Environmental Remote Sensing GEOG 2021 Lecture 2 Image display and enhancement
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Image Display and Enhancement Purpose visual enhancement to aid interpretation enhancement for improvement of information extraction techniques 3
How to? Altering image contrast Histogram Manipulation Display Properties Transformations Density slicing Greyscale Display Colour composites Pseudocoluor Re-express or Transformation Image arithmetic (+ ) 4
Histogram Manipulation WHAT IS A HISTOGRAM? Frequency of occurrence (of specific DN) 5
Histogram Manipulation 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 distribution is observed 6
Histogram Manipulation Typical histogram manipulation algorithms: Change the original values So that more of the available range is used This then increases the contrast between features and their backgrounds e.g. Linear Contrast Stretch 7
Histogram Manipulation Central Massachusetts (Original) tail 8
Histogram Manipulation Central Massachusetts (Linear Stretch:22-140) 9
Histogram Manipulation Central Massachusetts (Linear Stretch:37-118) 10
Histogram Manipulation output 0 255 Typical histogram manipulation algorithms: Linear Transformation 0 255 input 11
Histogram Manipulation output 0 255 Typical histogram manipulation algorithms: Linear Transformation 0 255 input 12
Histogram Manipulation Typical histogram manipulation algorithms: Linear Contrast Stretch Can automatically scale between upper and lower limits or apply manual limits or apply piecewise operator But automatic not always useful... 13
Histogram Manipulation Typical histogram manipulation algorithms: Histogram Equalisation Attempt is made to equalise the frequency distribution across the full DN range 14
Histogram Manipulation Typical histogram manipulation algorithms: Histogram Equalisation Attempt to split the histogram into equal areas 15
Histogram Manipulation Typical histogram manipulation algorithms: Histogram Equalisation Resultant histogram uses DN range in proportion to frequency of occurrence 16
Histogram Manipulation Original Histogram Linear Stretch Histogram Equalisation 17
Histogram Manipulation 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 18
Density Slicing 19
Density Slicing Highlight different but internally homogeneous areas At the expense of loss of detail 20
Density Slicing Use single cutoff = Thresholding 21
Density Slicing Multiple cutoffs Or don t always want to use full dynamic range of display Density slicing: a crude form of classification 22
Black & White, so far Greyscale, actually Single band 23
Bands RS images capture EM energy (i.e. light) by sampling over predetermined wavelength ranges which are referred to as 'bands' i.e. blue, green, red, near-infrared, mid-infrared, farinfrared, thermal infrared. 24
Colour Composites Real Colour composite red band on red green band on green blue band on blue Swanley, Landsat TM 1988 25
Colour Composites Real Colour composite red band on red 26
Colour Composites Real Colour composite red band on red green band on green 27
Colour Composites Real Colour composite red band on red green band on green blue band on blue approximation to real colour... 28
Colour Composites False Colour composite NIR band on red red band on green green band on blue 29
Colour Composites False Colour composite NIR band on red red band on green green band on blue 30
Colour Composites False Colour composite many channel data, much not comparable to RGB (visible) e.g. Multi-polarisation SAR HH: Horizontal transmitted polarization and Horizontal received polarization VV: Vertical transmitted polarization and Vertical received polarization HV: Horizontal transmitted polarization and Vertical received polarization 31
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 32
Greyscale Display Put same information on R,G,B: August 1995 August 1995 August 1995 33
Pseudocolour use colour to enhance features in a single band each DN assigned a different 'colour' in the image display 34
Pseudocolour Human visual system is particularly efficient in detecting variations in hue and saturation, but not so effective in detecting intensity variations Mapping from a 1-D greyscale to a 3-D (RGB) colour 35
Image Arithmetic 36
Image Arithmetic Combine multiple channels of information to enhance features e.g. NDVI = (NIR-R)/(NIR+R) Normalized Difference Vegetation Index 37
Image Arithmetic Combine multiple channels of information to enhance features e.g. NDVI (NIR-R)/(NIR+R) 38
Image Arithmetic Common operators: Ratio Landsat TM 1992 Southern Vietnam: green band what is the shading? 39
Image Arithmetic Common operators: Ratio topographic effects visible in all bands FCC 40
Image Arithmetic Common operators: Ratio (ch a /ch b ) apply band ratio = NIR/red what effect has it had? 41
Image Arithmetic Common operators: Ratio (ch a /ch b ) Reduces topographic effects Enhance/reduce spectral features e.g. ratio vegetation indices (SAVI, NDVI++) 42
Image Arithmetic Common operators: Subtraction MODIS NIR: Botswana Oct 2000 Predicted Reflectance Based on tracking reflectance for previous period examine CHANGE 43
Image Arithmetic Measured reflectance 44
Image Arithmetic Difference (Z score) measured minus predicted noise 45
Image Arithmetic Common operators: Addition + Reduce noise (increase SNR) averaging, smoothing... Normalisation (as in NDVI) = 46
Image Arithmetic Common operators: Multiplication rarely used per se: logical operations? land/sea mask 47
Summary Histogram Manipulation Properties Density slicing Transformations Display Colour composites Greyscale Display Pseudocoluor Image arithmetic + 48
Summary Followup: web material http://www.geog.ucl.ac.uk/~plewis/geog2021 Mather chapters (e-book available through UCL lib) Follow up material on web and other RS texts Access Journals 49