PRE-PROCESSING IN IMAGE ANALYSIS OF SATELLITE PICTURES

1
PRE-PROCESSING IN IMAGE ANALYSIS OF SATELLITE
PICTURES

• BEGÜM KÜLTÜR
• 110010205

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)

14
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.

16
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.

17
Geometric Rectification

                                               
                                
18
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.

19
Subset of Study Area
                                               
                                                  
                                                  
                 

20
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.

26
(No Transcript)
27
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.

29

• 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.

30
.                                              
31
(No Transcript)
32
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.

33
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.

34
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.

35
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.

36
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.

37
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.

38
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.

39
(No Transcript)
40
(No Transcript)
41
We start from scanned maps. Later this can be
extended to satellite images.
42
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.
43
In the last step the coastline is detected, using
the following algorithms -Box filtering
-Robert's gradient -Tresholding

                                                 
                                                  
                 
44
(No Transcript)