Remote Sensing and Image Processing: 2 Dr. Hassan J. Eghbali

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.) Dr. Hassan J. Eghbali

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 ( ) 8 bits/colour (2 8 ) or address differently Dr. Hassan J. Eghbali

Colour Composites ‘Real Colour’ composite red band on red green band on green blue band on blue Swanley, Landsat TM 1988 Dr. Hassan J. Eghbali

Colour Composites ‘Real Colour’ composite red band on red Dr. Hassan J. Eghbali

Colour Composites ‘Real Colour’ composite red band on red green band on green Dr. Hassan J. Eghbali

Colour Composites ‘Real Colour’ composite red band on red green band on green blue band on blue approximation to ‘real colour’... Dr. Hassan J. Eghbali

Colour Composites ‘False Colour’ composite NIR band on red red band on green green band on blue Dr. Hassan J. Eghbali

Colour Composites ‘False Colour’ composite NIR band on red red band on green green band on blue Dr. Hassan J. Eghbali

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 April; August; September Dr. Hassan J. Eghbali

Greyscale Display Put same information on R,G,B: August 1995 Dr. Hassan J. Eghbali

Density Slicing Dr. Hassan J. Eghbali

Density Slicing Dr. Hassan J. Eghbali

Density Slicing Don’t always want to use full dynamic range of display Density slicing: a crude form of classification Dr. Hassan J. Eghbali

Density Slicing Or use single cutoff = Thresholding Dr. Hassan J. Eghbali

Density Slicing Or use single cutoff with grey level after that point ‘Semi-Thresholding’ Dr. Hassan J. Eghbali

Pseudocolour use colour to enhance features in a single band –each DN assigned a different 'colour' in the image display Dr. Hassan J. Eghbali

Pseudocolour Or combine with density slicing / thresholding Dr. Hassan J. Eghbali

Histogram Manipluation WHAT IS A HISTOGRAM? Dr. Hassan J. Eghbali

Histogram Manipluation WHAT IS A HISTOGRAM? Dr. Hassan J. Eghbali

Histogram Manipluation WHAT IS A HISTOGRAM? Frequency of occurrence (of specific DN) Dr. Hassan J. Eghbali

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 Dr. Hassan J. Eghbali

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 Dr. Hassan J. Eghbali

Histogram Manipluation Typical histogram manipulation algorithms: Linear Transformation input output

Histogram Manipluation Typical histogram manipulation algorithms: Linear Transformation input output Dr. Hassan J. Eghbali

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... Dr. Hassan J. Eghbali

Histogram Manipluation Typical histogram manipulation algorithms: Histogram Equalisation Attempt is made to ‘equalise’ the frequency distribution across the full DN range Dr. Hassan J. Eghbali

Histogram Manipluation Typical histogram manipulation algorithms: Histogram Equalisation Attempt to split the histogram into ‘equal areas’ Dr. Hassan J. Eghbali

Histogram Manipluation Typical histogram manipulation algorithms: Histogram Equalisation Resultant histogram uses DN range in proportion to frequency of occurrence Dr. Hassan J. Eghbali

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 Dr. Hassan J. Eghbali

Colour Spaces Define ‘colour space’ in terms of RGB Only for visible part of spectrum: Dr. Hassan J. Eghbali

Colour Spaces RGB axes: Dr. Hassan J. Eghbali

Colour Spaces RGB (primaries) as axes Dr. Hassan J. Eghbali

Colour Spaces Alternative: CMYK ‘subtractive primaries’ often used for printing (& some TV) Dr. Hassan J. Eghbali

Colour Spaces Alternative: CMYK ‘subtractive primaries’ Dr. Hassan J. Eghbali

Colour Spaces Other important concept: HSI transforms Hue(which shade of color) Saturation (how much color) Intensity also, HSV (value), HSL (lightness) Dr. Hassan J. Eghbali

Colour Spaces Other important concept: HSI transforms Dr. Hassan J. Eghbali

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 Dr. Hassan J. Eghbali