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Raw imagery is calibrated and gain
adjusted to correct for known
radiance response characteristics of
the camera sensor system.
Automatic update of calibration
table
Filtering of transmission
channel and other noises
TLE changing; Time correction;
Roll, Pitch, Yaw correction.
Calibration
Ground
Receiving
Station
Autocalibration
Level 0
Satellites
Preprocessing
Database
record
Calibration
table
DB
Unpacking, rastering, assembling.
Raw imagery is provided in
completely unprocessed format just
as it is received from the satellite.
Level 0 A
Preprocessed and
calibrated raw imagery
Level 1 A
Automatic recording of 1A,1B images
to archive. Image passport is used for
automatic entry of DB fields.
Archiving
Calibration
table
Based on known sensor or acquisition
(ephemeris). Applied to compensate for
camera optics and scanning distortions.
Geometric
correction
Both radiometric and
geometrically corrected images
Level 1 B
Precision Correction, Map Projected,
Orthorectified, Multi-layer Synthesed,
etc. – on particular customer’s request.
Value Added
Processing
Database
query
 Special 0-A level preprocessing: assembling of image rulers
 There are three PAN objectives on IRS 1C/1D satellite, which give
partially overlapped and displaced image fragments.
 We have developed robust algorithms for automatic and exact
mutual referencing of PAN. Can you recognize where the parts of
images where snapped together?
 The same technology can be used for registering of such
fragments as well as for registering of near-by circuit images.
 Preprocessing
– We have developed robust algorithms for preprocessing in order to
filter impulse and noises appearing during receiving of images. No any
pixel lost but noise. Moreover, we do not replace “salt and pepper”
noise with nearest neighbor approximation – we substitute a true
value, which has been transmitted but noised.
– Full automatic procedures are the plug-ins for Centaurus.
– Use advanced noise analysis with built-in analysis tools
 Samples of standard preprocessing:
IRS 1C / 1D PAN
Meteor - 3
 Some special cases of preprocessing
IRS 1C / 1D LISS
Missed columns
 Some special cases
of preprocessing
Okean – O
Noisy strips
 Raw imagery is calibrated and gain adjusted to correct for known
radiance response characteristics of the camera sensor system.
 Most of satellite imagery suppliers provide users with calibration table
for this (if you don’t have it – no problem: we can restore it for you if
you have both initial and calibrated image).
 To overcome this problem we have developed algorithms for automatic
iterative update of calibration tables (so called autocalibration).
 Nevertheless, even the images adjusted with such tables are not
enough homogeneous. It can be detected even visually on naturally
homogeneous areas for various satellite images, such as, for example,
6 bit/channel IRS 1C/1D PAN, 8 bit/channel Meteor – 3 and even 11
bit/channel EROS-A.
 IRS 1C / 1D PAN
a) Initial 6 bit/channel, not
calibrated, so very
stripy (highlighted).
Profile on the most smooth part of image
(water surface): initial (white), calibrated
(red) and autocalibrated (blue)
b) Calibrated 8 bit/channel with the
table provided by imagery supplier
(highlighted). Less stripy.
c) Autocalibrated.
Most smooth.
 Meteor – 3, channel 1
b) No calibration table
has been provided.
a) Initial 8 bit/channel, not calibrated,
so very stripy (highlighted).
Profile on the most smooth part of
image (water surface): initial (red)
and autocalibrated (blue)
c) Autocalibrated
and adjusted.
 EROS-A a) No 0A level image has been supplied.
b) 1A 11 bit/channel image calibrated by
imagery provider (highlighted). Looks
stripy a little.
Profile on the most smooth part of
image (water surface): initial (red)
and autocalibrated (blue)
c) Autocalibrated and adjusted.
 After radiometric correction including satellite- and GRS-
specific denoising and radiometric calibration you obtain 1A-
level product, i.e. radiometric corrected satellite images.
 You can collect your standard scenes into archive with
advanced search abilities and provide to customers on their
requests. Another parts of information to be supplied with
images are: image passport and (optionally) calibration table,
camera model, telemetry information, etc.
 Image passport can be used for rough geometric
transformation of image to develop 1B-level product. Such
kind of image registration accuracy (which is, of course,
different for different satellite) is not enough for solving of
such precision problems as, for example, land cadastre, but
enough, say, for geographic search, for visual observation and
analysis, etc.
 See sample EROS-A geometrically calibrated with Image
Referencing and Registration (IRR) module (a part of Centaurus).
 1A- and 1B-level products can be used as the background or
initial information source for advanced image analysis and
processing. Various value-add products are the result of such
processing, depending on space images nature, i.e. spectrum
band, spatial resolution, etc., and on the nature of the
problems to be solved.
 For example, see below some standard add-value problems:
 Precise geometric correction with ground control points (GCP)
 Orthorectification (with digital elevation model - DEM)
 Calculation of derivative values, such as vegetation indexes (NDVI, EVI)
 Thematic classification and segmentation
 Recognition of small-dimension objects and corresponding targeting
 Mapping and updating of existed maps
 More (for agriculture, forestry, ecology, geology, etc.)
 Next slides will demonstrate solving of sample add-value
problems with Centaurus:
 We can register together pan-chrome images, multispectral
images, SAR images and digital maps to create original initial
data for next step thematic processing and to update mapping
data. Such images can be used for better visualization,
increasing of comprehension and for future steps – thematic
processing and mapping.
Common coordinate system.
The set of registering points
and checking points.
Referencing
Multi-layer
image
PAN
Geometric correction.
Common geographic
projection
Registration
DB
LISS
Digital map
More…
(SAR, another satellite
images, etc.)
DB
query
Creation of multi-layer image. Chose
of pseudo-colors and transparency
modes.
Synthesis
 Next slide reflect the fragment of Kiev-2002 poster (A0 format)
(front and down window). The initial data are IRS 1C-1D
images: PAN channel (0.5 – 0.75 µm, 6 meters per pixel
resolution, left back window) and LISS image (0.52-0.59 µm,
0.62-0.68 µm, 0.77-0.86 µm; 23 meters per pixel resolution,
right back window). The aim of synthesis was not to degrade
resolution 6 m per pixel and add complete of the image with
multi-spectral information. We did an exact registering of these
images. So, you can visually and naturally recognize such
information layers as, for example, forest (dark green),
hydrography (blue on deep water, violet on sandbacks and
swamps), meadows and fields (gradations of light blue). The
streets, buildings, bridges and other urban objects are also
recognizable.
Original Centaurus’ modules as well
as the external ones can be used
for segmentation
VectoringSegmentation
Vector map
(*.mif/mid,
*.shape)
Creation of multi-layer vector
images. Filtering, selection and
homogenization
Vector
analysis and
processing
Histogram analysis, statistical
analysis, Fourier, wavelet, external
analysis tools
Raster image
analysis
Saving map in GIS format
(including DB of attributive
parameters).
Mapping
Initial multi-layer
raster image
Creation of the report on
recognition results
Reporting
Report
(*.xls, *.doc)
 We can use Centaurus and its modules for solving a wide range of
classification problems. The results of segmentation will be easy vectorized
and used for creation or updating of digital maps. The results will be saved
as standard GIS formats. Centaurus can also automatically create reports
on recognition result (MS Excel or Word or any other output file). It can be
used, for example, for creation and correction of the following digital map
layers: hydrographic, buildings, roads, streets, railways, forestry, etc.
 Automated vectoring. Different types of soils bordered with
bold lines on initial raster image. Finally we obtain a vector
map with corresponding layers; some database fields such as
region square, etc. can be filled automatically.
 Investigation and mapping of coniferous and deciduous forest.
Left window contains initial vector map to be updated (blue
polygons) and updated layer (red polygons); initial LANDSAT
image (right window) uses for segmentation. Each kind of
wood to be recognized, segmented, vectored and saved into
separate vector layer.
 Investigation and mapping of disafforestation. Initial 1:15000
map to be upgraded (right bottom window); initial IRS PAN
image (right top window); updated disafforestation vector
layers (left vector window): both old (red) and updated (blue).
Image statistics window reflects distribution of area for
selected areas. Report could reflect, for example, total
disafforestation square, separately for each sort of wood and
estimated volume of timber.
 Problem statement:
 Detect the region of interest (ROI) where the objects (here –airplanes, but we
can recognize actually any object types) can be found to make their search
and recognition easier and faster.
 Recognize objects automatically (i.e. create a table – the report of image
interpretation – and a vector map which both contains the information about
object coordinates and object type).
 Has been done:
1. ROI has been detected as a landing strip and other places where the
airplanes could be placed (not to try finding them between buildings). It’s
mostly manual work; nevertheless, we don’t assume it a problem as far as it
must be done once for each airport.
2. We used a set of image segmentation methods on ROI to detect the objects
on it. The result is: set of segmented object of interest. We assume it as the
solution of first problem, i.e. each segmented object must be investigated
and recognized. (to be continued)
User investigates the image areas
where segmented objects found
Investigation of initial image to
chose methods of processing.
Mostly supervised for 1-st image
Import Image
Analysis
Centaurus
Supervised
Recognition
Initial Satellite
image
Segmented
Image
Preparing of image for
segmentation (adaptive filtering,
edge detection, etc.) Automated
Preprocessing
Detection of the region of
interest. Manual (for 1-st image in
series)
Detection of ROI
Report
Creation of
report
Creation of the report on recognition
results - supervised
Reporting
Detection of particular objects on
raster image. Various methods
can be used. Automated
Segmentation
Raster Image
ROI
 Has been done:
3. We used the methods of advanced vector analysis for automated
recognition of the segmented objects. We have detected visually 7 types of
objects, so the aim was to recognize automatically the same.
4. The results have been saved as MapInfo tables (in non-earth coordinates).
Centaurus has also created the MS Excel report on recognition result. Note
that no real airplane models were used (neither supplied by you nor another
ones; the names of recognized airplanes are very relative – just to indicate
which objects are different and which ones are similar for Centaurus).
Creation of vector objects of various
derivative types from initial ones.
Automated and extra fast
Result of previous
processing step
Segmented
Raster Image
Centaurus
Transforms
Automatic and extra fast
converting of segmented raster
image to vector form
Vectoring
Filtering of wrong objects.
Automated
Initial
preselection
Report
Export of
result
Creation of vector objects of
secondary derivative types.
Automated
Processing
and analysis
Vector Image
Measurement of objects’
parameters and their classification
in the attribute space. Automated
Measurement
and selection
All recognized object collected at
corresponding layers and
transformed to true view. Automated
Recognition
and post-
processing
Vector
map
Creation of
report
 Has been done:
5. Final result is a vector map with the following layers:
• Landing strip (large and multiply connected area object)
• Airplanes as symbols with corresponding coordinates.
 Design and development of centers for mapping and remote
sensing from alpha to omega including structure design, supply
of soft- and hardware, delivery, installation, start-and-adjustment
and support.
 Custom software development for mapping and satellite image
processing based on Centaurus including new customized
modules of any destination.
 Software delivery and support.
 Participation in projects on image processing including remote
sensing and mapping.
 Technical and business consulting.
 Users training (for example, cartographers or interpretators).
 Solving of various problems in the fields of image processing,
pattern recognition, compression, computer geometry, flight
simulation and other – any complexity.
 Custom software development in other fields.
• Customer Oriented Product Development &
Implementation Cycle
• Modern Design & Development Methods
• Comprehensive Subject Area Expertise
& General System Approach
• Competitive Rates
• Reliable Quality Assurance
• Extensive Software Design & Development
Experience
• Long Term Partnership Focus
• Security and Non-disclosure Agreements
• Guarantee of High Quality
• Our Address:
9/12 Baumana Street
Office #33
03190, Kiev, Ukraine
• Phone: +380 (44) 459 6062, 442 6077, 443 0155
• Fax: +380 (44) 459 6062
• http://www.pworlds.com
• http://vnm.pworlds.com
• Contact Person:
Mr. Victor Yu. Chekh, General Manager
E-Mail: chekh@pworlds.com
 Centaurus and IRR are the copyrights of Parallel Worlds,
Kiev, Ukraine
 Windows and VBA are the copyrights of Microsoft Corp.,
USA
 EROS-A and corresponding images are the copyrights of
ImageSat International, Israel
 IRS 1C/1D and corresponding images are the copyrights
of ANTRIX, Space Imaging Inc.
 Okean-O and corresponding images are the copyrights of
National Space Agency of Ukraine (NSAU)
 Meteor-3 and corresponding images are the copyrights of
Russian Space Agency (RKA)
Centaurus satellite.PPT

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Centaurus satellite.PPT

  • 1.
  • 2. Raw imagery is calibrated and gain adjusted to correct for known radiance response characteristics of the camera sensor system. Automatic update of calibration table Filtering of transmission channel and other noises TLE changing; Time correction; Roll, Pitch, Yaw correction. Calibration Ground Receiving Station Autocalibration Level 0 Satellites Preprocessing Database record Calibration table DB Unpacking, rastering, assembling. Raw imagery is provided in completely unprocessed format just as it is received from the satellite. Level 0 A Preprocessed and calibrated raw imagery Level 1 A Automatic recording of 1A,1B images to archive. Image passport is used for automatic entry of DB fields. Archiving Calibration table Based on known sensor or acquisition (ephemeris). Applied to compensate for camera optics and scanning distortions. Geometric correction Both radiometric and geometrically corrected images Level 1 B Precision Correction, Map Projected, Orthorectified, Multi-layer Synthesed, etc. – on particular customer’s request. Value Added Processing Database query
  • 3.  Special 0-A level preprocessing: assembling of image rulers  There are three PAN objectives on IRS 1C/1D satellite, which give partially overlapped and displaced image fragments.  We have developed robust algorithms for automatic and exact mutual referencing of PAN. Can you recognize where the parts of images where snapped together?  The same technology can be used for registering of such fragments as well as for registering of near-by circuit images.
  • 4.  Preprocessing – We have developed robust algorithms for preprocessing in order to filter impulse and noises appearing during receiving of images. No any pixel lost but noise. Moreover, we do not replace “salt and pepper” noise with nearest neighbor approximation – we substitute a true value, which has been transmitted but noised. – Full automatic procedures are the plug-ins for Centaurus. – Use advanced noise analysis with built-in analysis tools
  • 5.  Samples of standard preprocessing: IRS 1C / 1D PAN Meteor - 3
  • 6.  Some special cases of preprocessing IRS 1C / 1D LISS Missed columns
  • 7.  Some special cases of preprocessing Okean – O Noisy strips
  • 8.  Raw imagery is calibrated and gain adjusted to correct for known radiance response characteristics of the camera sensor system.  Most of satellite imagery suppliers provide users with calibration table for this (if you don’t have it – no problem: we can restore it for you if you have both initial and calibrated image).  To overcome this problem we have developed algorithms for automatic iterative update of calibration tables (so called autocalibration).  Nevertheless, even the images adjusted with such tables are not enough homogeneous. It can be detected even visually on naturally homogeneous areas for various satellite images, such as, for example, 6 bit/channel IRS 1C/1D PAN, 8 bit/channel Meteor – 3 and even 11 bit/channel EROS-A.
  • 9.  IRS 1C / 1D PAN a) Initial 6 bit/channel, not calibrated, so very stripy (highlighted). Profile on the most smooth part of image (water surface): initial (white), calibrated (red) and autocalibrated (blue) b) Calibrated 8 bit/channel with the table provided by imagery supplier (highlighted). Less stripy. c) Autocalibrated. Most smooth.
  • 10.  Meteor – 3, channel 1 b) No calibration table has been provided. a) Initial 8 bit/channel, not calibrated, so very stripy (highlighted). Profile on the most smooth part of image (water surface): initial (red) and autocalibrated (blue) c) Autocalibrated and adjusted.
  • 11.  EROS-A a) No 0A level image has been supplied. b) 1A 11 bit/channel image calibrated by imagery provider (highlighted). Looks stripy a little. Profile on the most smooth part of image (water surface): initial (red) and autocalibrated (blue) c) Autocalibrated and adjusted.
  • 12.  After radiometric correction including satellite- and GRS- specific denoising and radiometric calibration you obtain 1A- level product, i.e. radiometric corrected satellite images.  You can collect your standard scenes into archive with advanced search abilities and provide to customers on their requests. Another parts of information to be supplied with images are: image passport and (optionally) calibration table, camera model, telemetry information, etc.  Image passport can be used for rough geometric transformation of image to develop 1B-level product. Such kind of image registration accuracy (which is, of course, different for different satellite) is not enough for solving of such precision problems as, for example, land cadastre, but enough, say, for geographic search, for visual observation and analysis, etc.  See sample EROS-A geometrically calibrated with Image Referencing and Registration (IRR) module (a part of Centaurus).
  • 13.  1A- and 1B-level products can be used as the background or initial information source for advanced image analysis and processing. Various value-add products are the result of such processing, depending on space images nature, i.e. spectrum band, spatial resolution, etc., and on the nature of the problems to be solved.  For example, see below some standard add-value problems:  Precise geometric correction with ground control points (GCP)  Orthorectification (with digital elevation model - DEM)  Calculation of derivative values, such as vegetation indexes (NDVI, EVI)  Thematic classification and segmentation  Recognition of small-dimension objects and corresponding targeting  Mapping and updating of existed maps  More (for agriculture, forestry, ecology, geology, etc.)  Next slides will demonstrate solving of sample add-value problems with Centaurus:
  • 14.  We can register together pan-chrome images, multispectral images, SAR images and digital maps to create original initial data for next step thematic processing and to update mapping data. Such images can be used for better visualization, increasing of comprehension and for future steps – thematic processing and mapping. Common coordinate system. The set of registering points and checking points. Referencing Multi-layer image PAN Geometric correction. Common geographic projection Registration DB LISS Digital map More… (SAR, another satellite images, etc.) DB query Creation of multi-layer image. Chose of pseudo-colors and transparency modes. Synthesis
  • 15.  Next slide reflect the fragment of Kiev-2002 poster (A0 format) (front and down window). The initial data are IRS 1C-1D images: PAN channel (0.5 – 0.75 µm, 6 meters per pixel resolution, left back window) and LISS image (0.52-0.59 µm, 0.62-0.68 µm, 0.77-0.86 µm; 23 meters per pixel resolution, right back window). The aim of synthesis was not to degrade resolution 6 m per pixel and add complete of the image with multi-spectral information. We did an exact registering of these images. So, you can visually and naturally recognize such information layers as, for example, forest (dark green), hydrography (blue on deep water, violet on sandbacks and swamps), meadows and fields (gradations of light blue). The streets, buildings, bridges and other urban objects are also recognizable.
  • 16. Original Centaurus’ modules as well as the external ones can be used for segmentation VectoringSegmentation Vector map (*.mif/mid, *.shape) Creation of multi-layer vector images. Filtering, selection and homogenization Vector analysis and processing Histogram analysis, statistical analysis, Fourier, wavelet, external analysis tools Raster image analysis Saving map in GIS format (including DB of attributive parameters). Mapping Initial multi-layer raster image Creation of the report on recognition results Reporting Report (*.xls, *.doc)  We can use Centaurus and its modules for solving a wide range of classification problems. The results of segmentation will be easy vectorized and used for creation or updating of digital maps. The results will be saved as standard GIS formats. Centaurus can also automatically create reports on recognition result (MS Excel or Word or any other output file). It can be used, for example, for creation and correction of the following digital map layers: hydrographic, buildings, roads, streets, railways, forestry, etc.
  • 17.  Automated vectoring. Different types of soils bordered with bold lines on initial raster image. Finally we obtain a vector map with corresponding layers; some database fields such as region square, etc. can be filled automatically.  Investigation and mapping of coniferous and deciduous forest. Left window contains initial vector map to be updated (blue polygons) and updated layer (red polygons); initial LANDSAT image (right window) uses for segmentation. Each kind of wood to be recognized, segmented, vectored and saved into separate vector layer.  Investigation and mapping of disafforestation. Initial 1:15000 map to be upgraded (right bottom window); initial IRS PAN image (right top window); updated disafforestation vector layers (left vector window): both old (red) and updated (blue). Image statistics window reflects distribution of area for selected areas. Report could reflect, for example, total disafforestation square, separately for each sort of wood and estimated volume of timber.
  • 18.  Problem statement:  Detect the region of interest (ROI) where the objects (here –airplanes, but we can recognize actually any object types) can be found to make their search and recognition easier and faster.  Recognize objects automatically (i.e. create a table – the report of image interpretation – and a vector map which both contains the information about object coordinates and object type).  Has been done: 1. ROI has been detected as a landing strip and other places where the airplanes could be placed (not to try finding them between buildings). It’s mostly manual work; nevertheless, we don’t assume it a problem as far as it must be done once for each airport. 2. We used a set of image segmentation methods on ROI to detect the objects on it. The result is: set of segmented object of interest. We assume it as the solution of first problem, i.e. each segmented object must be investigated and recognized. (to be continued)
  • 19. User investigates the image areas where segmented objects found Investigation of initial image to chose methods of processing. Mostly supervised for 1-st image Import Image Analysis Centaurus Supervised Recognition Initial Satellite image Segmented Image Preparing of image for segmentation (adaptive filtering, edge detection, etc.) Automated Preprocessing Detection of the region of interest. Manual (for 1-st image in series) Detection of ROI Report Creation of report Creation of the report on recognition results - supervised Reporting Detection of particular objects on raster image. Various methods can be used. Automated Segmentation Raster Image ROI
  • 20.  Has been done: 3. We used the methods of advanced vector analysis for automated recognition of the segmented objects. We have detected visually 7 types of objects, so the aim was to recognize automatically the same. 4. The results have been saved as MapInfo tables (in non-earth coordinates). Centaurus has also created the MS Excel report on recognition result. Note that no real airplane models were used (neither supplied by you nor another ones; the names of recognized airplanes are very relative – just to indicate which objects are different and which ones are similar for Centaurus).
  • 21. Creation of vector objects of various derivative types from initial ones. Automated and extra fast Result of previous processing step Segmented Raster Image Centaurus Transforms Automatic and extra fast converting of segmented raster image to vector form Vectoring Filtering of wrong objects. Automated Initial preselection Report Export of result Creation of vector objects of secondary derivative types. Automated Processing and analysis Vector Image Measurement of objects’ parameters and their classification in the attribute space. Automated Measurement and selection All recognized object collected at corresponding layers and transformed to true view. Automated Recognition and post- processing Vector map Creation of report
  • 22.  Has been done: 5. Final result is a vector map with the following layers: • Landing strip (large and multiply connected area object) • Airplanes as symbols with corresponding coordinates.
  • 23.  Design and development of centers for mapping and remote sensing from alpha to omega including structure design, supply of soft- and hardware, delivery, installation, start-and-adjustment and support.  Custom software development for mapping and satellite image processing based on Centaurus including new customized modules of any destination.  Software delivery and support.  Participation in projects on image processing including remote sensing and mapping.  Technical and business consulting.  Users training (for example, cartographers or interpretators).  Solving of various problems in the fields of image processing, pattern recognition, compression, computer geometry, flight simulation and other – any complexity.  Custom software development in other fields.
  • 24. • Customer Oriented Product Development & Implementation Cycle • Modern Design & Development Methods • Comprehensive Subject Area Expertise & General System Approach • Competitive Rates • Reliable Quality Assurance • Extensive Software Design & Development Experience • Long Term Partnership Focus • Security and Non-disclosure Agreements • Guarantee of High Quality
  • 25. • Our Address: 9/12 Baumana Street Office #33 03190, Kiev, Ukraine • Phone: +380 (44) 459 6062, 442 6077, 443 0155 • Fax: +380 (44) 459 6062 • http://www.pworlds.com • http://vnm.pworlds.com • Contact Person: Mr. Victor Yu. Chekh, General Manager E-Mail: chekh@pworlds.com
  • 26.  Centaurus and IRR are the copyrights of Parallel Worlds, Kiev, Ukraine  Windows and VBA are the copyrights of Microsoft Corp., USA  EROS-A and corresponding images are the copyrights of ImageSat International, Israel  IRS 1C/1D and corresponding images are the copyrights of ANTRIX, Space Imaging Inc.  Okean-O and corresponding images are the copyrights of National Space Agency of Ukraine (NSAU)  Meteor-3 and corresponding images are the copyrights of Russian Space Agency (RKA)