to be preserved and you want the color axes to be orthogonal. To reduce the amount of trial and error involved in creating an image, it helps to understand something about the way the mind interprets color images and then start with a suitable IHS coordinate system. Color has long been used for analysis by photo interpreters using color or false color film. These products produce images in which all of the original data are displayed and direction is preserved. In any channel, brighter objects appear brighter. As stated before, the human mind processes IHS 19 data while photos are RGB data so we must convert the RGB space to IHS space in order to evaluate what the observer is observing. RGB images usually equate color intensity with the intensity of brightest of the three RGB values. Hue and saturation display the ratios of the three RGB values. If two channels are highly correlated as is the case in false color-IR image their ratio with the third channel will appear as saturation. AN IHS APPLICATION CASE STUDY The False Color IR Image The false color infrared (IR) image, commonly used for monitoring vegetation, is created by the computer as RGB images, but for crop monitoring they are IHS images. Fortuitously, the IHS coordinate system in the RGB color space of the false color IR image is aligned with the most commonly used vegetation index. Color IR film was created to detect foliage camouflage. Military equipment can be painted green, but it cannot be painted to look like foliage on color IR photos. Plants reflect much more near IR radiation than visible, and all paints reflect nearly equal amounts of both visible and near IR. To make the film, all three primary colors are shifted to longer wave lengths. Blue light is dropped, green light is recorded as blue, red light is recorded as green, and the near IR is recorded as red by the film. The result is an image which displays vegetation as bright red, water as black, dark blue or cyan and most other objects as grey or white. The false color IR image is an RGB image which displays each of three sensor magnitudes as a primary color. In the case of satellite images, these sensor magnitudes are the incoming visible and near IR radiation. For Landsat channels 4, 5, and 7 become blue, green, and red respectively. For NOAA AVHRR, there is only one visible channel, so the blue and green are both used to display channel I while red alone displays channel II. The resulting images are similar because NOAA AVHRR channel I has the same spectral range as Landsat channels 4 and 5 combined and natural scenes contain little crop status information within the green-red portion of the spectrum. Most of the information is contained between the visible and the near IR. Channels I and II of the AVHRR are ideal for monitoring the health and status of vegetation because healthy plants absorb visible light as a good source and reflect almost all near IR radiation. Turgid plant cells (swelled by internal fluid pressure) provide air-water interfaces which are highly reflective to near IR radiation. As soon as the internal pressure of the cells decreases, this high reflectivity falls rapidly. The loss of near IR reflectance can be detected before the plant shows any physical sings of wilting. After a period of 20 days, the water stress may effect the chlorophyll and visible reflectance will increase. Reflectance varies considerably between different types of plants and with their phenological stages, yet, when a full cover of healthy plants is viewed through a clear atmosphere, there can be no doubt about the target's identity. Obviously, the analysis of this index is a valuable tool for the crop monitor and its relationship to false color IR images provides insight into the initial success of research programs designed to monitor vegetation from space and the operational success of the current assessment programs. The false color IR image has been used in crop monitoring without question for at least the past decade. It is the standard method of three channel image display; it works and it is used. The false color IR image is actually an IHS presentation of the Normalized Vegetation Index (NVI), and this is the reason for its success as a crop monitoring tool. The sign of the NVI is given by hue. All positive values are red and all negative values are cyan on both Landsat and AVHRR false color IR images. The value of the index is given by saturation. The diagonal, which has an index value of zero, has a saturation of zero and the saturation increases to one towards both axes. Scene brightness is portrayed as intensity in a natural manner. It is the saturation of the hues red and cyan which contain the most valuable information in the false color IR image. If the saturation of any pixel is compared to the absolute value of its normalized vegetation index they will be found closely related. The calculation of hue and saturation is complex on a normal chromaticity diagram but when an image is formed by three pigments or phosphors the problem is simplified somewhat. The triangle formed on the chromaticity diagram by the three primary colors contains all possible colors. If the edges of this triangle, which represent the highest color saturation possible, are defined as having an image saturation of one, the problem becomes even simpler. In the case of red, this is good approximation because the red phosphor has an actual chromaticity saturation of 0.905, but the cyan on a CRT has a saturation of only 0.414. This is not a serious problem because most of the useful information on a false color IR image is in the red. The cyan hues tend to be very dark when they increase in saturation because it is only water which has index near minus one. The image saturation and the absolute value of the normalized vegetation index are equal at zero and one. Between zero and one, the difference is never more that 0.10. The normalized vegetation image is displayed as a smooth change in saturation which is nearly the same as the vegetation index. Although the false color IR image is an RGB image formed from the direct assignment of three channel data to the intensity of the blue, green, and red phosphors on a CRT, it fortuitously is an IHS display of the normalized vegetation index. This is 21 the reason that so much information about agriculture could be seen in the early Landsat images. It is also the reason that image interpretation of these false color images, whether from Landsat or NOAA AVHRR, are the prime information source in all operational systems. If it is the human mind which is to do the analysis of the satellite image then the question must be asked "Is false color IR the best type of image to analyze for satellite analysis of large scale agriculture?" The false color IR image displays all of the information about plant vigor in terms of saturation changes of red (remember, cyan is for water). The path of the color on the chromaticity diagram is a straight line from white to red phosphor, which has a length of 78.2 jpcd (table 1). This means that a maximum of 78 different colors can be identified, under ideal conditions, between the best vegetation and no vegetation at all. Another favorite color scheme is to use green for near IR and magenta (blue and red) for visible to make vegetation green rather than red. The distance from green to white along the chromaticity diagram straight line is only 26.7 jpcd or about one-third the color resolution as false color IR. Although this display provides more natural colors, it is rarely used for analysis because of the poor color resolution it provides for analyzing patterns of the normalized vegetation index. The best image of the false color family would be one in which the near IR (AVHRR channel II) is assigned to blue, and yellow (red and green) assigned to the visible. The distance from blue to white along the chromaticity straight line is 103.3 jpcd and, therefore, has 32 percent more visual color resolution than the more common false color IR with red on the near IR channel. The colors of this image are not pleasing and the improvement is not appreciated by analysts who see this type of image as strange and not worth getting used to. SOLUTION TO THE PROBLEM Image Color Coordinate System -- ACCS Although the false color IR image has been enormously useful in vegetation analysis from space, it is obvious from the discussion of color theory and of perceptual color resolution that it can be improved. Any improvements must keep the favorable points of the color IR coordinate system while improving its weaknesses. Strengths of the false color IR image The strong points of the color IR images are (1) its ability to display all of the spectral information of interest for analysis, (2) its display of the vegetation index on an axis 22 of the minds natural coordinate system, (3) the ability to provide the information which allows the distinction between lands, water, and surface features, and (4) the relatively low cost of image processing. These are the positive attributes of the color coordinate system which have made the color IR images an integral part of all operational assessment programs and which can afford the expense of satellite data and satellite data processing. The loss of one of these factors would certainly have an adverse effect on the value of the product. The display of all of the data of interest is important to the analyst for several reasons. Real time crop monitoring is, to a large extent, an intelligence function. The data needed are never complete and the quality of all of the data is usually suspect to some degree. The key to a successful operation is the ability to take all available, pertinent information and combine it to produce the best possible answer in the time allotted. If parts of the data are removed from the system, it is quite possible for errors of judgement to be made. If, for example, only the vegetation index values are available, a region with partial cloud cover may be mistaken for a loss of vegetation vigor. If all of the spectral information is available, the brightness and the patterns of brightness will allow the true situation to be determined. The display of the vegetation index on the saturation axis of the IHS color coordinate system allows the human mind to see crop vigor as an independent feature of the false color IR image. Relative crop vigor is then unchanged by changes in brightness caused by atmospheric interference or surface phenomenon. Redder pixels are better pixels regardless of the situation. A thin cirrus cloud can partially cover a portion of a crop region making the healthy red field appear pink, but within the cloud covered region the better fields will still appear redder (more saturated). The human mind will be annoyed by the presence of the cloud but not confused, and the analysis of the health of the fields will be done correctly. The way the information is displayed in a false color, IR images makes the identification of surface features, clouds, and water possible. The pattern recognition capabilities of the mind are at their best when viewing regions with partial cloud cover of different cloud types. The amorphous patterns caused by various cloud types and cloud systems are easily recognized by the mind and naturally sorted out as not part of the surface scene. The patterns of fields, terrain, and water bodies, which are also amorphous, are very different and easily recognized. In the false color IR image, it is quite possible to do this type of pattern recognition rapidly and effortlessly, and with very little training. The production of the image itself requires no image processing other than the decoding of the input data and the necessary hardware to produce a color image. Each pixel is assigned colors which are based only on the incoming sensor data 23 for that pixel. This type of image is the least expensive image to process because no time consuming, complicated mathematics are necessary to produce it. Shortcomings of the false color IR image The shortcomings of the false color IR image are (1) the use of three color axes to display two dimensional data, and (2) a lack of perceptual color resolution. When the success of the image in crop monitoring is considered, these shortcomings may not be serious, but they do provide the possibility to improve image quality, and therefore, the value of the image. In the false color IR image, the scene brightness is displayed as intensity, the absolute value of the vegetation index is displayed as saturation, and hue is used to display the sign of the data: red for positive, and cyan for negative. There is no color axis available to display such important information as thermal IR. If thermal IR is substituted for either the blue or the green axis of the RGB color coordinate system, the status of the vegetation is no longer displayed in the minds' natural coordinate system and one of the main positive attributes of the image is lost. The result is a confusing image of little value for crop monitoring analysis. The choice of saturation for display of the vegetation index is fortuitus in that it did produce an image which allowed analysis to be performed. It is unfortunate in that it may not be the best image which can be designed and it is not the industry standard. The image was not designed for the purpose for which it is being used and the task of designing a better one will certainly involve changing the axes of the color coordinate system. Of the three axes of IHS, the axis of saturation has the poorest resolution. The chromaticity diagram compares saturation and hue, and it is obvious that hue is the better of the two axes. The ACCS (Ambroziac Color Coordinate System) The maximum color resolution possible for saturation depends on the hue. Blue is the best, with 103.0 jpcd between blue and white, while yellow is the worst, being only 26.7 jpcd distant from white. The greater the path length in color space, the greater the perceptual resolution along the path. Hue would be a better choice because resolutions are better between hues and the length on the line is unbounded. The shape of the IHS axis is difficult to discuss in the context of distance comparisons because it is 6-dimensional and the distance calculations in this 6-dimensional space are not worth the effort unless the intensity axis of IHS is being considered to display the vegetation index as a replacement for saturation axis used in false color IR. Intensity is certainly a 24 possibility for display of the index but the transformed image is difficult to learn to use. People are comfortable with intensity showing scene brightness even if it is brightness of an invisible part of the spectrum. Bright colors representing high reflectances requires no learning. In the conical coordinate system, the false color IR image color coordinate system is the plane containing red, white, cyan, and black. Infrared reflectance is plotted along the line black to red and visible is plotted from black to cyan. The path followed in moving from red to cyan is not a geodesic path but rather it is the path specified by the color changes on the CRT. These paths are straight lines in Euclidean space and the distances along some of the key paths are given in table 1. The total color distance from red to cyan, the axis of the vegetation index, along the path followed by the CRT is 78.2 jpcd (red to white) plus 49.7 jpcd (white to cyan) for a total for 127.9 jpcd. There are an infinite number of longer paths which could be used to increase perceptual visual resolution, but care must be taken that the increase in resolution is not offset by a loss in some other attribute of the false color IR system. One way to increase perceptual resolution is to choose 128 colors which are as far apart as possible and order them to maximize the differences between adjacent colors. The gain in resolution would not be as great as the loss in information due to the removal of false color IR's second attribute -- display of the vegetation index on an axis of the minds natural coordinate system. If more red is more biomass, the mind can see increasing biomass under a wide variety of adverse conditions, but if the order of the colors is not natural to the mind, it will lose all analytical ability, for if relative change is not displayed then relative analysis is not possible. If hue is used in place of saturation, the possible color length of the vegetation index axis is increased from 127.9 to 450.3 jpcd, which is a 3.5 fold increase in perceptual color resolution over red and cyan saturation changes, and no image attributes are lost. It is not practical to use all of the possible hues in the new coordinate system, nor does the system need to cover all possible combinations for visible and near IR sensor readings. One of the most difficult tasks for the scientist working with color is to create a hue scale that looks reasonable. Such a scale has been calculated for 100 hues and 256 hues. Since hue scales are display devices and viewer dependant, some modification is almost always needed, but a good starting point is nice to have. The Ambroziak Color Coordinate System (ACCS) image color coordinate system, which was chosen for testing, was one which replaces the positive vegetation indexes with changing hue and gives all negative index values a single hue which contrasts with the positive NVI hues. The result is an image with (1) all of 25 the positive attributes of the false color IR image, (2) 3.5 times the perceptual resolution of false color IR, and (3) which uses only two coordinate axes leaving one axis free for additional information such as thermal IR data. The new coordinate system used equally spaced hues from red through green to blue to display the positive values on the NVI. There is no loss of information for analysis because all possible values of naturally occurring combinations of visible and near IR reflectance are assigned a unique color. The negative vegetation indexes are not as important as the positive in crop status assessment and all combinations of visible and near IR reflectance are not possible in the negative index region of the reflectance graph. The negative index values are basically one dimensional in the visible and near IR portion of the spectrum. Pure, deep water is near the origin and moves in an arc towards the zero NVI of whatever substance is beneath the water or suspended in it as the depth decreases or turbidity increases. These positions are identified well enough by intensity alone for crop assessment analysis. The region of the ACCS covered by water has at least the same perceptual resolution as the false color IR image. In the false color IR image, the water is shown by blue and/or cyan. Depending on the satellite used to create the image deep, clear water is either very dark blue (Landsat) or cyan (AVHRR). In both cases, the color is black on most images and hue has no meaning. As the water becomes more shallow or more turbid, the intensity increases in false color IR and in the ACCS images, both producing similar results. The major difference in the ACCS is that the saturation does not decrease as it does in the false color IR image. The ACCS displays water as magenta in all cases. The use of hue instead of saturation does not violate the use of the minds' natural coordinate system to display the normalized vegetation index since hue is one of the axes of the IHS system. The use of hue for both the sign of the index and its magnitude is quite valid because both the index and the hue are represented by angles. The normalized vegetation index is an angle measured at the origin. Hue is an angle in the hue saturation plane of the IHS conical type coordinated system. The correspondence is at least as natural as the use of saturation from index values of minus one to plus one. The false color IR image coordinate system slices though the IGS coordinate system cone while the ACCS wraps around the outside. The coordinate system makes a distinction between land and water which is greater than that made by the false color IR image. The color of soil on the false color IR image is grey or white and anything but black water is unsaturated cyan. The maximum color distance from cyan to white (assuming cyan of saturation = 1) is 49.7 jpcd while the distance form all water on the ACCS to soil is the color distance from magenta to red which is 59.2 jpcd (table 1). The color distance along the intensity axis is the same in both coordinate systems. The patterns of 26 land and water are, therefore, as visible or more visible than those patterns on false color IR. An annoying feature of the ACCS is that clouds are bright red rather than a natural white as they appear on false color IR. This can be overcome by making the pixels with high visible reflection values white, because clouds are bright in the visible and nothing else except bright sand is that bright. A better solution is to use the thermal channel to detect clouds and make them white. Temperature of the cloud can be displayed by saturation changes so that the saturation channel is used to display temperature making the ACCS a true three channel display with white clouds. Using this IHS color coordinate system, it was possible to do analysis of African vegetation which could not be done with false color IR images. In training courses, repeated attempts by students to use the false color IR images always resulted in failure while using the ACCS was usually successful. Color vision problems of some students prevented one color coordinate system from being universally better that the others, but the best analysis was always done using the ACCS. SUMMARY Each application of color to an analysis problem is unique and no single color system can be called the best. Since color is more psychological than physical, some trial and error will always be a part of any color coordinate design process. The best tool to have for this process is one which is as versatile, as well as general and as controllable as possible and make sure you can change it, because no system design will ever let you do everything you will want to do. To use these systems, a working knowledge of color theory is a must and the more you use it, the easier it will become to use. I believe that image processing is the evolutionary process of putting any color of pixel anywhere you want to, and every time you do it, you think of a better way to do it. There are so many variables that building a truly generic system is impossible at best. 27