Copyright, 1998-2013 © Qiming Zhou

GEOG3610 Remote Sensing and Image Interpretation

Human Vision and ColourHuman Vision and Colour

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Human Vision and Colour

The human visual systemColour visionReproducing coloursColour images and colour photos

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The human visual system

The interpretation of colour images is one of the most common tasks in remote sensing, through human vision.

The human visual system comprises a receptor, the eyes, linked to a processing system, the brain.

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The human visual system (cont.)

The human eye is very similar to a camera: Light reflected from or emitted by an object is

focused by a lens on the back of the eye. Cells on the back wall of the eye, the retina,

have an electrochemical response to the level of light that they absorb.

The response from the cells is interpreted by the brain to form an image of the reality.

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The human visual system

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Human eye

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Relative distribution of rods and cones in the retina

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Absorption spectra

Eyes contain cells that respond to a particular type of EMR.

The wavelengths of lights that the retinal cells respond to can be measured.

Pigments within the cells absorb certain wavelengths more than others.

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Absorption of light sensitive cells

monochromatic dichromatic

Absorption of photons by pigment in cell

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Wavelength

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Wavelength

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The retina

There are two types of photoreceptors: rods and cones.

Rods: respond to small changes in intensity, but are insensitive to differences in wavelength (scotopic vision).

Cones: need a greater degree of illumination, but sensitive to differences in wavelength (photopic vision). Three varieties of cones sensitive to different

narrow bands of spectrum

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Sensitivity of cones

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Wavelength

Blue cone Green cone

Red cone

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Colour vision

Colour vision comes from relative differences in the amount of either blue, green or red light that are absorbed by cones on the retina.

The trichromatic nature of human vision is the basis of red-green-blue light theory. A colour is viewed by stimulating each

of the three cones in a controlled manner.

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Reproducing colours

The hue that we see depends on the ratio of blue, green and red lights which are known as the additive colour primaries.

The three primaries can be mixed together to produce all colour shades

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Additive primaries

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Pigments

Colours can also be produced by starting with white and subtracting a proportion of the blue, green or red.

A red car looks red because the other colour components (blue and green) are absorbed by pigments in the paint.

These pigments are known as subtractive primaries.

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Subtractive primariesBGR G + R = Y

-B

BGR B + R = M

-G

BGR G

-B -R

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The RGB colour modelRed (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)

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The CMY colour model

Green

Cyan

Blue Magenta

Red

Yellow

Black

(minus blue)

(minus green)

(minus red)

The Relation between RGB and CMY

C = 1 - RM = 1 - GY = 1 - B

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The HSI colour model

Colour (hue)Purity (saturation)Brightness (intensity)The 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.

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The HSI colour model (cont.)

Saturation

Intensity

Hue

Red Yellow

White

Green

CyanBlue

Magenta

Black

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Colour images and colour photos

Black and white photos record all light as a shade of grey.

If filters are used in front of the camera, a picture can be taken of only blue, green or red light.

When the picture of blue light is coloured blue, green coloured green and red coloured red, we see a normal colour picture.

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Normal colour imagesRed band Green band Blue band

Normal colourcomposite

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False colour images

If 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.

Similarly, 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.

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False colour images (cont.)

Red band Green band Blue band

False colourcomposite

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Visible and near-infrared lights

The choice of bands from a remote sensor depends on the type of information that you want to find in the image.

Infrared light is very useful for vegetation and soil interpretation because plants absorb most of the visible light while reflecting high in near-infrared.

Using a false colour composite, vegetation information can be enhanced by assigning red light to near-infrared bands.

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Colour infrared compositeNIR band Red band Green band

Source: Ross Alford(www.pibweb.com/ross)

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Colour infrared compositeNIR band Red band Green band

Source: Ross Alford(www.pibweb.com/ross)

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Interpreting images

Computer 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).

Other information, e.g. texture and tones, can also be used to enhance the images to help us to interpret the images.