Lecture 7: Image Processing and Interpretation

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Lecture 7 Image Processing and Interpretation
Online Reading http//hosting.soonet.ca/eliris/re
motesensing/bl130lec10.html
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Class Activity Concept Map

• Image Enhancement
• Linear Contrast Stretch
• Equalized Contrast Stretch
• Spatial Filtering
• Low-pass Filters
• High-pass Filters
• Directional Filters
• Image Ratios
• Principle Components Analysis
• Contrast enhancement
• Image Processing
• photo-interpretation
• digital image processing
• classification techniques
• Image interpretation
• Photointerpretation
• machine-processing manipulations
• Image Restoration and Rectification
• Image Enhancement

• Striping
• line dropouts
• Image Enhancement
• Spatial Filtering.
• Contrast Stretching
• Image Histogram
• Contrast Stretching

• Low-pass Filters
• High-pass Filters
• Directional Filters

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(No Transcript)
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What is an Image?

• An image is an array, or a matrix, of square
  pixels (picture elements) arranged in columns and
  rows.

Figure 1 An image an array or a matrix of
pixels arranged in columns and rows.
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Black and white image

• In a (8-bit) greyscale image each picture element
  has an assigned intensity that ranges from 0 to
  255. A grey scale image is what people normally
  call a black and white image, but the name
  emphasizes that such an image will also include
  many shades of grey.

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An example8-bit greyscale image

• Each pixel has a value from 0 (black) to 255
  (white). The possible range of the pixel values
  depend on the colour depth of the image, here 8
  bit 256 tones or greyscales.

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Pixel Values, DN

• Pixel Values The magnitude of the
  electromagnetic energy (or, intensity) captured
  in a digital image is represented by positive
  digital numbers.
• The digital numbers are in the form of binary
  digits (or 'bits') which vary from 0 to a
  selected power of 2
• Image Type Pixel Value Color Levels
• 8-bit image 28 256 0-255
• 16-bit image 216 65536 0-65535
• 24-bit image 224 16777216 0-16777215  

16 million colors!!!
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Online Reading

• http//hosting.soonet.ca/eliris/remotesensing/bl13
  0lec10.html

Image Type Pixel Value Color Levels
8-bit image 28 256 0-255
16-bit image 216 65536 0-65535
24-bit image 224 16777216 0-16777215  
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DN

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Image below, brighter portions relate to higher
energy levels
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True color image

• A true-colour image assembled from three
  greyscale images coloured red, green and blue.
  Such an image may contain up to 16 million
  different colors.

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Image Resolution

• Image Resolution the resolution of a digital
  image is dependant on the range in magnitude
  (i.e. range in brightness) of the pixel value.
  With a 2-bit image the maximum range in
  brightness is 22 4 values ranging from 0 to 3,
  resulting in a low resolution image. In an 8-bit
  image the maximum range in brightness is 28 256
  values ranging from 0 to 255, which is a higher
  resolution image

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2-bit Image(4 grey levels)
8-bit Image(256 grey levels)
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two prime approaches in the use of remote sensing

• 1) standard photo-interpretation of scene content
  
• 2) use of digital image processing and
  classification techniques that are generally the
  mainstay of practical applications of information
  extracted from sensor data sets

To accomplish this, we will utilize just one
Landsat TM subscene that covers the Morro Bay
area on the south-central coast of California
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Image interpretation

• relies on one or both of these approaches
• Photointerpretationthe interpreter uses his/her
  knowledge and experience of the real world to
  recognize scene objects (features, classes,
  materials) in photolike renditions of the images
  acquired by aerial or satellite surveys of the
  targets (land sea atmospheric planetary) that
  depict the targets as visual scenes with
  variations of gray-scale tonal or color patterns
  (more generally, spatial or spectral variability
  that mirror the differences from place to place
  on the ground)
• machine-processing manipulations (usually
  computer-based) that analyze and reprocess the
  raw data into new visual or numerical products,
  which then are interpreted either by approach 1
  or are subjected to appropriate decision-making
  algorithms that identify and classify the scene
  objects into sets of information

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Image Processing CASI

• Computer-Assisted Scene Interpretation (CASI)
  also called Image Processing
• The techniques fall into three broad categories
• Image Restoration and Rectification
• Image Enhancement
• Image Classification
• There is a variety of CASI methods
• contrast stretching, band ratioing, band
  transformation, Principal Component Analysis,
  Edge Enhancement, Pattern Recognition, and
  Unsupervised and Supervised Classification

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Image Classification

• In classifying features in an image we use the
  elements of visual interpretation to identify
  homogeneous groups of pixels which represent
  various features or land cover classes of
  interest. In digital images it is possible to
  model this process, to some extent, by using two
  methods Unsupervised Classifications and
  Supervised Classifications.

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• Unsupervised Classifications
• this is a computerized method without direction
  from the analyst in which pixels with similar
  digital numbers are grouped together into
  spectral classes using statistical procedures
  such as nearest neighbour and cluster analysis.
  The resulting image may then be interpreted by
  comparing the clusters produced with maps,
  airphotos, and other materials related to the
  image site.

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• Supervised Classification

Training areas
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• Limitations to Image Classification
• have to be approached with caution because it
  is a complex process with many assumptions.
• In supervised classifications, training areas may
  not have unique spectral characteristics
  resulting in incorrect classification.
• Unsupervised classifications may require field
  checking in order to identify spectral classes if
  they cannot be verified by other means (i.e. maps
  and airphotos).

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Classification

• Classification is probably the most informative
  means of interpreting remote sensing data

• The output from these methods can be combined
  with other
• computer-based programs

• The output can itself become input for organizing
  and
• deriving information utilizing what is known as
• Geographic Information Systems (GIS)

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Image Processing Procedures

• Image Restoration most recorded images are
  subject to distortion due to noise which degrades
  the image. Two of the more common errors that
  occur in multi-spectral imagery are striping (or
  banding) and line dropouts

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Image Processing Procedures

• Dropped Lines are errors that occur in the sensor
  response and/or data recording and transmission
  which loses a row of pixels in the image.
•  

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Image Enhancement

• One of the strengths of image processing is that
  it gives us the ability to enhance the view of an
  area by manipulating the pixel values, thus
  making it easier for visual interpretation.
• There are several techniques which we can use to
  enhance an image, such as Contrast Stretching and
  Spatial Filtering.

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Image Enhancement

• Image Histogram For every digital image the
  pixel value represents the magnitude of an
  observed characteristic such as brightness level.
  An image histogram is a graphical representation
  of the brightness values that comprise an image.
  The brightness values (i.e. 0-255) are displayed
  along the x-axis of the graph. The frequency of
  occurrence of each of these values in the image
  is shown on the y-axis.

8-bit image(0 - 255 brightness levels)
Image Histogramx-axis 0 to 255y-axis number
of pixels
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Class Activity

• http//www.fas.org/irp/imint/docs/rst/Sect1/Sect1_
  1.html1-2

TM Band 3 Image of Morro Bay, California
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Image Enhancement

• Contrast Stretching Quite often the useful data
  in a digital image populates only a small portion
  of the available range of digital values
  (commonly 8 bits or 256 levels). Contrast
  enhancement involves changing the original values
  so that more of the available range is used, this
  then increases the contrast between features and
  their backgrounds. There are several types of
  contrast enhancements which can be subdivided
  into Linear and Non-Linear procedures.

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Image Enhancement

• Linear Contrast Stretch This involves
  identifying lower and upper bounds from the
  histogram (usually the minimum and maximum
  brightness values in the image) and applying a
  transformation to stretch this range to fill the
  full range.
• Equalized Contrast Stretch This stretch assigns
  more display values (range) to the frequently
  occurring portions of the histogram. In this way,
  the detail in these areas will be better enhanced
  relative to those areas of the original histogram
  where values occur less frequently.

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Linear Stretch Example
Before Linear Stretch
After Linear Stretch
The linear contrast stretch enhances the contrast
in the image with light toned areas appearing
lighter and dark areas appearing darker, making
visual interpretation much easier. This example
illustrates the increase in contrast in an image
before (left) and after (right) a linear
contrast stretch.
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Related to your activity last time -Is this
stretching?
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Spatial Filtering

• Spatial filters are designed to highlight or
  suppress features in an image based on their
  spatial frequency. The spatial frequency is
  related to the textural characteristics of an
  image. Rapid variations in brightness levels
  ('roughness') reflect a high spatial frequency
  'smooth' areas with little variation in
  brightness level or tone are characterized by a
  low spatial frequency. Spatial filters are used
  to suppress 'noise' in an image, or to highlight
  specific image characteristics.
• Low-pass Filters
• High-pass Filters
• Directional Filters
• etc

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Spatial Filtering

• Low-pass Filters These are used to emphasize
  large homogenous areas of similar tone and reduce
  the smaller detail. Low frequency areas are
  retained in the image resulting in a smoother
  appearance to the image.

Linear Stretched Image
Low-pass Filter Image
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Spatial Filtering

• High-pass Filters allow high frequency areas to
  pass with the resulting image having greater
  detail resulting in a sharpened image

Hi-pass Filter
Linear Contrast Stretch
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Spatial Filtering

• Directional Filtersare designed to enhance
  linear features such as roads, streams, faults,
  etc.The filters can be designed to enhance
  features which are oriented in specific
  directions, making these useful for radar imagery
  and for geological applications. Directional
  filters are also known as edge detection filters.

Edge DetectionLakes Streams
Edge DetectionFractures Shoreline
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Image Ratios

• It is possible to divide the digital numbers of
  one image band by those of another image band to
  create a third image. Ratio images may be used to
  remove the influence of light and shadow on a
  ridge due to the sun angle. It is also possible
  to calculate certain indices which can enhance
  vegetation or geology

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Sensor Image Ratio EM Spectrum Application
Landsat TM Bands 3/2 red/green Soils
Landsat TM Bands 4/3 PhotoIR/red Biomass
Landsat TM Bands 7/5 SWIR/NIR Clay Minerals/Rock Alteration
For example Normalized Difference Vegetation
Index (NDVI) a commonly use vegetation index
which uses the red and infrared bands of the EM
spectrum.
   
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Image Ratio example NDVI
NDVI image of Canada.Green/Yellow/Brown
represent  decreasingmagnitude of
thevegetation index.
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Principle Components Analysis

• Different bands in multispectral images like
  those from Landsat TM have similar visual
  appearances since reflectances for the same
  surface cover types are almost equal. Principle
  Components Analysis is a statistical procedure
  designed to reduce the data redundancy and put as
  much information from the image bands into fewest
  number of components. The intent of the procedure
  is to produce an image which is easier to
  interpret than the original.

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Data Visualization
Contrast enhancement or stretch reassigns the
DN range that corresponds to the
256 gray shades Top row of images are ETM data
with no enhancement and bottom row consists of
linear contrast stretches of the image DNs to the
full 0-255 gray shades
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Data
Visualization Ability to quickly
discern features is improved by using 3-band
color mixes Image below assigns blue to band 2,
green to band 4, and red to band 7 Vegetation
is green Surface water is blue Playa is gray and
white (Playas are dry lakebeds)
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• Color
  display
• Rely on display hardware to convert
  between DN and gray levels
• Digital Numbers (DNs) are image data
• Grey Levels (GLs) are numerical display values
• Look-Up Tables (LUTs) map DNs gt GLs and change
  image
• brightness, contrast and colors
• Actual displayed colors depend on the color
  response characteristics of
• the display system

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Data Visualization
Changing the color assignment to red, green, and
blue does not alter the surface
material only appearance
of the image All images below show only
combinations of bands 2, 4, and 7 of ETM
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Data
Visualization Other band combinations of
the same data set bring out different features
(or in some
cases lack there of) All images below show only
combinations of bands 2, 4, and 7 of ETM
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Video Wonder How Hubble Color Images are made?

• http//hubblesite.org/gallery/behind_the_pictures/
  
• Images must be woven together from the incoming
  data from the cameras, cleaned up and given
  colors that bring out features that eyes would
  otherwise miss.

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• File formats
• File formats play an important role in
  that many are automatically
• recognized in image
  processing packages
• Makes life very easy
• Raw data typically have no header information
• GeoTIFF is a variant of TIFF that includes
  geolocation information in
• header (http//remotesensing.org/geotiff/geotiff.
  html)
• HDF or Hierarchical Data Format
  (http//hdf.ncsa.uiuc.edu/) is a self-
• documenting format
• All metadata needed to read image file contained
  within the image file
• First developed for web sites in the 1980s
• Allows for variable length subfiles
• EOS-HDF is NASA version (http//hdf.ncsa.uiuc.edu
  /hdfeos.html)
• NITF
• National Imagery Transmission Format
• (http//remotesensing.org/gdal/frmt_nitf.html)

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• Data processing levels
• Recently, operational processing of remote
  sensing data has led to multiple
• 
  processing levels
• Standard types of preprocessing
• Radiometric calibration
• Geometric calibration
• Noise removal
• Formatting
• Generic description
• Level 0 raw, unprocessed sensor data
• Level 1 radiometric (1R or 1B) or geometric
  processing (1G)
• Level 2 derived product, e.g. vegetation index