Orthorectification and target analysis of RADARSAT-2 …cradpdf.drdc-rddc.gc.ca/PDFS/unc223/p803619_A1b.pdf · Orthorectification and target analysis of RADARSAT-2 data from the 3CMV

Orthorectification and target analysis

of RADARSAT-2 data from the 3CMV

and ACD trials

Garrett Parsons

The scientific or technical validity of this Contract Report is entirely the responsibility of the Contractor

and the contents do not necessarily have the approval or endorsement of Defence R&D Canada.

Defence R&D Canada – Ottawa

Contract Report DRDC Ottawa CR 2013-115

December 2013

Orthorectification and target analysis of RADARSAT-2 data from the 3CMV and ACD trials

Defence R&D Canada – Ottawa

Contract Scientific Authority

Original signed by Jeff Secker

Jeff Secker

Defence Scientist

Approved by

Original signed by Caroline Wilcox

Caroline Wilcox

Head, Space and ISR Section

Approved for release by

Original signed by Chris McMillan

Chris McMillan

Chair, Document Control Panel

© Her Majesty the Queen in Right of Canada, as represented by the Minister of National Defence, 2013

© Sa Majesté la Reine (en droit du Canada), telle que représentée par le ministre de la Défense nationale, 2013

DRDC Ottawa CR 2013-115 i

Abstract ……..

This report provides a technical description of the co-registration and orthorectification of repeat-pass RADARSAT-2 data collected during the Three Colour Multi-View (3CMV) and the Automatic Change Detection (ACD) trials. It also describes point target analysis that was performed on known targets (vehicles and corner reflectors) in the ACD trial data. Sample products and target analysis results are included.

Résumé ….....

Ce rapport contient une description technique de la mise en correspondance et de l’orthorectification des données acquises en passages répétés par RADARSAT-2 pendant les essais de multi-affichage à trois couleurs (3CMV) et de détection automatique des changements (ACD). On y décrit également l’analyse des cibles ponctuelles effectuée à l’aide de cibles connues (véhicules et réflecteurs en coin) sur les données de l’essai ACD. Des échantillons de produits et des résultats d’analyse des cibles sont inclus.

ii DRDC Ottawa CR 2013-115

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DRDC Ottawa CR 2013-115 iii

Executive summary


Garrett Parsons; DRDC Ottawa CR 2013-115; Defence R&D Canada – Ottawa; December 2013.

This report provides a technical description of the co-registration and orthorectification of repeat-pass RADARSAT-2 data collected during the Three Colour Multi-View (3CMV) and the Automatic Change Detection (ACD) trials. The co-registration was performed using the GAMMA software and the orthorectification was performed in PCI’s OrthoEngine©. An example 3CMV product showing change in the images is provided. This report also describes point target analysis that was performed on known targets (vehicles and corner reflectors) in the ACD trial data using DRDC Ottawa’s Image Analysis Pro (IA Pro) system. Sample 3CMV products are included, as are the results of the target analysis.

iv DRDC Ottawa CR 2013-115

Sommaire .....


Garrett Parsons; DRDC Ottawa CR 2013-115; R & D pour la défense Canada – Ottawa; décembre 2013.

Ce rapport contient une description technique de la mise en correspondance et de l’orthorectification des données acquises en passages répétés par RADARSAT-2 pendant les essais de multi-affichage à trois couleurs (3CMV) et de détection automatique des changements (ACD). La mise en correspondance a été effectuée à l’aide du logiciel GAMMA et l’orthorectification à l’aide du logiciel OrthoEngine© de PCI. Un exemple de produit de l’essai 3CMV montrant les changements dans les images est fourni. Dans ce rapport, on décrit également l’analyse des cibles ponctuelles effectuée à l’aide de cibles connues (véhicules et réflecteurs en coin) sur les données de l’essai ACD au moyen du système Image Analysis Pro (IA Pro) de RDDC Ottawa. Des échantillons de produits de l’essai 3CMV sont inclus, de même que les résultats de l’analyse de cibles.

DRDC Ottawa CR 2013-115 v

Table of contents

Abstract …….. ................................................................................................................................. iRésumé …..... ................................................................................................................................... iExecutive summary ........................................................................................................................ iiiSommaire ..... .................................................................................................................................. ivTable of contents ............................................................................................................................. vList of figures ................................................................................................................................. viList of tables .................................................................................................................................. viiAcknowledgements ...................................................................................................................... viii1 Introduction ............................................................................................................................... 12 Orthorectification of RADARSAT-2 trial data ......................................................................... 4

2.1 Co-registration using GAMMA..................................................................................... 42.1.1 ACD trial ............................................................................................................. 72.1.2 3CMV trial ........................................................................................................... 8

2.1.2.1 England ...................................................................................................... 92.1.2.2 Northern Europe Site 1 .............................................................................. 92.1.2.3 East Asia .................................................................................................... 92.1.2.4 Northern Europe Site 2 ............................................................................ 102.1.2.5 Canada ..................................................................................................... 112.1.2.6 Northern Europe Site 3 ............................................................................ 12

2.1.3 Afghanistan ........................................................................................................ 122.2 Orthorectification using PCI OrthoEngine© ................................................................ 14

2.2.1 Orthorectifying cropped co-registered RADARSAT-2 ..................................... 142.2.2 Orthorectification of co-registered RADARSAT-2 ........................................... 14

2.3 Orthorectification using GAMMA .............................................................................. 182.4 Three-colour multi-view creation ................................................................................ 19

3 Target analysis of ACD trial data ........................................................................................... 213.1 Review of IA Pro ......................................................................................................... 233.2 Target Analysis with IA Pro ........................................................................................ 233.3 Target offset measurement .......................................................................................... 303.4 Target length measurement ......................................................................................... 333.5 TCR width measurement ............................................................................................. 34

4 Afghanistan coherence results ................................................................................................ 355 Discussion ............................................................................................................................... 38References ..... ............................................................................................................................... 39List of symbols/abbreviations/acronyms/initialisms ..................................................................... 40

vi DRDC Ottawa CR 2013-115

List of figures

Figure 1: 3CMV showing part of the port .................................................................................... 20

Figure 2: 3CMV showing part of the airport ................................................................................ 20

Figure 3: RADARSAT-2 Spotlight image from 12-Dec-2009 (top) and 3CVM showing changes. Target vehicles were positioned in yellow rectangle area and TCRs are identified by blue circles. ............................................................................................ 22

Figure 4: Input Total RCS of the TCRs from all RADARSAT-2 images .................................... 25

Figure 5: Peak Region RCS of the TCRs from all RADARSAT-2 images ................................. 25

Figure 6: Peak-to-Clutter Ratio for the TCRs from all RADARSAT-2 images ........................... 26

Figure 7: Mean clutter estimate for the TCRs from all RADARSAT-2 images .......................... 26

Figure 8: TCR schematic .............................................................................................................. 27

Figure 9: Peak Region RCS for 12-Apr-2010 plotted in linear units showing a power of 4 relationship between RCS and TCR size .................................................................... 27

Figure 10: Input Total RCS of all trucks from all RADARSAT-2 images organized by orientation ................................................................................................................... 28

Figure 11: Peak Region RCS of all trucks from all RADARSAT-2 images organized by orientation ................................................................................................................... 28

Figure 12: Peak-to-Clutter ratio for all trucks in all RADARSAT-2 images organized by orientation ................................................................................................................... 29

Figure 13: Mean clutter estimate of all trucks from all RADARSAT-2 images organized by orientation ................................................................................................................... 29

Figure 14: Screen shot of a truck in IA Pro with three different mensuration options. ................ 34

Figure 15: Orthorectified RASARSAT-2 Spotlight image acquired on 20-Feb-2009 of the Afghanistan site. The yellow square shows the magnified area in Figure 18. ........... 35

Figure 16: Orthorectified coherence image of the Afghanistan site. The yellow square shows the magnified area in Figure 18. .................................................................................. 36

Figure 17: Histogram of coherence values ................................................................................... 36

Figure 18: Magnified view of optical WorldView-2 (top) and coherence (bottom) .................... 37

DRDC Ottawa CR 2013-115 vii

List of tables

Table 1: Summary of RADARSAT-2 trial data sets. ...................................................................... 3

Table 2: Pre-processing steps for co-registration of RADARSAT-2 data using GAMMA ........... 5

Table 3: Steps for co-registering RADARSAT-2 pre-processed imagery using GAMMA ........... 6

Table 4: RADARSAT-2 images collected during ACD trial. ......................................................... 8

Table 5: RADARSAT-2 images collected during England trial. .................................................... 9

Table 6: RADARSAT-2 images collected for Northern Europe site 1. .......................................... 9

Table 7: RADARSAT-2 images collected during East Asia trial. ................................................ 10

Table 8: RADARSAT-2 images collected during Northern Europe Site 2 trial. .......................... 11

Table 9: RADARSAT-2 images collected during Canada trial. .................................................... 11

Table 10: RADARSAT-2 images collected during the Northern Europe Site 3 trial. ................... 12

Table 11: RADARSAT-2 images collected during Afghanistan trial. .......................................... 12

Table 12: Optical satellite image used during the Afghanistan trial ............................................ 13

Table 13: Steps for calculating coherence using GAMMA. .......................................................... 13

Table 14: Processing steps for orthorectifying cropped co-registered RADARSAT-2 images with PCI. ..................................................................................................................... 15

Table 15 Processing steps for aligning orthorectified RADARSAT-2 with optical or GPS measurements using PCI ............................................................................................. 16

Table 16: Processing steps for orthorectifying full co-registered RADARSAT-2 images using the Radar Satellite Modeling method in PCI............................................................... 17

Table 17: Orientation of Target Vehicles ..................................................................................... 30

Table 18: TCR offsets measured from GPS coordinate to target in ascending SAR images ....... 32

Table 19: TCR offsets measured from GPS coordinate to target in descending SAR images ..... 33

viii DRDC Ottawa CR 2013-115

Acknowledgements

The RADARSAT-2 images used in this report were collected by J. Secker (DRDC Ottawa). The Image Analyst Pro (IA Pro) system was provided by J. Secker (DRDC Ottawa).

RADARSAT-2 Data and Products © MacDonald Dettwiler and Associates Ltd. (2009-2011) – All Rights Reserved. RADARSAT is an official trademark of the Canadian Space Agency.

WorldView-2 data © DigitalGlobe (2010).

DRDC Ottawa CR 2013-115 1

1 Introduction

Throughout 2009/2010, Defence R&D Canada (DRDC) Ottawa conducted trials to assess the effectiveness of RADARSAT-2 Spotlight mode data for various geospatial intelligence (GEOINT) tasks. This report is a brief technical description documenting the processing of RADARSAT-2 data sets collected by DRDC Ottawa during the Automatic Change Detection (ACD) and Three Colour Multi-View (3CMV) trials. Details of the two trials are summarized below.

ACD Trial:

The primary objective of the ACD trial was to collect a custom data set that can be used to characterize the high-resolution Spotlight mode on RADARSAT-2. It was a site monitoring experiment that assessed the utility and effectiveness of the Spotlight mode for change detection in urban vehicle compounds:

For small vehicles, including pick-up trucks, SUVs, minivans and cars; and

During wintertime conditions (snow and darkness).

The secondary objectives of the ACD Trial were to evaluate:

The Automatic Change Detection tool in the Image Analyst Pro (IA Pro) system [4];

Commercial orthorectification algorithms for RADARSAT-2 data, both those in Geomatica (amplitude only) and in GAMMA (amplitude and phase); and

Manual and automated length estimates (using the Point Target Analysis tool in IA Pro) for small vehicles in the RADARSAT-2 Spotlight mode data.

3CMV Trial:

The objectives of the 3CMV trial were to: (a) collect interferometric-compatible Spotlight mode triplets (three images from the same sensor mode and geometry, with each pair separated by 24 days) for sites of interest to the CF/DND, (b) create 3CMV products from accurately-coregistered (sub-pixel) repeat-pass RADARSAT-2 Spotlight mode images, and (c) assess the results as follows:

Can meaningful intelligence be extracted from the products?

Can the products be interpreted or are they too complex?

Is the 24-day repeat pass cycle too long to be useful?

The trial data sets consisted of single-look complex (SLC) RADARSAT-2 Spotlight mode data acquired with horizontal-horizontal (HH) polarization and are summarized in Table 1. More details of these acquisitions are provided in Sections 2.1.1 through 2.1.3. The Afghanistan data

2 DRDC Ottawa CR 2013-115

set shown in Table 1 was acquired under a different trial, but is included here because similar processing was required. The Afghanistan data set was processed for coherent change detection (CCD).

Section 2 describes the methods used to co-register and orthorectify the trial imagery and Section 3 shows the procedure used to perform target analysis on the ACD trial using IA Pro. Section 4 discusses the results of the Afghanistan coherence work and Section 5 presents a short discussion, including some comments on the use of IA Pro for this work and suggestions for improvements.


Table 1: Summary of RADARSAT-2 trial data sets.

Trial Name Location Number of Images Date Range Average Incidence Angle (degrees)

ACD Trial DRDC Ottawa 12 (6 ascending and 6 descending)

Dec-2009 to Apr-2010

Asc : 44.11

Des: 35.46

3CMV Trial England 3 descending Dec-2009 to Mar-2010

40.56

Northern Europe Site 1

3 ascending Dec-2009 to Jan-2010

26.95

East Asia 3 ascending Dec-2009 to Mar-2010

41.14


3 ascending Dec-2009 to Jan-2010

37.54

Canada 3 ascending Dec-2009 to Feb-2010

44.95


3 ascending Feb-2010 to Mar-2010

36.80

CCD Afghanistan 2 descending Feb-2009 to Mar-2009

26.61


2 Orthorectification of RADARSAT-2 trial data

RADARSAT-2 imagery from eight trial data sets were co-registered using the Gamma SAR and Interferometry software (GAMMA) and then orthorectified using PCI Geomatica’s OrthoEngine© V10.3.2 (PCI).

Orthorectification of the RADARSAT-2 images without first co-registering will result in offsets between the repeat-pass imagery. The orthorectification process uses the satellite orbit state vectors (OSV) and ground control points (GCPs) provided with each RADARSAT-2 image in the product.xml file [1]. PCI OrthoEngine© then uses a mathematical model to perform the orthorectification. Errors in the OSV and GCPs result in offsets between orthorectified SAR images. Auxiliary data sources, such as a digital elevation model (DEM), independent GCPs from global positioning system (GPS) measurements or an optical satellite image, can also be used by PCI to improve the orthorectification result. A more in-depth investigation of orthorectification with PCI’s OrthoEngine© can be found in [2].

Co-registration of the repeat-pass RADARSAT-2 images can achieve sub-pixel accuracy and is typically required for further imagery analysis, including change detection, generating 3CMV images, and interferometric analysis. The repeat-pass SAR images are co-registered to a reference SAR image and for all the trial data sets the first image acquired in each series was selected as the reference. After co-registration PCI can be used to orthorectify the data series.

The final orthorectified (and co-registered) images, GAMMA processing commands, PCI GCP files used during orthorectification, OrthoEngine project files and the 3CMV images were copied onto DVDs and provided to the Technical Authority.

Section 2.1 outlines the steps used to co-register the RADARSAT-2 images with GAMMA and Section 2.2 describes the process for orthorectifying the co-registered images using PCI OrthoEngine©. Section 2.3 gives a brief description of how GAMMA can be used to orthorectify the co-registered RADARSAT-2 images.

2.1 Co-registration using GAMMA

The RADARSAT-2 trial data sets were pre-processed using GAMMA before they were co-registered. The pre-processing steps are listed in Table 2. Table 3 describes the process for co-registering the pre-processed RADARSAT-2 imagery using GAMMA. A full description of each GAMMA function can be found in [3].


Table 2: Pre-processing steps for co-registration of RADARSAT-2 data using GAMMA

Processing Step Description

Convert data to Gamma format

GAMMA command: par_RSAT2_SLC

GAMMA’s format for SLC images is a raster file of complex numbers (.slc) and a parameter file (.slc.par). The conversion can also use one of the three look-up tables (LUT) provided with the RADARSAT-2 data to calibrate the data or no LUT can be used.

The lutSigma.xml LUT was used for all trial data.

Select reference SLC image

All repeat-pass images in the data series are registered to the same reference image. The first acquisition in the series was selected as the reference image.

Crop reference image (optional)

GAMMA command: SLC_copy

Cropping the area of interest (AOI) reduces the file size, shortens the processing time, and can help the registration process by eliminating areas that could cause problems for the co-registration. The .slc file can be displayed using GAMMA or a raster image (.ras) or bitmap (.bmp) can be created to choose the AOI. Only the reference image is cropped as the co-registration process will automatically crop the other repeat-pass images.

If cropping the reference image then the original GCPs from the product.xml are no longer valid. Furthermore, the orientation of the image in the .slc file no longer matches the original GCPs. A script was written to extract only GCPs from the RADARSAT-2 product.xml file that match the cropped area. The script also flips the GCPs based on the orbit direction so that they match the .slc orientation and writes the GCPs into a text file that can be read by PCI OrthoEngine.


Table 3: Steps for co-registering RADARSAT-2 pre-processed imagery using GAMMA


Calculate initial integer offset using orbit state vectors

GAMMA command: init_offset_orbit

Calculates the initial integer offset using the orbit state vectors in the .slc.par file. By default, the center of the reference image is chosen for the offset calculation, although the user can select any image location. The default was used for all trial data sets.

Calculate second initial offset using correlation

GAMMA command: init_offset

Calculates a second offset estimate using the previous offset as a starting point. The offset is calculated using a correlation of image intensities and by default the centre of the image is used for the calculation.

If the center of the image does not contain sufficient features for the correlation to return a valid result, another starting point can be selected. This is often the case for large water areas or homogeneous land areas. The signal to noise ratio (SNR) of the correlation is used to determine if the calculated offsets are valid, and by default this is set to 7.

In all trial data sets, the center of the image and a SNR of 7 were used.

Calculate offsets over entire image using correlation

GAMMA command: offset_pwr

The offsets between the images are calculated in patches over the entire image. The size and number of patches in azimuth and range is specified by the user. Increasing the number of patches and adjusting the patch size can improve the registration if the registration fails.

For all trial sets a patch size of 64 in range and 128 in azimuth was selected with 32 patches in both range and azimuth.

Calculate registration offset polynomials

GAMMA command: offset_fit

The registration offset polynomial for both range and azimuth is calculated. The polynomial model can be specified from a list. For more information on the offset polynomial see the GAMMA Interferometric SAR Processor reference manual included with GAMMA.

For all trial sets the polynomial model number 3 was selected.

Resample images GAMMA command: SLC_interp


Processing Step Description to reference The images are resampled to the reference image.

Generate intensity images

GAMMA command: multi_look

Converts the co-registered SLC images into intensity images. The intensity images can be opened and orthorectified using PCI.

The registration can also be verified at this point by opening the intensity images in an appropriate program, such as with GAMMA’s dispwr or PCI, and quickly switching between images. Alternatively, a raster image or bitmap can be created with GAMMA from either the resampled SLC images or intensity images and opened in an image viewing program.

A multi-look of 1 in range and 1 in azimuth was used to keep the same resolution in the intensity images as in the resampled SLC images.

The results of the co-registration using GAMMA on the trial data sets is summarized in the following sub-sections. Note that the pixel and lines given for the crop area are referenced with respect to the orientation of the .slc file from GAMMA. The pixel and line values given for the data sets are the same as used in the GAMMA command SLC_copy.

2.1.1 ACD trial

The six ascending (SLA19) and six descending (SLA7) Spotlight RADARSAT-2 images collected for the ACD trial is listed below in Table 4. The images were cropped to focus on the DRDC Ottawa site to reduce the file sizes and remove the areas not of interest. The cropped area for the ascending series starts at pixel 1896 and line 7664 and is 3000 pixels wide and 9000 lines high, referenced from the GAMMA .slc image. The descending series starts at pixel 4017 and line 10767 and is 3000 pixels wide and 9000 lines high. No registration problems in either the ascending or descending data sets were encountered.


Table 4: RADARSAT-2 images collected during ACD trial.

AcquisitionDate

AcquisitionTime (UTC)

Incidence Angle (degrees)

Polarization Orbit Direction

12-Dec-2009 23:03:26 44.11 HH Ascending

05-Jan-2010 23:03:25 44.11 HH Ascending

29-Jan-2010 23:03:25 44.11 HH Ascending

22-Feb-2010 23:03:27 44.11 HH Ascending

18-Mar-2010 23:03:28 44.12 HH Ascending

11-Apr-2010 23:03:29 44.12 HH Ascending

13-Dec-2009 11:14:04 35.48 HH Descending

06-Jan-2010 11:14:03 35.47 HH Descending

30-Jan-2010 11:14:02 35.47 HH Descending

23-Feb-2010 11:14:04 35.47 HH Descending

19-Mar-2010 11:14:05 35.48 HH Descending

12-Apr-2010 11:14:06 35.48 HH Descending

2.1.2 3CMV trial

Change in multi-temporal SAR images can be visualized by colour coding the individual images and combining the resultant images. A two-colour multi-view (2CMV) is an image fusion product which combines images acquired by the same sensor and (ideally) from the same imaging geometry: cyan is assigned as the modulation colour of the newer image, and red is assigned as a modulation colour of the older image. Where the images are properly aligned or co-registered, and if the image intensities are the same, the resultant 2CMV product will appear to be a grey-scale image. If the image intensities differ, then the resultant 2CMV product will appear cyan (blue) or red, depending on whether the newer or older image is brighter. The saying "blue is new, red has fled" is helpful in remembering the origin of the change. A 3CMV extends this concept to a set of three images: blue, green and red correspond to the newer, middle and older images, and colours in the 3CMV product indicate relative intensities on the given dates.


The following subsections discuss the RADARSAT-2 imagery used in the 3CMV trials. Details about image cropping and co-registration are also provided.

2.1.2.1 England

The three RADARSAT-2 Spotlight (SLA12) images used in the England trial are listed in Table 5. The reference image was cropped to focus on the port and surrounding area, starting at pixel 5000 and line 1 and is 5232 pixels wide and 20000 lines high. There were no issues with the registration of the RADARSAT-2 images.

Table 5: RADARSAT-2 images collected during England trial.

AcquisitionDate




20-Dec-2009 06:05:59 39.28 HH Descending

06-Feb-2010 06:05:57 39.27 HH Descending

02-Mar-2010 06:05:59 39.28 HH Descending

2.1.2.2 Northern Europe Site 1

The SLA73 images are given in Table 6. They were cropped to focus on the facility on the river surrounding area starting at pixel 3400 to the end of the image (3070 pixels wide) and included all azimuth lines (20198 lines). There were no registration problems in this data set.

Table 6: RADARSAT-2 images collected for Northern Europe site 1.

Acquisition Date Acquisition Time (UTC)

Incidence Angle (degrees) Polarization Orbit Direction

05-Dec-2009 14:50:48 24.74 HH Ascending

29-Dec-2009 14:50:48 24.74 HH Ascending

22-Jan-2010 14:50:47 24.74 HH Ascending

2.1.2.3 East Asia

The RADARSAT-2 (SLA14) images collected during the East Asia trial are listed in Table 7. The full images were co-registered because there were other interesting features in the scene in addition to the air base, including boats in a river and a train station. No registration issues were encountered in the processing of this data set.


Table 7: RADARSAT-2 images collected during East Asia trial.


Incidence Angle (degrees) Polarization Orbit Direction

24-Dec-2009 10:22:59 40.67 HH Ascending

17-Jan-2010 10:22:58 40.67 HH Ascending

30-Mar-2010 10:23:01 40.67 HH Ascending


The Northern Europe Site 2 trial data (SLA7) is listed in Table 8. The reference image was cropped to focus on the facility on the river, starting at pixel 1 and line 1 and is 7000 pixel wide and 20000 lines high. There was an error in the registration with this data set due to a change in the azimuth pixel spacing in the 18-Jan-2010 image. In both the 01-Dec-2009 and 25-Dec-2009 images the azimuth pixel spacing was approximately 0.41 m which changed to 0.34 m in the 18-Jan-2010 image. The change in azimuth pixel spacing in the delivered imagery resulted from enhancements to the product specifications of some RADARSAT-2 beams made during this time by MDA.

To compensate for the change in azimuth pixel spacing the 18-Jan-2010 image had to be resampled to the same geometry as the reference image before the co-registration was performed. The additional GAMMA steps were to first generate a LUT between the image to register and the reference image using rdc_trans. The image was then resampled to the reference image using SLC_interp_lt. The resampled output of this step was finally co-registered to the reference image using the usual method.


Table 8: RADARSAT-2 images collected during Northern Europe Site 2 trial.

AcquisitionDate

AcquisitionTime

(UTC)



01-Dec-2009 15:07:30 35.46 HH Ascending

25-Dec-2009 15:07:30 35.47 HH Ascending

18-Jan-2010 15:07:29 35.47 HH Ascending

2.1.2.5 Canada

Table 9 lists the RADARSAT-2 Spotlight (SLA19) images acquired during the Canada trial. The images were cropped to focus on the airport and surrounding area starting at pixel 4200 and line 1800 and are 6000 pixels wide and 23000 lines high. There were no issues with the image co-registration for this data series.

Table 9: RADARSAT-2 images collected during Canada trial.

AcquisitionDate




08-Dec-2009 23:19:45 44.10 HH Ascending

01-Jan-2009 23:19:45 44.10 HH Ascending

18-Feb-2010 23:19:45 44.10 HH Ascending



The RADARSAT-2 acquisitions (SLA6) are listed in Table 10. The series was cropped to focus on the area of interest, starting at pixel 1850 and line 1 and was 4700 pixels wide and included all azimuth lines (22190 lines). There were no issues with the co-registration.

Table 10: RADARSAT-2 images collected during the Northern Europe Site 3 trial.

AcquisitionDate




11-Feb-2010 15:07:34 34.65 HH Ascending

07-Mar-2010 15:07:35 34.65 HH Ascending

31-Mar-2010 15:07:37 34.66 HH Ascending

2.1.3 Afghanistan

The Afghanistan trial consisted of the RADARSAT-2 image pair listed in Table 11 and an optical image listed in Table 12. The full images were co-registered because no particular AOI was identified and there were no issues with the co-registration. The coherence between the two Afghanistan RADARSAT-2 images was also calculated using both a 5x5 pixel rectangular (default for coherence) and a 5x5 pixel triangular coherence window. The triangular window applies a linearly decreasing weight from the centre pixel. According to [3], the advantages of using a weighting function for the coherence window is an improved frequency response of the averaging kernel. The coherence calculated using the triangular window was used for the final output. The additional GAMMA steps required to calculate the coherence are listed in Table 13.

Table 11: RADARSAT-2 images collected during Afghanistan trial.


Beam Mode Polarization Orbit Direction

20-Feb-2009 01:46:42 SLA14 (Spotlight) HH Descending

16-Mar-2009 01:46:42 SLA14 (Spotlight) HH Descending


Table 12: Optical satellite image used during the Afghanistan trial

Sensor Acquisition Date Acquisition Time (UTC)

WorldView-2 13-Jan-2010 06:33:49

Table 13: Steps for calculating coherence using GAMMA.


Estimate baseline from orbit state vectors

GAMMA command: base_calc

Estimates the baseline between the two satellites using the orbit state vectors.

Simulate flat earth phase

GAMMA command: phase_sim

The flat earth phase is simulated using the baselines calculated in the previous step.

Generate differential interferogram

GAMMA command: SLC_diff_intf

The flattened differential interferogram is generated. The SLC_diff_intf command uses the simulated phase from the previous step to compensate for the flat earth phase.

Calculate coherence

GAMMA command: cc_wave

The coherence is calculated using the co-registered SAR images and the differential interferogram. The coherence is a windowed process so the size and window weighting are set. The weighting function options are constant, triangular, Gaussian, or none (uses phase only). Various window sizes and weighting functions were generated, with the 5x5 triangular window selected for the final output. The 5x5 window size reduced much of the noise present in the 3x3 version. Changing the weighting function had less of an effect then changing the window size.


2.2 Orthorectification using PCI OrthoEngine©

After the co-registration, PCI OrthoEngine© was used to orthorectify the images. As discussed in Section 2.1, some images were cropped during co-registration. The procedure to orthorectify the co-registered images is different than the procedure to orthorectify cropped co-registered images. The difference results because the OSV and GCPs in RADARSAT-2 product.xml file no longer match the cropped image. A script was used during the co-registration of the cropped images to extract the GCPs for the cropped area, but at this time there is no method available to recalculate the OSV for the cropped area.

Although the RADARSAT-2 GCPs are specified in latitude and longitude, the output projection used for all orthorectification was UTM because it is more convenient to specify the output pixel and line spacing in metres instead of degrees. The orthorectified image in UTM can be projected into degrees if necessary.

2.2.1 Orthorectifying cropped co-registered RADARSAT-2

Table 14 lists the procedure for orthorectifying the cropped and co-registered RADARSAT-2 images with using PCI, as was the case for the data from all trial sites. No auxiliary data (GPS or optical imagery) were available for these datasets.

The ACD trial contained GPS measurements of target vehicles and trihedral corner reflectors (TCRs) which were used in the orthorectification processing. Normally, these would be imported into OrthoEngine during the GCP Collection step in Table 14, but it was found that there was an offset between the GPS measurements and the RADARSAT-2 GCPs from the product.xml file. This resulted in stretching of the orthorectified image when both sources were used during orthorectification because OrthoEngine was trying to fit the image to the conflicting geographic information. Therefore, a two stage orthorectification process was followed, first using the RADARSAT-2 GCPs from the product.xml for the first orthorectification (as in Table 14) and then using the GPS measurements of the TCRs for the second. The procedure used to align the orthorectified RADARSAT-2 imagery to other data sources using OrthoEngine© is given in Table 15. Table 15 also discusses the use of optical imagery instead of GPS measurements for thesecond orthorectification that, although not used in this case, is possible.

2.2.2 Orthorectification of co-registered RADARSAT-2

The Afghanistan and East Asia trial images were not cropped during co-registration and two different methods for orthorectifying with PCI were tested. In the case of the East Asia trial, a GAMMA script was used to extract all the GCPs from the product.xml for the reference RADARSAT-2 image to a text file during co-registration. The co-registered East Asia trial data was then orthorectified following the method above for cropped images.

For the Afghanistan trial, the OSV and GCPs of the reference RADARSAT-2 image were extracted using PCI and saved to a .pix file (the OSV and GCPs are the non-raster layers of the RADARSAT-2 image when the product.xml is opened in PCI). The co-registered intensity images were opened in PCI and were flipped so that they match the orientation of the orbital elements and saved as .pix files (ascending requires flipping left-right, and descending requires an


up-down flip). The orbital elements were imported into the intensity image .pix files in PCI and the orthorectification procedure is listed in Table 16. A DTED Level 3 DEM was also available of the Afghanistan area was used during the orthorectification process. A total of five tie points were originally used during the orthorectification between the SAR and the optical image. However, it was discovered that superior results could be achieved using as few as two tie points.

Table 14: Processing steps for orthorectifying cropped co-registered RADARSAT-2 images with PCI.


Create Project Math Modeling Method: Polynomial

Output Projection: UTM

Earth Model: D000 – WGS 84

Output pixel and line spacing: Same as reference RADARSAT-2 image

GCP Projection: Set GCP Projection based on Output Projection

GCP Collection Open new or existing image: Open co-registered intensity image (.rmli) by selecting the header file (.rmli.hdr). Select the image in Open Image window and click Quick Open.

Collect GCPs Manually: Choose PIX/Text file as the Ground control source and select the GCP file created during co-registration and click Apply Format.

(Repeat GCP collection for each image to orthorectify, keeping view window for each image open)

Geometric Correction

Schedule geometric correction: Transfer all images in Available images window to Images to process window and set output file name.

Working cache: 1024

Sampling interval: 1

Resampling: Bilinear

DEM: A DEM is not used when the polynomial Math Modeling Method is used. This was not considered an issue with the data sets processed in this way because high resolution DEMs were not available at the time of processing and the regions of interest in the RADARSAT-2 images were small and typically had little topographic effects.


Table 15 Processing steps for aligning orthorectified RADARSAT-2 with optical or GPS measurements using PCI


Create Project Same as in Table 14.

GCP Collection Open new or existing image: Open orthorectified RADARSAT-2 image. Select the image in Open Image window and click Quick Open to open the RADARSAT-2 image in a view window.

Collect GCPs Manually: Choose Geocoded Image (if reference is an optical image) or Geocoded vectors (if reference is GPS measurements in a shape file) as the Ground control source. Open optical image or GPS shape file. The file will open in another view window.

Create tie points by clicking on the same location in both view windows. For an optical image use points spread throughout the SAR image. For GPS measurements use all available points, within reason. Only the three corner reflector GPS points were used for the ACD trial.

Save the tie points to a text file by clicking Options-Export-GCPs in the OrthoEngine project window Choose the GCP created during co-registration and click Apply Format. This GCP file can be used for all other images in the data series by choosing PIX/Text file as the Ground control source instead of manually selecting the points again.

(Repeat GCP collection, using the exported GCP file for each image to orthorectify, keeping view window for each image open)


Same as in Table 14.


Table 16: Processing steps for orthorectifying full co-registered RADARSAT-2 images using the Radar Satellite Modeling method in PCI.


Create Project Math Modeling Method: Radar Satellite Modeling, Toutin’s Model

Output Projection: UTM

Earth Model: D000 – WGS 84

Output pixel and line spacing: Same as reference RADARSAT-2 image

GCP Projection: Set GCP Projection based on Output Projection

GCP Collection

(optional, only if auxiliary data is available)

Open new or existing image: Open orthorectified RADARSAT-2 image. Select the image in Open Image window and click Quick Open to open the RADARSAT-2 image in a view window.

Collect GCPs Manually: Choose Geocoded Image (if reference is an optical image) as the Ground control source. Open optical image. The file will open in another view window.

Create tie points by clicking on the same location in both view windows. For an optical image use points spread throughout the SAR image. Two tie points were used spread throughout the RADARSAT-2 and WorldView-2 image in the Afghanistan trial. For GPS measurements use all available points, within reason. Only the three corner reflector GPS points were used for the ACD trial.

Save the tie points to a text file by clicking Options-Export-GCPs in the OrthoEngine© project window Choose the GCP created during co-registration and click Apply Format. This GCP file can be used for all other images in the data series by choosing PIX/Text file as the Ground control source instead of manually selecting the points again.

(Repeat GCP collection, using the exported GCP file for each image to orthorectify, keeping view window for each image open)




Open image: Open the flipped .pix file with the added orbital elements.

Schedule geometric correction: Transfer all images in Available images window to Images to process window and set output file name.

Working cache: 1024

Sampling interval: 1

Resampling: Bilinear

DEM: Enter DEM file if available. Otherwise use the default 0 height. The DEM should be in the same projection as specified in Create Project.

2.3 Orthorectification using GAMMA

It is possible to orthorectify SAR images with GAMMA using a DEM. The steps for orthorectifying in GAMMA are:

1. Import the DEM into GAMMA.

2. Create an initial LUT mapping the DEM geometry to the SAR geometry based on theDEM projection and the orbit state vectors.

3. Create a simulated SAR image from the DEM.

4. Resample the simulated SAR image into the SAR geometry using the initial LUT.

5. Register the resampled simulated SAR image to the SAR image.

6. Create a refined LUT based on the co-registration mapping the SAR geometry to theDEM geometry.

7. Resample SAR images to DEM geometry using refined LUT.

8. Create geocoded images (geotiffs) using DEM parameters and resampled SAR images.

The key factor for this process is the co-registration of the simulated SAR image (from the DEM) to the SAR image to create the refined LUT. If the posting of the DEM (pixel size) is much larger than the pixel spacing of the SAR image then it will be difficult to co-register the two images with any certainty. A DTED Level 2 DEM was available for many of the trial sites but with a posting of approximately 20 m easting by 30 m northing the resolution was too poor to accurately register to the RADARSAT-2 spotlight image with a pixel spacing of approximately 1.3 m in range by 0.4 m in azimuth. Additionally, there needs to be topographic features in the SAR image for the co-registration to the simulated SAR image from the DEM to be successful. The two LUTs created during this process are approximately twice the file size of the SAR image which can get very large, depending on the output pixel spacing chosen for the orthorectified


images. For example, one LUT for a full RADARSAT-2 spotlight image with an output pixel spacing of 1 metre by 0.34 metres was about 7GB.

If no appropriate DEM is available, GAMMA can also use a generic earth model, such as WGS 84, and the orbit state vectors from the SAR image to do the geocoding. The processing steps are the same as for the case with the DEM except that only the initial LUT is created and used to resample the SAR images to the earth model geometry.

The PCI OrthoEngine© was selected over both of these methods because OrthoEngine© allows other information, such as GPS points or optical imagery to be used during orthorectification. Furthermore, the orthorectification processing time was shorter and used less hard drive space with PCI.

2.4 Three-colour multi-view creation

Three-colour multi-view images were created using the orthorectified England, Canada, Northern Europe and East Asia trial data using IA Pro.

The three .pix files for each trial were opened using SAR as the Image Type in IA Pro. The Linear Stretch/Enhancement was used for all trial sets except Canada which used the Equalization Enhancement option.

The 3CMV images were created using the Autocycle Tool in IA Pro. The acquisition date and time has to be added to each image using the Add data/time information to raster button because the metadata from the original product.xml file was not preserved during the co-registration and orthorectification processing. The images were then sorted by time collected before clicking the 3 colour view button.

The output images were created using Tools GEOINT Product. 3CMV was selected for the Legend and the Save Options was set to Image and Annotation – TIFF – not Geocoded.

An overview 3CMV image was created for each data set and multiple close-up images were created using a zoom of 1:2. The close-up images were focused on features of interest in the data sets such as site infrastructure and areas showing changes. Figures 1 and 2 show example 3CMV products.

The 3CMV image in Figure 1 shows ships at the port on different dates (red, green, or blue ships). The vertical line on the right side of the image is part of the dock structure.

Figure 2 shows a section of the airport and shows a lot of changes are found along the airport where the planes park. The airport is close to a highway and other streets (off image) that likely contribute to the artefacts found throughout this image.

20

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3 Target analysis of ACD trial data

The ACD trial consisted of a stationary array of 3 pairs of trihedral corner reflectors, an array of 5 commercial pickup trucks (locations changed for each image) and target vehicles of opportunity. Target analysis was performed on all TCRs and pickup trucks using IA Pro in each RADARSAT-2 image. Section 3.1 briefly describes the processing steps used for the target analysis. Section 3.3 lists the procedures for measuring the offset between the corner reflectors in the original RADARSAT-2 image and the measured locations and Section 3.4 outlines measuring of the target vehicles in IA Pro.

Figure 3 shows part of the DRDC Ottawa facility where the ACD trial was conducted. The top image is the RADARSAT-2 ascending Spotlight image from 12-Dec-2009 and the bottom image is a 3CMV of this image and images acquired on 05-Jan-2010 and 29-Jan-2010. The yellow rectangles in both images show the location of the target vehicles and the three TCRs are circled in blue. The darker blue lines in the 3CMV image are snow banks that are only present in the 29-Jan-2010 image.

The detailed target analysis, target offset, and target length measurements were compiled in a spreadsheet for further analysis not included in this report.

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3.1 Review of IA Pro

IA Pro is DRDC Ottawa’s test bed system for validation and demonstration of new algorithms and tools for SAR-based GEOINT. IA Pro includes unique algorithms and tools that: are missing from commercial off the shelf (COTS) software; need to be packaged differently for the CF/DND; and introduce automation not present in COTS software. The motivation for IA Pro was to develop easy-to-use tools to facilitate processing and exploitation of SAR data for GEOINT to address CF/DND requirements:

Load, display and manipulate SAR data;

Support complex data;

Pre-process data to deal with sensor-specific quirks;

Support multiple SAR sensors;

Support exploitation and contextual evaluation.

This will allow the image and geospatial analysts to focus on their core business of intelligence extraction.

3.2 Target Analysis with IA Pro

The procedure for analysing the targets in the ACD trial is as follows:

1. Open the imported RADARSAT-2 image in IA Pro (has filename extension .xml.vrt). If this is the first time opening image with IA Pro, use the Import feature to generate the .xml.vrt file.

2. Open the ground truth information in ESRI shape file format:

a. Open the trihedral corner reflector shape file. (TCR3.shp)

b. Open the target shape file for the RADARSAT-2 acquisition date. (e.g. Dec12_TrialVehicles.shp)

3. Using the Nudge tool, move the RADARSAT-2 image so that the corner reflectors in the SAR image align with the GPS measurements.

4. Change the zoom window to 32x32 under File-Options.

5. Using the Insert target tool, add a target for the north-most truck and click Insert target to accept the target. Go to the RADARSAT-2 image directory and rename the IA Pro created file imagery_HH_iapro_TargetAnal.txt to include the truck ID and zoom window size. Continue adding targets to the trucks progressing south, renaming the text file after each target is accepted (Otherwise the file is overwritten with each new target). Then add targets for the three corner reflectors, starting with the north-most and moving south, again renaming the text file after each reflector is accepted.


6. Inspect all targets using the Interactive Vector Validation tool:

a. If clutter is found in the corners of the zoom window, try moving the target toexclude the clutter. If this is not possible, reduce the size of the window to 16x16 and add this as a new target. As before, rename the target analysis text file after each new target is accepted.

7. Save the vectors using the Interactive Vector Validation tool.

8. Save the project.

The renamed text files were then imported into Excel and a subset of the parameters were analysed. These measures include:

Input Total RCS [dBm2]: Input total radar cross section is the integral of linear ° over the entire target region/area converted to dBm2

Peak Region Radar Cross Section (RCS) [dBm2]: Peak region RCS is the integral of linear sigma nought over the peak target region/area converted to dBm2. (Sigma nought is one of the physically meaningful measures of SAR backscatter and is usually expressed in dB)

Peak-to-Clutter Ratio (PCR) [dB]: Ratio of the peak value to the mean clutter estimate

Mean Clutter Estimate [dB]: Estimate of the mean clutter calculated from four “clutter boxes” at corners of the target sub-scene

Figure 4 to Figure 7 show the averaged value of these four radiometric measures for the TCRs in all RADARSAT-2 images, organized by TCR size. In all cases, the linear radiometric measures were averaged, and then converted into dB. From these figures (except the mean clutter estimate) the radiometric measure increases as the TCRs get larger. Figure 8 shows a schematic of a TCR and Equation (1) shows the relationship between the peak sigma nought for a corner reflector with triangular sides ( is wavelength). Ignoring all the constants in the equation, the peak sigma nought is proportional to the size of the TCR to the fourth power (in linear units). Unfortunately, the orientations of the TCRs were not pointing in the correct direction to be aligned with the line of sight (LOS) of RADARSAT-2 for the Dec-2009 to Mar-2010 images, so this relationship will not be true. The orientation was corrected for the Apr-2010 acquisitions. Plotting the linear peak region RCS for the TCRs in the 12-Apr-2010 shows a power of four trend, as seen in Figure 9.

Figure 10 to Figure 13 shows the averaged value of the four radiometric measures for the trucks from all RADARSAT-2 images, organized by orientation from true north. It is clear from these figures (except the mean clutter estimate) that the measures are highest when the trucks were perpendicular to the RADARSAT-2 LOS, 351° for ascending and 370° for descending. The values for the other angles of 234°, 323°, and 343° were lower. The orientation of the target vehicles (with respect to true north) are listed in Table 17. The trucks used in this experiment turned out to be complex targets and presented many different scattering structures at various off perpendicular angles.

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Table 17: Orientation of Target Vehicles

Image Date Target Vehicle Orientation, from true north (degrees)

12-Dec-2009 All at 324

05-Jan-2010 All at 324

29-Jan-2010 All at 234

22-Feb-2010 All at 324

18-Mar-2010 All at 324

11-Apr-2010 All at 351 (perpendicular to RADARSAT-2 LOS)

13-Dec-2009 Three at 324, two at 343

06-Jan-2010 Three at 324, two at 343

30-Jan-2010 All at 234

23-Feb-2010 All at 324

19-Mar-2010 Three at 324, two at 343

12-Apr-2010 All at 370 (perpendicular to RADARSAT-2 LOS)

3.3 Target offset measurement

The target analysis in IA Pro also included a measurement of the offset between the corner reflectors in the SAR image and the GPS measurement of the reflectors. These offsets were measured using the following steps:

1. Open the RADARSAT-2 image in IA Pro.

2. Open the corner reflector shape file.


3. Zoom in so that only the current corner reflector and GPS point is visible in IA Pro.

4. Using the GEO point tool get the latitude and longitude of the GPS measurement for thecurrent corner reflector.

5. Using the GEO point tool get the latitude and longitude of the GPS measurement for thebrightest pixel of the corner reflector in the RADARSAT-2 image.

6. Using the Mensuration tool click on the GPS point and right-click on the brightest pixel incorner reflector. Record the length in metres and pixels, and the bearing.

7. Repeat for the other reflectors.

There was some difficulty encountered when trying to click on the GPS marker and the brightest pixel in the corner reflector. The mouse click would snap to a grid that was horizontal and vertical, while the image pixels were in a slanted grid due to the orthorectification. Because of this, there could be a small error in the offset measurements. Table 18 and Table 19 show the measured offsets between the GPS coordinate and the associated TCR in the RADARSAT-2 ascending and descending images. (Note that in both tables the heading is from the GPS location to the TCR).


Table 18: TCR offsets measured from GPS coordinate to target in ascending SAR images

Date TCR ID Offset (metres) Heading (degrees)

12-Dec-2009 1 52.43 78.7

2 52.42 78.8

3 46.92 77

05-Jan-2010 1 47.64 264.3

2 48.08 264.2

3 54.09 264.9

29-Jan-2010 1 45.12 258.3

2 45.92 257.8

3 51.21 259.4

22-Feb-2010 1 47.48 263.5

2 47.57 262.9

3 55.88 264

18-Mar-2010 1 55.14 80.8

2 54.97 81

3 48.71 79.6

11-Apr-2010 1 54.26 80.8

2 52.47 80.6

3 46.54 79.3


Table 19: TCR offsets measured from GPS coordinate to target in descending SAR images

Date TCR ID Offset (metres) Heading (degrees)

13-Dec-2009 1 72.7 282.4

2 72.53 282.5

3 64.06 284.8

06-Jan-2010 1 58.28 87.6

2 55.73 87.1

3 66.3 87

30-Jan-2010 1 57.12 88.7

2 54.95 88.5

3 64.98 88.2

23-Feb-2010 1 53.06 88.7

2 54 89.2

3 61.88 88.6

19-Mar-2010 1 69.92 280.8

2 72.07 280.9

3 62.76 282.5

12-Apr-2010

1 74.82 281.9

2 74.57 282.3

3 65.88 284.8

3.4 Target length measurement

The length of the trucks in the ACD trial is automatically calculated using an algorithm built into the IA Pro point target analysis tool. The lengths were also measured using the Mensuration tool to compare with the automatically calculated values. The steps for measuring the trucks with IA Pro are:

1. Open the RADARSAT-2 image in IA Pro.

2. Open the target shape file for the RADARSAT-2 acquisition date.

3. Zoom in on the first truck.

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5 Discussion

This report provides a technical description of the co-registration and orthorectification of repeat-pass RADARSAT-2 data collected during the 3CMV and ACD trials. The co-registration was performed using the GAMMA software and the orthorectification was performed in PCI’s OrthoEngine©. Three colour multi-view images showing changes in the images were created from six trial data sets and coherent change detection was performed on another trial data set.

This report also describes point target analysis that was performed on known targets (vehicles and corner reflectors) in the ACD trial data using DRDC Ottawa’s IA Pro system. The following comments on the use of IA Pro are based on the work completed in this report:

Saved projects would not always load properly.

Saved targets would not always load properly. The problem is that when selected in the Interactive Vector Validation Tool, the Point Target Analysis window would not open.

The Point Target Analysis window does not always open when a target is inserted.

It would be helpful to see an outline of the clutter areas in the zoom window, to determine if bright pixels are in the clutter area.

Permit a non-square zoom window size for target analysis of RADARSAT-2 Spotlight imagery because of the higher azimuth resolution.

An easier method for adjusting the brightness of the zoom window in target analysis would be beneficial. This would make finding the brightest pixel simple and help with determining if pixels in the clutter area are significant.


References .....

[1] PCI Geomatics (2007). Geomatica® OrthoEngine® 10.1: User Guide. http://www.gis.unbc.ca/help/software/pci/orthoeng.pdf (accessed 11-Oct-2013).

[2] Secker, J., Cole, M., and Gong, S. (2011). Exploitation of space-based SAR data: Orthorectification and DEM generation using PCI Geomatics’ OrthoEngine© software. DRDC Ottawa TM 2011-233. Defence R&D Canada – Ottawa.

[3] GAMMA Remote Sensing (2011). Documentation – User’s Guide: Interferometric SAR Processor – ISP. Version 1.6 – May 2011. GAMMA Remote Sensing AG, Worbstrasse 225, CH-3073 Gümligen, Switzerland.

[4] Secker, J. and Vachon, P.W. (2007). Exploitation of multi-temporal SAR and EO satellite imagery for geospatial intelligence. In Proceedings of Fusion 2007: The 10th International Conference on Information Fusion, 09-12 July 2007, Quebec City. 8 pages


List of symbols/abbreviations/acronyms/initialisms

3CMV Two-Colour Multi-View

3CMV Three-Colour Multi-View

ACD Automatic Change Detection

AOI Area of Interest

CCD Coherent Change Detection

CF Canadian Forces

COTS Commercial-off-the-shelf

DEM Digital Elevation Model

DND Department of National Defence

DRDC Defence Research & Development Canada

DRDKIM Director Research and Development Knowledge and Information Management

GCPs Ground Control Points

GEOINT Geospatial Intelligence

GPS Global Positioning System

IA Pro Image Analysis Pro

LOS Line of Sight

LUT Look-Up Table

OSV Orbit state vectors

PCR Peak-to-Clutter Ratio

R&D Research & Development

RCS Radar cross section

SLC Single-Look Complex

SNR Signal-to-Noise Ratio

TCR Trihedral Corner Reflector

UTC Coordinated Universal Time

DOCUMENT CONTROL DATA (Security classification of title, body of abstract and indexing annotation must be entered when the overall document is classified)

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June 2010

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the 3CMV and ACD trials(last name, followed by initials – ranks, titles, etc. not to be used)

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6

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Ottawa CR 2013-115 (Any limitations on further dissemination of the document, other than those imposed by security classification.)

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This report provides a technical description of the co-registration and orthorectification of repeat-pass RADARSAT-2 data collected during the Three Colour Multi-View (3CMV) and the Automatic Change Detection (ACD) trials. It also describes point target analysis that was performed on known targets (vehicles and corner reflectors) in the ACD trial data. Sample products and target analysis results are included.

Ce rapport contient une description technique de la mise en correspondance et de l’orthorectification des données acquises en passages répétés par RADARSAT-2 pendant les essais de multi-affichage à trois couleurs (3CMV) et de détection automatique des changements (ACD). On y décrit également l’analyse des cibles ponctuelles effectuée à l’aide de cibles connues (véhicules et réflecteurs en coin) sur les données de l’essai ACD. Des échantillons de produits et des résultats d’analyse des cibles sont inclus.

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; Spotlight; co-registration; orthorectification;

PCI; OrthoEngine; three-colour multiview; 3CMV; point target analysis; change detection

Documents

Orthorectification and target analysis of RADARSAT-2 …cradpdf.drdc-rddc.gc.ca/PDFS/unc223/p803619_A1b.pdf · Orthorectification and target analysis of RADARSAT-2 data from the 3CMV