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Title: PRE-PROCESSING IN IMAGE ANALYSIS OF SATELLITE DATA


1
PRE-PROCESSING IN IMAGE ANALYSIS OF SATELLITE DATA
By Jwan M. Aldoski Geospatial Information
Science Research Center (GISRC), Faculty of
Engineering, Universiti Putra Malaysia, 43400
UPM Serdang, Selangor Darul Ehsan. Malaysia
2
Processing Satellite Imagery
  • When the first satellite, Sputnik, was launched
    in 1957 no one could have foreseen how its
    diverse its use would become. Today, we have
    Direct TV, On-Star, XM Radio and live
    up-to-the-second television coverage from every
    corner of the world. Today, satellite information
    is being relayed back to earth every second of
    every day. Before Sputnik had completed it first
    orbit it had relayed the first data back to
    earth. And it was not the "oldies" station on XM
    Radio. It was environmental data. More than forty
    years later, the use of satellite imaging
    continues as the most popular provider of
    environmental monitoring. With recent demands for
    new levels of data we are presented with the
    problem of how to manipulate our new raw
    satellite images so that these images can be
    integrated with pre-existing environmental
    observations and methods.

3
  • In order to retrieve, manipulate and process raw
    satellite images we make use of commercial
    computer software, in particular ENVI
    (ENnvironment for Visualizing Images) written in
    IDL (Interactive Data Language).ENVI is used for
    data visualization and analysis of satellite
    images. With a full understanding of IDL and the
    use of key components of the Interactive Data
    Language, we are able to customize, compose and
    modify algorithms. This allows us to prompt and
    direct ENVI to meet our specific needs and
    tailor, to our needs, the processing of the
    satellite data.

4
  • Satellite data comes from the SeaStar, a polar
    orbiting satellite launched in 1997, which
    carries the SeaWiFS (Sea-viewing Wide
    Field-of-view Sensor) sensor. The SeaStar
    satellite travels at an altitude of about 1000 km
    above the Earth. It travels pole to pole in
    ninety-nine minutes. SeaWiFS is an eight-channel
    sensor sensing radiation in the range of
    0.402-0.885 m m with a swath width of 2800-km.
    Radiation sensed by the eight channels of SeaWiFS
    comes from four sources air (gas) scattering and
    absorption, aerosol scattering and absorption,
    cloud reflectance and surface reflectance
    (Fig.1).

5
The four sources of radiation sensed by SeaWiFS
6
  • Satellite imagery used in the detection of change
    along coastlines is processed in a standardized
    fashion to ensure temporal, spatial, and spectral
    compatibility between scenes. Imagery is
    initially selected to correlate as closely as
    possible with season and time-of-year coincident
    with high biomass and favorable atmospheric
    conditions as appropriate per region.

7
Processing Steps
  • Spot satellites can transmit image data to the
    ground in two ways, depending on whether or not
    the spacecraft is within range of a receiving
    station. As the satellite proceeds along its
    orbit, four situations arise concerning imagery
    acquisition and image data transmission to
    ground.

8
Processing Steps
  • The satellite is within range of a Direct
    Receiving Station (DRS), so imagery can be
    down-linked in real-time provided both satellite
    and DRS are suitably programmed.
  • The satellite is not within range of a Spot DRS.
    Programmed acquisitions are executed and the
    image data stored on the onboard recorders.
  • The satellite is within range of a main receiving
    station (Kiruna or Toulouse). It can thus be
    programmed either to down-link image data in
    real-time or play back the onboard recorders and
    transmit image data recorded earlier during the
    same orbital revolution.
  • The rest of the time, the satellite is on standby
    ready to acquire imagery in accordance with
    uplinked commands.

9
Processing Steps
  • Once data has been transmitted, the SPOT images
    undergo preprocessing operations (for SPOT data,
    the term "processing" is used only in terms of
    data manipulations undertaken by end-users).
  • The data transmissions are demodulated,
    synchronized and simultaneously recorded onto two
    high-density data tapes (HDDTs). One of the HDDTs
    is used as an archive master while the other HDDT
    acts as a backup for the master tape.

10
Processing Steps
  • A SPOT satellite data-collection pass lasting
    approximately 10 minutes with a constant viewing
    configuration yields two data segments with each
    segment containing approximately 75 scenes. This
    yield represents use of either one HRV set to
    dual mode or use of both HRVs in single mode. The
    size of individual scenes varies.

11
Processing Steps
  • The SPOT scenes are defined by the following
    additional preprocessing characteristics only
    when there have been user requests for the
    scenes
  • Preprocessing level
  • Computer compatible tape (CCT) or film

12
Satellite Image Aquisition and Pre-processing
  • Two Landsat-TM images and one ERS-1 SAR scene
    were used in this study. These were already
    available within the JRC archive, as they have
    been used in a previous study within the EMAP
    Unit. As such, there were no costs involved,
    specific to this project, with regard to image
    acquisition and pre-processing.

13
Image Pre-processing
  • Preprocessing of satellite images prior to
    image classification and change detection is
    essential. Preprocessing commonly comprises a
    series of sequential operations, including
    atmospheric correction or normalization, image
    registration, geometric correction, and masking
    (e.g., for clouds, water, irrelevant features)

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Image Pre-processing
  • The normalization of satellite imagery takes into
    account the combined, measurable reflectances of
    the atmosphere, aerosol scattering and
    absorption, and the earths surface. It is the
    volatility of the atmosphere which can introduce
    variation between the reflectance values or
    digital numbers (DNs) of satellite images
    acquired at different times. Although the effects
    of the atmosphere upon remotely sensed data are
    not considered errors, since they are part of the
    signal received by the sensing device,
    consideration of these effects is important. The
    goal conveniently should be that following image
    preprocessing, all images should appear as if
    they were acquired from the same sensor.

15
Satellite image rectification
  • The goal of image rectification is to facilitate
    the overlay of additional imagery and other
    geographic data sets. A standard map area, with
    boundaries set in UTM, is established for each
    scene, thus all image files for the same region,
    once rectified, will occupy the same map area.
    The UTM bounds for the scene are established
    according to the file size, the 28.5 x 28.5 m
    pixels, and the minimum/maximum northing and
    easting required to contain the full scene area.
    These boundaries, the UTM zone and the ellipsoid
    are established on each newly- created empty
    file.

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Geometric Rectification
  • Geometric rectification of the imagery resamples
    or changes the pixel grid to fit that of a map
    projection or another reference image. This
    becomes especially important when scene to scene
    comparisons of individual pixels in applications
    such as change detection are being sought.

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Geometric Rectification
                                               
                                

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Subset of Study Area
  • In some cases, Landsat TM scenes are much larger
    than a project study area. In these instances it
    is beneficial to reduce the size of the image
    file to include only the area of interest. This
    not only eliminates the extraneous data in the
    file, but it speeds up processing due to the
    smaller amount of data to process. This is
    important when utilizing multiband data such as
    Landsat TM imagery. This reduction of data is
    known as subsetting. This process cuts out the
    preferred study area from the image scene into a
    smaller more manageable file.

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Subset of Study Area
                                               
                                                  
                                                  
                 

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Subset of Study Area
  • A Landsat TM image is 115 miles (185 kilometers)
    wide by 106 miles (170kilometers) long and has a
    total area of 12,190 square miles or 31,450
    square kilometers.
  • Carroll County has an area of approximately 641
    square miles. In order to subset the study area
    from each of the four Landsat scenes, a vector
    file defining the county boundary with the same
    georeferenced coordinates as the Landsat images,
    UTM Zone 15, NAD27, was imported into PCI
    Imageworks. The county boundary vector file was
    converted to a binary bitmap mask and overlaid on
    to each of the TM scenes. The county mask acts as
    a virtual cookie-cutter and subsets the study
    area similar to the previous figure.

21
Preprocessing Procedure
  • Before the creation of the minimum images,
    preprocessing must occur. The pre-processing
    procedure consists of six steps (1) collection
    (2) downloading (3) unzipping twice (4)
    executing the preprocessing algorithms through
    the ENVI software (5) checking the final
    preprocessed images, and (6) executing the patch
    procedure, where necessary.

22
Preprocessing Procedure
  • Once this preprocessing procedure is complete,
    an image containing clouds, surface reflectance
    and aerosol reflectance is created

23
Final preprocessing JPEG image consisting solely
of clouds, surface reflectance and aerosol
reflectance.
.
  •  

24
Image processing
  • Once the raw remote sensing digital data has been
    acquired, it is then processed into usable
    information. Analog film photographs are
    chemically processed in a darkroom whereas
    digital images are processed within a computer.
    Processing digital data involves changing the
    data to correct for certain types of distortions.
    Whenever data is changed to correct for one type
    of distortion, the possibility of the creating
    another type of distortion exists. The changes
    made to remote sensing data involve two major
    operations preprocessing and postprocessing.

25
Preprocessing
  • The preprocessing steps of a remotely sensed
    image generally are performed before the
    postprocessing enhancement, extraction and
    analysis of information from the image.
    Typically, it will be the data provider who will
    preprocess the image data before delivery of the
    data to the customer or user. Preprocessing of
    image data often will include radiometric
    correction and geometric correction.

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Radiometric corrections
  • Radiometric corrections are made to the raw
    digital image data to correct for brightness
    values, of the object on the ground, that have
    been distorted because of sensor calibration or
    sensor malfunction problems. The distortion of
    images is caused by the scattering of reflected
    electromagnetic light energy due to a constantly
    changing atmosphere. This is one source of sensor
    calibration error.

28
Geometric corrections
  • Geometric corrections are made to correct the
    inaccuracy between the location coordinates of
    the picture elements in the image data, and the
    actual location coordinates on the ground.
    Several types of geometric corrections include
    system, precision, and terrain corrections.
  • System correction uses a geographic reference
    point for a pixel element such as that provided
    by the global positioning system. Correction
    accuracy often varies depending upon the accuracy
    of the position given by the global positioning
    system.  Aircraft platform system instability is
    shown in the first figure. Preprocessing
    correction removes the motion distortion as shown
    in second figure.

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.                                              
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Data Processing, Interpretation and Analysis
  • Remote sensing data available in pictorial or
    digital form need to be interpreted to derive
    meaningful information. To interpret the remote
    sensing data, knowledge of the spectral
    reflectance signature of various objects on the
    earth is essential. The data can be interpreted
    either visually, digitally or both. Image
    interpretation and analysis is beyond the scope
    of this guide here we focus on image processing,
    enhancement, georeferencing and categorization.

32
Data Processing, Interpretation and Analysis
  • Before images can be analyzed, some degree of
    pre-processing is necessary to correct for any
    distortion inherent in the images due to the
    characteristics of imaging system and conditions.
    Commonly used pre-processing procedures include
    radiometric correction, geometric correction and
    atmospheric correction.

33
Data Processing, Interpretation and Analysis
  • Once pre-processing is completed, images can be
    enhanced to improve the visual appearance of the
    objects on the image. Commonly used image
    enhancement techniques include image reduction,
    image magnification transect extraction,
    contrast adjustments, band ratioing, spatial
    filtering, Fourier transformations, principal
    components analysis, and texture transformation
    These are all used to extract useful information
    that assists in image interpretation.

34
Data Processing, Interpretation and Analysis
  • For both visual image interpretation and digital
    image processing, the availability of secondary
    data and knowledge of the analyst are extremely
    important. The visual interpretation can be done
    using various viewing and interpretation devices.
    Most commonly used elements of visual analysis
    are tone, color, size, shape, texture, pattern,
    height, shadow, site and association of the
    object under investigation. Digital image
    processing relies primarily on the radiance of
    image picture elements (pixels) for each band.
    Radiance is then translated into digital numbers
    (DNs), or gray scale intensity, for example from
    0 (lowest intensity, or black) to 255 (highest
    intensity, or white). A DN for a specific band
    will indicate the intensity of the radiance at
    that wavelength.

35
Data Processing, Interpretation and Analysis
  • Georeferencing is the process of taking the image
    in its raw format (rows and columns of data) and
    linking it to the land that it covers. Images are
    georeferenced by linking spatially distributed
    control points in the satellite image to points
    on base maps or points referenced in the field
    through global positioning systems. The raster
    data in the image is thereby registered to a
    Cartesian coordinate system, and can be combined
    with other georeferenced data sets in a
    geographic information system.

36
Data Processing, Interpretation and Analysis
  • For many purposes, data that is collected from
    the earths surface, which represents a continuous
    variation, needs to be categorized. Pixels with
    similar spectral signatures are grouped together
    in a process known as image classification.
    Supervised classification entails telling the
    software what a certain pixel represents, such as
    boreal forest, and then having the computer
    classify every pixel with a similar spectral
    signature as boreal forest. To undertake
    supervised classification, it is necessary to
    collect training samples that relate ground cover
    to spectral signatures for a given geographic
    location. In unsupervised classification, the
    analyst specifies the desired number of classes,
    and the computer automatically sorts the pixels.
    For an example of supervised classification, see
    Figure in the next slide.

37
Data Processing, Interpretation and Analysis
  • The output of remote sensing data analysis can be
    presented in a variety of ways including a
    printout of the enhanced image itself, an image
    map, a thematic map (e.g. land use map), a
    spatial database, summary statistics and/or
    graphs. The output data can be integrated with a
    geographic information system (GIS) database for
    further analysis.

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We start from scanned maps. Later this can be
extended to satellite images.
41
The color channels are decomposed, but instead of
RGB, CMY is used. From the components we use the
yellow channel, beacuse it is easy to detect sea,
which is blue, so having only a little yellow
component.
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In the last step the coastline is detected, using
the following algorithms -Box filtering
-Robert's gradient -Tresholding
                                                 
                                                  
                 

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