Image Analysis, Interpretation

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Image Analysis, Interpretation Classification

• Dr. Garver
• GEO 420

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• United Launch Alliance (ULA) Atlas-V rocket with
  the Landsat Data Continuity Mission (LDCM)
  spacecraft launched Monday, Feb. 11, 2013 at
  Vandenberg Air Force Base.
• The LDCM mission is a collaboration between NASA
  and the U.S. Geological Survey that will continue
  the Landsat Program's 40-year data record of
  monitoring the Earth's landscapes from space.

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• The payload faring containing the Landsat Data
  Continuity Mission LDCM spacecraft is lifted to
  the top of Space Launch Complex-3E at Vandenberg
  Air Force Base where it will be hoisted atop a
  United Launch Alliance Atlas V for launch.

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Orbiting Carbon Observatory (OCO)

• Failed following a rocket malfunction
• fairing - part of rocket which covers satellite
  on top of launcher - did not separate properly.
• crashed into ocean near Antarctica.
• Nasa officials confirmed loss of the satellite at
  a press conference held at 1300 GMT.

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Orbiting Carbon Observatory (OCO) Feb. 2009

• Collect precise global measurements of carbon
  dioxide (CO2)
• Improve understanding of natural processes and
  human activities that regulate abundance and
  distribution.
• Enable more reliable forecasts of future changes
  in abundance and distribution of CO2 in
  atmosphere and the effect changes may have on
  Earth's climate.

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Important Aspects of OCO Mission

• Study carbon dioxide sources (where it comes
  from) and sinks (where it is pulled out of the
  atmosphere and stored).
• OCO-2 high-resolution spectrometers spreads
  reflected sunlight into its various colors like a
  prism, focusing on different, narrow color ranges
  to detect light with the specific colors absorbed
  by carbon dioxide and molecular oxygen. The
  amount of light absorbed at these specific colors
  is proportional to the concentration of carbon
  dioxide in the atmosphere. Scientists will use
  these data in computer models to quantify global
  carbon dioxide sources and sinks.

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• Scientists don't know why the amount of carbon
  dioxide absorbed by Earth's natural ocean and
  land "sinks" varies dramatically from year to
  year.
• These sinks help limit global warming.
• The Orbiting Carbon Observatory will help
  scientists better understand what causes this
  variability and whether natural absorption will
  continue, stop or even reverse.

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Landsat 5 set Guinness World Records title -
Longest-operating Earth observation satellite

• Outliving its three-year design life, Landsat 5
  delivered high-quality, global data of Earth's
  land surface for 28 years and 10 months.NASA
  launched Landsat 5 from Vandenberg Air Force base
  in Lompoc, Calif. on March 1, 1984. Landsat 5 was
  designed and built at the same time as Landsat 4
  and carried the same two instruments the
  Multispectral Scanner System (MSS) and the
  Thematic Mapper (TM).

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Midterm

• Overview History of Remote Sensing
• 2_intro_history.ppt
• Energy
• Sources Radiation Principles
• Interactions in the Atmosphere and at the Surface
• 3_energy.ppt
• 4LK_pg1-12.ppt
• 5_atmosphere.ppt
• 6_spectralsigs.ppt
• Data acquisition/Characteristics of R.S. Systems
• 7_sensors.ppt
• Landsat Program
• 8_landsat.ppt

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Materials from weeks 1 to 4

• Primary Text Online fundamentals of Remote
  Sensing
• Section 1 2
• LK Handout (1 2)
• Other Links
• Supplementary online text Remote Sensing
  Tutorial.
• Videos, Glossary
• Exercise 1 and 2
• Quiz 1 and 2

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How do we extract meaningful information from
imagery?

• 6. Interpretation and Analysis (F) - the
  processed image is interpreted, visually and/or
  digitally, to extract information about the
  target which was illuminated.

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7 elements of the remote sensing process.

• 1. Energy Source or Illumination (A) the first
  requirement for remote sensing is to have an
  energy source which illuminates or provides
  electromagnetic energy to the target of interest.
• 2. Radiation and the Atmosphere (B) as the
  energy travels from its source to the target, it
  will come in contact with and interact with the
  atmosphere it passes through. This interaction
  may take place a second time as the energy
  travels from the target to the sensor.
• 3. Interaction with the Target (C) - once the
  energy makes its way to the target through the
  atmosphere, it interacts with the target
  depending on the properties of both the target
  and the radiation.

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• 4. Recording of Energy by the Sensor (D) - after
  the energy has been scattered by, or emitted from
  the target, we require a sensor (remote - not in
  contact with the target) to collect and record
  the electromagnetic radiation.
• 5. Transmission, Reception, and Processing (E) -
  the energy recorded by the sensor has to be
  transmitted, often in electronic form, to a
  receiving and processing station where the data
  are processed into an image (hardcopy and/or
  digital).
• 7. Application (G) - the final element of the
  remote sensing process. apply the information we
  have been able to extract from the imagery in
  order to better understand the target we are
  studying.

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How do we extract meaningful information from
imagery?

• 6. Interpretation and Analysis (F) - the
  processed image is interpreted, visually and/or
  digitally, to extract information about the
  target which was illuminated.

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• Interpretation and analysis r.s. of imagery - the
  identification and/or measurement of various
  targets in an image to extract useful
  information.
• Much interpretation and identification of targets
  is performed manually or visually, by a human
  interpreter.
• done using imagery displayed in a photograph-type
  format
• independent of what type of sensor was used to
  collect data

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• Images are represented in a computer as arrays
  of pixels, with each pixel corresponding to a
  digital number, representing the brightness level
  of that pixel in the image.
• digital format.

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• Both analog and digital imagery can be displayed
  as black and white (monochrome) images, or as
  color by combining three bands representing
  different wavelengths.

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Analog and Digital Images

• Image - two-dimensional representation of objects
  in a real scene.
• representations of parts of the earth surface as
  seen from altitude.
• Images may be analog or digital.
• Aerial photographs - examples of analog images
• Satellite images - acquired using electronic
  sensors are examples of digital images.

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4.3 Digital Image Processing

• R. S. data are recorded in digital format, so
  virtually all image interpretation and analysis
  involves some element of digital processing.
• Need appropriate hardware and software to
  process data.
• Several commercially available remote sensing
  image processing and analysis software systems
  exist.

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Image processing software
Erdas Imagine
ENVI
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4.3 Digital Image Processing

• Common image processing image analysis functions
• A. Preprocessing
• B. Image Enhancement
• C. Image Transformation
• D. Image Classification and Analysis

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A. Preprocessing - operations normally required
prior to main data analysis extraction of
information.

• radiometric corrections
• correcting data for sensor irregularities,
  sensor or atmospheric noise
• converting data to accurately represent
  reflected/emitted radiation measured by sensor.
• Typically done before we get data.

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A. Preprocessing - operations normally required
prior to main data analysis extraction of
information.

• geometric corrections
• correcting distortions due to sensor-Earth
  geometry variations
• conversion of data to real world coordinates
  (e.g. latitude and longitude).

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B. Image enhancement - improve the appearance of
the imagery to assist in visual interpretation
and analysis.

• contrast stretching - increase the tonal
  distinction between various features in a scene.
• spatial filtering - enhance (or suppress)
  specific spatial patterns in an image.

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Contrast stretching

• Raw imagery - data populates only a small portion
  of the available range of digital values
  (commonly 8 bits or 256 levels).
• Change the original values so that more of the
  available range is used, thereby increasing the
  contrast between targets and their backgrounds.

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Contrast stretching

• Image histogram - a graphical representation of
  the brightness values that comprise an image.
• brightness values (i.e. 0-255) displayed along
  x-axis of graph
• frequency of occurrence of values shown on y-axis.

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Spatial filtering

• Highlight or suppress specific features in an
  image based on spatial frequency.
• Spatial frequency - variations in tone that
  appear in an image.
• High spatial frequency - rough" textured areas
  of an image - changes in tone are abrupt over a
  small area.
• Low spatial frequency - "smooth" areas with
  little variation in tone over several pixels.

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C. Image transformations - processing of data
from multiple spectral bands.

• Arithmetic operations (i.e. subtraction,
  addition, multiplication, division) are performed
  to combine and transform the original bands into
  "new" images which better display or highlight
  certain features in the scene.
• spectral or band ratioing
• principal components analysis

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Spectral ratioing - one of the most common
transforms applied to an image.

• Ratioing data from different spectral bands.
• resultant image enhances variations in the slopes
  of the spectral reflectance curves between the
  two different spectral ranges that may otherwise
  be masked by the pixel brightness variations in
  each of the bands.

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Vegetation Indices
Normalized Difference Vegetation Index
(NDVI) Used to map global primary production and
is computed
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Common band ratios
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D. Image classification - digitally identify
classify pixels.

• usually performed on multi-band data sets (A)
• assigns each pixel in an image to a particular
  class or theme (B) based pixel brightness values.

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Interpretation Classification

• Classifying features into meaningful categories
  or classes.
• Image then becomes a thematic map
• Unsupervised classification - features separated
  solely on spectral properties
• Supervised classification - some prior or
  acquired knowledge of classes

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Spectral Signatures of 4 Materials
Band 1 0.55 um
Band 2 0.85 um
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false color composite
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A thematic map is designed to show a particular
theme connected with a specific Geographic area
Unsupervised Classification
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Supervised Classification

• Ground truth
• Select training sites
• after unsupervised classification
• prior to supervised classification

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Field Instruments

• Detailed spectral signatures using
• spectrometers, spectrophotometers, and
  radiometers
• laboratory, field, aircraft
• Define reference signatures

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Training Sites

• Determine Land Cover/Use categories to classify
  (map) a scene, using space observations, assisted
  by other information sources.
• Select sites in sufficient number, size and
  shape, variety, and distribution to maximize
  accuracy of classification.

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