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Copyright, 1998-2013 © Qiming Zhou GEOG3610 Remote Sensing and Image Interpretation Human Vision and Colour.

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Presentation on theme: "Copyright, 1998-2013 © Qiming Zhou GEOG3610 Remote Sensing and Image Interpretation Human Vision and Colour."— Presentation transcript:

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2 Copyright, © Qiming Zhou GEOG3610 Remote Sensing and Image Interpretation Human Vision and Colour

3 2 rThe human visual system rColour vision rReproducing colours rColour images and colour photos

4 3 The human visual system rThe interpretation of colour images is one of the most common tasks in remote sensing, through human vision. rThe human visual system comprises a receptor, the eyes, linked to a processing system, the brain.

5 4 The human visual system (cont.) rThe human eye is very similar to a camera: rLight reflected from or emitted by an object is focused by a lens on the back of the eye. rCells on the back wall of the eye, the retina, have an electrochemical response to the level of light that they absorb. rThe response from the cells is interpreted by the brain to form an image of the reality.

6 5 The human visual system

7 6 Human eye

8 7 Relative distribution of rods and cones in the retina

9 8 Absorption spectra rEyes contain cells that respond to a particular type of EMR. rThe wavelengths of lights that the retinal cells respond to can be measured. rPigments within the cells absorb certain wavelengths more than others.

10 9 Absorption of light sensitive cells monochromaticdichromatic Absorption of photons by pigment in cell Relative sensitivity Wavelength Relative sensitivity Wavelength

11 10 The retina rThere are two types of photoreceptors: rods and cones. rRods: respond to small changes in intensity, but are insensitive to differences in wavelength (scotopic vision). rCones: need a greater degree of illumination, but sensitive to differences in wavelength (photopic vision). u Three varieties of cones sensitive to different narrow bands of spectrum

12 11 Sensitivity of cones Relative sensitivity Wavelength Blue cone Green cone Red cone

13 12 Colour vision rColour vision comes from relative differences in the amount of either blue, green or red light that are absorbed by cones on the retina. rThe trichromatic nature of human vision is the basis of red-green-blue light theory. u A colour is viewed by stimulating each of the three cones in a controlled manner.

14 13 Reproducing colours rThe hue that we see depends on the ratio of blue, green and red lights which are known as the additive colour primaries. rThe three primaries can be mixed together to produce all colour shades

15 14 Additive primaries

16 15 Pigments rColours can also be produced by starting with white and subtracting a proportion of the blue, green or red. rA red car looks red because the other colour components (blue and green) are absorbed by pigments in the paint. rThese pigments are known as subtractive primaries.

17 16 Subtractive primaries BGRG + R = Y -B BGRB + R = M -G BGRG -B -R

18 17 The RGB colour model Red (1,0,0) Yellow (1,1,0) White (1,1,1) Green (0,1,0) Cyan (0,1,1) Blue (0,0,1) Magenta (1,0,1) Black (0,0,0)

19 18 The CMY colour model Green Cyan BlueMagenta Red Yellow Black (minus blue) (minus green) (minus red) The Relation between RGB and CMY C = 1 - R M = 1 - G Y = 1 - B

20 19 The HSI colour model rColour (hue) rPurity (saturation) rBrightness (intensity) rThe HSI coordinates are derived using the RGB colour cube with axes redefined according to the shade of colour, the purity of colour and the brightness of colour.

21 20 The HSI colour model (cont.) Saturation Intensity Hue Red Yellow White Green Cyan Blue Magenta Black

22 21 Colour images and colour photos rBlack and white photos record all light as a shade of grey. rIf filters are used in front of the camera, a picture can be taken of only blue, green or red light. rWhen the picture of blue light is coloured blue, green coloured green and red coloured red, we see a normal colour picture.

23 22 Normal colour images Red bandGreen bandBlue band Normal colour composite

24 23 False colour images rIf we change the picture using red light for blue photo, green for red, blue for green, we produce a false colour image, or false colour composite. rSimilarly, we can use these primaries to show some images recorded in the invisible areas (also called bands) of spectrum (e.g. infrared), we can see what we could not see by our naked eyes.

25 24 False colour images (cont.) Red bandGreen bandBlue band False colour composite

26 25 Visible and near-infrared lights rThe choice of bands from a remote sensor depends on the type of information that you want to find in the image. rInfrared light is very useful for vegetation and soil interpretation because plants absorb most of the visible light while reflecting high in near- infrared. rUsing a false colour composite, vegetation information can be enhanced by assigning red light to near-infrared bands.

27 26 Colour infrared composite NIR bandRed bandGreen band Source: Ross Alford (www.pibweb.com/ross)

28 27 Colour infrared composite NIR bandRed bandGreen band Source: Ross Alford (www.pibweb.com/ross)

29 28 Interpreting images rComputer processed images enhance differences within the image, not just in colour (hue), but also in the strength and brightness of the colours (saturation and intensity). rOther information, e.g. texture and tones, can also be used to enhance the images to help us to interpret the images.


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