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 m, 6 meters per pixel resolution, left back window) and LISS image (0.52-0.59 m, 0.62-0.68 m, 0.77-0.86 m; 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

Vectoring Segmentation

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 StreetOffice #3303190, 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 ManagerE-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)