Remote Sensing and Image Processing 2 Dr Mathias

Remote Sensing and Image Processing: 2 Dr. Mathias (Mat) Disney UCL Geography Office: 301, 3 rd Floor, Chandler House Tel: 7670 4290 Email: mdisney@geog. ucl. ac. uk www. geog. ucl. ac. uk/~mdisney 1

Image processing: image display and enhancement Purpose • visual enhancement to aid interpretation • enhancement for improvement of information extraction techniques • Today we will look at image display and histogram manipulation (contrast ehancement etc. ) 2

Image Display • The quality of image display depends on the quality of the display device used – and the way it is set up / used … • computer screen - RGB colour guns – e. g. 24 bit screen (16777216) • 8 bits/colour (28) • or address differently 3

Colour Composites ‘Real Colour’ composite red band on red green band on green blue band on blue Swanley, Landsat TM 1988 4

Colour Composites ‘Real Colour’ composite red band on red 5

Colour Composites ‘Real Colour’ composite red band on red green band on green 6

Colour Composites ‘Real Colour’ composite red band on red green band on green blue band on blue approximation to ‘real colour’. . . 7

Colour Composites ‘False Colour’ composite NIR band on red band on green band on blue 8

Colour Composites ‘False Colour’ composite NIR band on red band on green band on blue 9

Colour Composites ‘False Colour’ composite • many channel data, much not comparable to RGB (visible) – e. g. Multi-temporal data – AVHRR MVC 1995 April August September 10

Greyscale Display Put same information on R, G, B: August 1995 11

Density Slicing 12

Density Slicing 13

Density Slicing Don’t always want to use full dynamic range of display Density slicing: • a crude form of classification 14

Density Slicing Or use single cutoff = Thresholding 15

Density Slicing Or use single cutoff with grey level after that point ‘Semi-Thresholding’ 16

Pseudocolour • use colour to enhance features in a single band – each DN assigned a different 'colour' in the image display 17

Pseudocolour • Or combine with density slicing / thresholding 18

Histogram Manipluation • WHAT IS A HISTOGRAM? 19

Histogram Manipluation • WHAT IS A HISTOGRAM? 20

Histogram Manipluation • WHAT IS A HISTOGRAM? Frequency of occurrence (of specific DN) 21

Histogram Manipluation • Analysis of histogram – information on the dynamic range and distribution of DN • attempts at visual enhancement • also useful for analysis, e. g. when a multimodal distibution is observed 22

Histogram Manipluation • Analysis of histogram – information on the dynamic range and distribution of DN • attempts at visual enhancement • also useful for analysis, e. g. when a multimodal distibution is observed 23

Histogram Manipluation Typical histogram manipulation algorithms: 0 output 255 Linear Transformation 0 255 input 24

Histogram Manipluation Typical histogram manipulation algorithms: 0 output 255 Linear Transformation 0 255 input 25

Histogram Manipluation Typical histogram manipulation algorithms: Linear Transformation • Can automatically scale between upper and lower limits • or apply manual limits • or apply piecewise operator But automatic not always useful. . . 26

Histogram Manipluation Typical histogram manipulation algorithms: Histogram Equalisation Attempt is made to ‘equalise’ the frequency distribution across the full DN range 27

Histogram Manipluation Typical histogram manipulation algorithms: Histogram Equalisation Attempt to split the histogram into ‘equal areas’ 28

Histogram Manipluation Typical histogram manipulation algorithms: Histogram Equalisation Resultant histogram uses DN range in proportion to frequency of occurrence 29

Histogram Manipluation Typical histogram manipulation algorithms: Histogram Equalisation • Useful ‘automatic’ operation, attempting to produce ‘flat’ histogram • Doesn’t suffer from ‘tail’ problems of linear transformation • Like all these transforms, not always successful • Histogram Normalisation is similar idea • Attempts to produce ‘normal’ distribution in output histogram • both useful when a distribution is very skewed or multimodal skewed 30

Colour Spaces • Define ‘colour space’ in terms of RGB • Only for visible part of spectrum: 31

Colour Spaces • RGB axes: 32

Colour Spaces • RGB (primaries) as axes 33

Colour Spaces • Alternative: CMYK ‘subtractive primaries’ • often used for printing (& some TV) 34

Colour Spaces • Alternative: CMYK ‘subtractive primaries’ 35

Colour Spaces • Other important concept: HSI transforms • Hue (which shade of color) • Saturation (how much color) • Intensity • also, HSV (value), HSL (lightness) 36

Colour Spaces • Other important concept: HSI transforms 37

Practical 1 • Histogram manipulation – Contrast stretching and histogram equalisation • visual (qualitative) enhancement of images according to brightness • Highlight clouds / sea / land in AVHRR image of UK • Multispectral information – Different DN (digital number) values in different bands – Due to different properties of surfaces 38