Lecture 21 Remote Sensing III Obtaining Information from Satellite Imagery

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Lecture 21Remote Sensing III Obtaining
Information from Satellite Imagery
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Lecture Plan

• 1. Spectral image enhancement
• 2. Spatial image enhancement
• 3. Unsupervised classification
• 4. Supervised classification

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• What is Image Enhancement?
• Altering the appearance of an image in such a way
  that the information of an image is better
  interpreted visually in terms of a particular
  need.
• It may involve single bands of data or multiple
  bands of data.
• Enhancement may be spectral or spatial.

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Why and when do we enhance Imagery?

• In its basic form, digital imagery simply a
  series of brightness values
• x, y location, z brightness value per band
• When?
• Brightness values are clustered within a narrow
  range,
• Particular combinations of spectral bands are
  required to discriminate between different
  features,
• We want to emphasize certain features and are
  less interested in other feature types.
• We want to remove small speckles of differing
  reflectance
• We want to increase the resolution of an image.
• Many others

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1. Spectral Enhancement
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Linear Contrast Stretch

• Raw data values in an image are explored in order
  to find best range of values for display of
  particular features and aid their interpretation.
  
• e.g. Bands 2, 3 4 of a Landsat TM image of
  Lowland rainforest and sugar cane farms.

Unenhanced
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Linear Contrast Stretch (cont.)

• The same image after a simple linear image
  contrast stretch.

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Histogram Equalisation

• Non-linear form of contrast enhancement.
• Uses the whole histogram in the contrast
  enhancement.
• Classes with low frequency have been amalgamated
• Classes with high frequency have been spaced out
  more widely than originally

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Band Ratios
The contrast in land-cover types in 1 or 2 can
be used to enhance image data. A transformation
involves

• BVs in a band being changed by applying a
  formula

• or by combining BVs from more than one band and
  applying a formula.

• NIR/ VISIBLE - a division or ratio.
• It enhances the differences between vegetated and
  un-vegetated areas.

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Reasons for using Band Ratios

• Logically, ratioing will cancel out or reduce
  whatever is common in two images and exaggerate
  where they are different.

• This creates a new set of data that may be used
  to highlight certain features.

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A ratio transformation does a number of things

• it enhances imagery
• it reduces 2 bands to one REDUNDANCY
• it can actually be used as a basis for physical
  measurements
• (crop productivity, LAI). Field measurements
  correlated with ratio values. TONNES/ HA

• Thus, because of this stability they can be
  used for monitoring

• Other ratios include TM3/TM2 as a measure of iron
  oxide concentration and TM5/TM7 as a clay
  alteration guide.

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• A band ratio is a new channel of data created by
  the division of two sets of band digital numbers.
  
• It is the simplest of the multi-spectral
  techniques, and a type of GIS 'overlay'
• New ratio channel a (band x / band y ) (where
  'a' is a numerical scaling factor)

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Spectral Slope Enhancement

• In general, band ratioing can emphasise the
  difference between adjacent spectrum sections in
  a  single image
• e.g. the reduction of the 'topographic effect'
• the Infra-red and red is the most common ratio.

• Since healthy vegetation  involves high
  reflectance in IR and low in red, any IR/Red (or
  any visible wavelength) will show vegetation
  differences more clearly

• Higher values (IR/red) more vegetation
  (biomass)

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Which other ratios would be useful?

• Generally pairs of bands that are highly
  correlated will not show much information in
  their ratios

• There are many options in a multi-band dataset
  n (n-1) / 2 3 (3 bands), 6 (4 bands), 15 (6
  bands), 21 (7 bands)

• We might more often pick pairs of bands from
  different portions of the EM spectrum
• MSS IR x Vis 
• TM Vis x IR x MIR

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• However there are some useful applications using
  two bands in the same region, e.g. in geology
• MSS 5/4, 6/5 7/6 (4 green, 5 red, 6,7 NIR)
• 3/2, 7/5, 3/1, 5/7 mineral enhancement (hydro
  thermally altered rocks)

• Ratio of two bands in same EM region distinguish
• soil types, geologic types

• Ratio of two bands not in the same area of the EM
  spectrum distinguishes...
• major groups e.g. water, rock, bare, coniferous,
  deciduous

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Vegetation indices

• An index involves a 'normalised difference',
  which compensates for additive effects as well as
  multiplicative effects negated by ratioing.

• Band difference / band sum NDI or Normalised
  Difference Index.

• The most common is the Normalised Difference
  Vegetation Index (NDVI)

• NDVI (IR - Red) / (IR Red)  For TM (TM4
  -TM3) / (TM4 TM3)

• NDVI is used extensively to present a measure of
  vegetation amount or biomass, especially in
  regional and global estimates.

• Other 'normalised indices' include
• NDSI (TM2-TM5) / (TM2TM5)   (S Snow)
• NDGI (TM4-TM2) / (TM4TM2)   (G Green)

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Indices (cont.)

• The use of ratios as a monitoring function is
  even better if they are NORMALISED.
• Normal ratio scaling (0-255 / 0-255) is usually
  between -5 and 5 and this range often differs
  between dates.

• Normalised ratios have the same scaling between
  dates -1 to 1 they are bounded ratios and can
  thus be compared.

• This compatibility between dates enhances its
  utility for comparing physical measurements
  between dates.

• NDVI is important as a vegetation discriminator

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NDVI

• It is also assumed to normalise reflectance from
  the soil background in an area of vegetation and
  also reflectance from the atmosphere so that all
  that is being recorded is differences in
  vegetation.

• It is not just a primary resource it also
  represents an energy flux. Vegetation comprises
  99 of earth's biomass. It is an important source
  of global energy and moisture for atmosphere.

• Total surface area of leaf material in contact
  with atmosphere is greater than surface area of
  planet. Major solar energy exchange surface
  through process of evapotranspiration (ET).

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• Composites of Ratios
• Ratio 1/4 for the blue band, basalt stands out
• Ratio 4/2 for green as this ratio depicts
  vegetation
• Band 3 for red as it was the only band not yet
  used.
• Basalts show up as blue
• Basaltic andesite shows up as pink
• Vegetation is green

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b. Empirically-based Image Transforms

• Specific transformations that are developed from
  real data using specific sensors.
• Originally based on Landsat MSS data in the
  1970s, when researchers saw that agricultural
  crops occupied an identifiable region of the 4
  dimensional space of MSS bands.
• ie. certain features (eg. soils, agricultural
  crops) can be clearly identified upon specific
  manipulation of the data.
• 2 examples
• Perpendicular vegetation index
• Tasselled cap transform.

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Perpendicular Vegetation Index

• Plot of visible red vs. NIR for a partly
  vegetated area
• PVI72 (0.355MSS7 - 0.149MSS5)2 (0.355MSS5 -
  0.852MSS7)2
• Value is correlated with green leaf area index
  and biomass.

• Requires care, however.
• Small sample size
• Specific location
• Sensitive to recent rainfall

Conceptual diagram of the PVI, which is
proportional to the perpendicular distance from P
to the soil line S1-S2.
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Tasselled Cap (Kauth-Thomas) Transform

• Related to PVI
• Kauth and Thomas (1976) defined a linear
  transformation to enhance the data according to
  the data structure in a particular dataset.
• Better represents soil and vegetation gradients
  in multispectral data

Output Layer1 Brightness Layer2
Greenness Layer3 Wetness
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Principal Components Analysis
PCA output (3 Axes) Axis2 (green gun) relates to
natural vegetation structure
Landsat ETM (7 bands)
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2. Spatial Enhancement

• Methods for selectively empasising or supressing
  information at different spatial scales, or
  artificially increasing the resolution of an
  image.
• For example,
• Suppress noise caused by detector imbalance
  (banding)
• Emphasise edges between fairly homogenous
  features
• Remove scattered bright pixels from a forest
  canopy
• Removal of noise such as scatter in a radar
  image.

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Types of Spatial Enhancement

• 1. Filtering
• a. Low-pass (smoothing) filters.
• a. High-pass (sharpening) filters.
• 2. Edge detection
• 3. Frequency-domain filter
• 4. Resolution merge

• Can get to these from
• viewer/raster/filtering/convolution
• Interpreter/spatial enhancement

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1a. Low-pass (smoothing) Filters
1 dimensional example Cross-section of a TM image

• Often, the underlying trends in data are obscured
  by local patterns and random noise.
• Therefore, removal.
• Involves a moving window of 3x3, 5x5 or 7x7
  pixels, so that every pixel (except those at the
  edge of the image) are allocated new values based
  of those of their neighbours
• Some uses
• Removal of banding.
• Smoothing away effects of image-to-image
  mis-registration

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• This is an example of a moving average low-pass
  filter.
• The target pixel therefore becomes the average of
  all those around it.
• Another types include
• Median filters
• Adaptive filters based on the mean and variance
  of the grey levels beneath the window. Eg. Sigma
  filter

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1a. Low-pass (smoothing) filters (cont).
Original image
55 low pass
33 low pass
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a. High-pass (sharpening) filters.

• These techniques de-blur an image
• Two Main Approaches
• Image subtraction
• Derivative-based methods
• Derivative-based methods use rate of change of
  grey-scale value over space
• These filters exaggerate difference where
  gradients in change in BVs of adjacent pixels are
  stronger.

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2. Edge detection

• Edges are sharp changes in the grey-scale values
  of an image or layer.
• May have some interpretation regarding cultural
  features such as roads.
• Also very important in geology identifying
  geological structure such as a fault.
• A range of techniques
• 2 sets of filter matrices (x direction and y
  direction)
• Subtract low-pass filtered image from the
  original
• Others

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2. Edge detection (cont)
Y weight matrix
X weight matrix
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4. Resolution Merge

• Resolution Merge functions integrate imagery of
  different spatial resolutions (pixel size). These
• Can be used either intra-sensor (i.e., Landsat TM
  multi with Landsat TM Pan) or inter-sensor (i.e.,
  SPOT panchromatic with Landsat TM).
• A key element of these multi-sensor integration
  techniques is that they retain the thematic
  information of the multiband raster image.
• Thus, you could merge a Landsat TM (28.5 m pixel)
  scene with a SPOT panchromatic scene (10 m pixel)
  and still do a meaningful classification, band
  ratio image, etc.

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Resolution Merge

• For example

Landsat ETM multi (30m) Landsat ETM Pan (15m)
Landsat ETM multi (15m)
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Image classification

• Digital image classification is process of
  assigning pixels to classes according to some
  classification scheme
• Compare unknown pixels to one another and to
  pixels of known identity or class using image
  bands as variables
• Classes form regions on image or map, so end
  result is a mosaic of uniform patches
• Process uses classification algorithms or
  classifiers

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Image classification

• Classification may be end point (ie land
  classification),
• May be an intermediate step in the analysis (ie
  further spatial and statistical analysis later)
• Many different classification strategies
• Characteristics of each image and study differ,
  therefore different classification strategies
  need to be used for different tasks
• For this reason, essential that the analyst
  understands the different image classification
  techniques to allow the most appropriate
  classifier to be chosen.

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Types of Classification

• Unsupervised Classification
• Minimal interaction from the analyst.
• Searching for natural groups of pixels in the
  image.
• Supervised Classification
• Considerable interaction by the analyst.
• Identifies areas on an image that are known to
  belong to each category.
• Uses these areas to guide the classification.

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One band classification - density slicing

• Dividing a range of brightnesses into intervals,
  then assigning each interval a colour.
• Can be achieved through unsupervised or
  supervised techniques.

NDVI Values
Split into classes
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Unsupervised Classification

• Identification of natural groups within
  multispectral data.
• Classification program
• automatically searches for natural groupings or
  clusters of the spectral properties of pixels

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Unsupervised procedure

• Typical procedure for performing an unsupervised
  classification is as follows
• Run the program to cluster the image data into a
  user-defined number of spectral classes
• Display the output classified image and assign
  each spectral class a tentative name and colour
  corresponding to a thematic class
• Pool and reclassify where multiple classes
  represent an homogenous feature of interest
• Evaluate the accuracy of the classification by
  overlaying classes or using statistical methods

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Clustering generalisation
Fine (41 classes)
Broad (11 classes)
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Supervised Classification

• Using samples of known identity to classify
  pixels of unknown identity into one of a number
  of information classes.
• These samples are known as training samples, and
  are located in training areas.
• Training samples can come from
• Ground surveys,
• Identification on the image
• Overlay or comparison with other spatial data
• Selection of appropriate training areas are vital

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Overall Project Plan Using Supervised Methodology

• State nature of problem
• Identify region of interest
• Identify classes of interest
• Address spatial, spectral and temporal resolution
  issues
• Acquire remote sensing and ground reference data
• Geometric rectification
• Issues of atmosphere, season, etc.
• a priori knowledge of area (surveys)

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Landsat TM example

• Supervised classification based on known
  vegetation types
• provides details of eleven structural vegetation
  classes
• can provide association mapping for floristically
  simple situations
• accuracy assessment gained from ground surveys
  and API

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