GEOMATICS WITH RADARSAT IMAGERY...Geomatics with Radarsat Imagery Emirates Journal for Engineering Research, Vol. 14, No.1, 2009 3 to radargrammetry, orbitography, geodesy and cartography

Emirates Journal for Engineering Research, 14 (1), 1-17 (2009) (Regular Paper)

1

GEOMATICS WITH RADARSAT IMAGERY

A.A. Alsalman

College of Engineering, King Saud University, 11421 Riyadh, Saudi Arabia, Email: [email protected]

(Received May 2008 and accepted January 2009)

-1/25000(فى تطبيقات الخرائط الطبوغرافية بمقياس رسم ) SAR(يعنى البحث باستخدام الصور الرادارية ، وتم اختيار منطقتين بالمملكة العربية السعودية والسودان لعمل دراسات ھندسية شاملة دقيقة للنظام )1/50000

، انتاج صورة رادارية قياسية، DEMتطبيقات الخرائط الطبوغرافية، جيل : الرادارى لتعيين قدرة ھذه الصور فىلصور الرياض بالسعودية مناسبة لخرائط SARلـ F2أظھرت النتائج أن الصيغة العالية . القطاع الجانبى الرادارىو

، أما 1/100000مناسبة للخرائط المساحية مقياس رسم SARلـ S1بينما الصيغة القياسية 1/50000مقياس رسم أو أقل، وقد أظھر ذلك 1/40000رسم لصور منطقة كسال بالسودان مناسبة لمقياس W2الصيغة العريضة

باستخدام صور رادارية مجسم وعالية الدقة، أمكن . األخطاء الفادحة بمقياس الرسم الھندسى للصور الغير مصححةوالتى تعد مناسبة لعديد من تطبيقات علوم األرض والتى ال ±7mتنفيذ النموذج الرقمى لإلرتفاعات بدقة تصل الى

. الجيولوجيا والجغرافيا المساحية، الھيدرولوجيا، المساحة االستكشافية لتضاريس األرض: تحتاج لدقة أعلى مثل

The present study is concerned with an investigation of the potential of third generation satellite-borne synthetic aperture radar (SAR) imagery, as exemplified by that flown onboard the Canadian Space Vehicle Radarsat, for topographic mapping applications at medium and small scales (1/50000-1/250000). Two test areas in Saudi Arabia and Sudan were made to undergo a series of extensive and comprehensive geometric accuracy tests of the Radarsat system to investigate the ability of these images for topographic mapping applications, DEM generation, radar ortho-photo production and radar profiling. The results show that the SAR fine mode F2 image of Riyadh is suitable for mapping at scale of 1/50000, while the standard mode S1 image satisfies the requirements of planimetric mapping at 1/100000 scale. The wide mode W2 image of Kassala area in Sudan is commensurate with only the requirements of 1/400000 scale and smaller. In this respect, the effects of uncompensated geometric scale errors in their raw uncorrected state are clearly noticeable in most images. Digital elevation model generation is possible with high resolution SAR stereo techniques. In this respect the accuracy achieved in height measurement was in the order of ±7m. This is a suitable accuracy level for a multitude of earth science applications where very stringent accuracy is not of paramount importance e.g. geologic and geographical surveys, hydrology and reconnaissance surveys for relief operations etc. Keywords: Radarsat, geomatics, mathematical transformations, geometric accuracy, PCI software,

satellite orbital model, stereo SAR, Radar orthoimage.

1. INTRODUCTION

Radarsat-1 is an advanced Earth observation satellite project developed by the Canadian Space Agency (CSA). It was launched by NASA in 1995 from Vanbenburg Air Force Base to monitor environmental changes and to support resource sustainability. Some potential applications of Radarsat-1 data include sea-ice monitoring, daily ice charts, extensive cartography and other non-cartographic applications. Under the auspices of CSA, Canada had been responsible for the design and integration of the overall system, of its control and operation in orbit, and for the operation of the data reception and processing stations located in Prince Albert, Saskatchewan and Gatineau (Quebec).

Radarsat is equipped with an advanced C-band synthetic aperture radar (SAR) instrument (5.6cm wavelength), with right-looking and steerable antenna and multi-mode imaging capabilities[1].

2. RADASAT SPACECRAFT AND SYSTEM CHARACTERISTECS

Radarsat was placed in a near-polar, sun-synchronous orbit 798 km above the Earth. It has a down-dusk orbit and is rarely in eclipse or darkness (Figure 1). The general orbital characteristics of Radarsat are shown in Table 1, while Table 2 presents its main system characteristics.

A.A. Alsalman

2 Emirates Journal for Engineering Research, Vol. 14, No.1, 2009

Figure 1. Radarsat orbit (Radarssat international web site)[1].

Table1. Radarsat orbit characteristics.

Altitude 798 km Inclination 98.6° Period 100.7 Minutes Repeat Cycle 24 days Coverage 7 day and 3 day sub-cycles

Table 2. Radarsat system specifications.

Frequency 5.3GHz Wavelength 798 km RF Bandwidth 11.6, 17.3, or30.0 MHzSampling rate 12.9, 18.5, or 30.0 MHzTransmit pulse length 42.0 s Pulse repetition frequency 1270 to 1390 Hz Transmitter peak power 5 Kw Transmitter average power 300 W Average radar data rate 73.9 to 100.0 Mb/sAntenna size 15.0 1.5 m Mass 2750 Km Solar array 3.5 Kw Batteries 3× 48 Ah NiCd Design lifetime 5 year

3. RADARSAT PRODUCTS

Radarsat products consist of SAR images or signal data stored on magnetic, optical or electronic media. Products are characterized by the beam mode and position used by the satellite and the level of processing that has been applied to the data. A summary of the various Radarsat products is given in Table 3.

Table 3. List of radarsat products.

Processing Level

Product TypeRadarsat

Mnemonics

Product Type RSI Terminology

Product Level

RAW (signal data) RAW RAW signal data

Level 0 Georeferenced data

SLC Single Look Complex

(Satellite path oriented)

SGF Path Image SGX Path Image Plus

Level 1 SCN ScanSAR Narrow SCW Scan SAR Wide

Geocoded Data SSG Map Image Level 2 (Map oriented) SPG Precision Map

Image

4. RADAR GEOMATIC MODELS

A variety of theoretical mathematical models exist for testing the geometric and cartographic fidelity of any radar system. These were thoroughly documented by Ali[2] and Toutin[3]. Some of these are described in the following.

4.1 Polynomial Transformation of SLR Imagery

In the 1970's and 1980's and in attempting mapping from side-looking radar (SLR) imagery many investigators made efforts to select a set of polynomial parameters in the light of knowledge of the basic imaging geometry and the known effects of the image displacements, inner orientation (timing and range marks, sweep delay constant, CRT deflection errors, scale factors along track and across track and ground resolution element) and exterior orientation (geometrical effects of the rotations, geometrical effects of the translations, and effect of Earth curvature, range migration, atmosphere and ionosphere on SLR imagery). By expressing these displacements as continuous functions of image coordinates, and taking the scale factor into consideration, the errors in the image coordinates (dx, dy) can be computed. According to Ali[4-6] the general form of the transformation polynomial is as follows:

yxxyxxxyyxdy

yxxyxxxyyxdx

o

o

37

36

25

24321

37

36

25

24321

(1)

Based on these equations, the relationship between the x,y image coordinates of the SLR system and the X,Y coordinates of the corresponding terrain point may be expressed as:

yxbxbyxbxbxybybxbbY

yxaxayxaxaxyayaxaaX

o

o

37

36

25

24321

37

36

25

24321

(2) where ai,bi are the polynomial transformation parameters; X, Y are the terrain coordinates; and x,y are the corresponding image coordinates.

4.2 Sattelite Orbital Mathematical Models

With the advent of satellite-borne radar system technology, other mathematical models became available to radar image analysts and users. One popular and rigorous three dimensional mathematical model is the satellite orbital model developed by Toutin[3] of the Canada Centre for Remote Sensing to compensate for distortions such as sensor geometry, satellite orbit, attitude variations, Earth curvature, and rotation and relief. This model can be applied to satellite radar images from ASAR, Radarsat, ERS-1 and JERS1. The mathematical model calculates the position and orientation of the sensor at the time when the image was taken. It is based on the well-known collinearity condition equations, which represent the physical law of transformation between the image space and the ground space. It uses principles related

Equator

Earth’s Rotation

Satellite Direction

SatelliteGround Track

Geomatics with Radarsat Imagery

Emirates Journal for Engineering Research, Vol. 14, No.1, 2009 3

to radargrammetry, orbitography, geodesy and cartography. The polynomial is given as follows:

n

i

n

j

P

k

kjiijkD ZYXaXYZP

0 0 03 )( (3)

where P point of interest; X,Y,Z are the terrain or cartographic coordinates; i,j,k are integer increments; n,m and p are integer values, generally being between 0 and 3, aij coefficients of the polynomial. This mathematical model reflects the physical reality of the complete viewing geometry and reflects all the distortions generated during image formation such as those caused by:

- The platform (position, velocity and orientation); - The sensor (orientation, integration time and field

of view); - The Earth (geoid, ellipsoid and relief); and - The cartographic projection. As a result of this integration, the modelling

equations are simple and straight-forward with few unknowns. Each of the unknowns is a combination of several correlated variables of the viewing geometry, so the number of unknowns is reduced to an independent set. The equations are then solved with few ground control points. Different images acquired from one satellite or from a combination of images from different satellites can be accomodated. It is believed that the accuracy of the satellite orbital mathematical model is approximately one pixel for radar images when quality ground control points are used. Toutin[3] proved the accuracy of this mathematical model by testing it using many different Radarsat stereo images of different areas and relief variations, with accuracies ranging from ±8.8 m to ±26.8m.

5. TEST AREA, DESIGN AND PROCEDURE OF THE TEST

The ultimate aim of this study is to investigate the potential of satellite-borne radar systems, as exemplified by Radarsat, for mapping applications in developing countries. For this purpose, several tests were carried out. These include:

- Accuracy tests of the radar imageries made available to the authors;

- Digital SAR image processing to produce digital elevation models;

- Creation of SAR orthophotos; - Stereo-SAR image generation; This section discusses the nature of the test areas

and materials used to achieve these goals and outline the procedures followed in carrying out these processes. The two images used to test these issues were made available to the authors by King Abdalaziz City for Science and Technology in Riyadh, Saudi Arabia under the kind sponsorship of the Research Centre of the College of Engineering of King Saud University in Riyadh. The first image covers Riyadh,

the capital of Saudi Arabia, and second covers the Kassala Town area in eastern Sudan.

5.1 Riyadh Test Area

The greater Riyadh area lies in the centre of the Arabian Peninsula at latitudes 24° to 28°N and longitudes 43° to 46°E, and comprises a land area of approximately 100,000 square kilometres. The average elevation of the area is 600m above mean sea level. This vantage location has given Riyadh City a strategic dimension where it is considered as the connecting region between the east, west, north and south of Arabia, in addition to the strategic importance of the Arabian Peninsula which is linking the two biggest continents, Africa and Asia. During the past two decades, the city has witnessed a high growth of population estimated at 8 percent annually[7,8]. The present population of the city (2006) is about 4.5 million, and it is expected to reach 9 million by the year 2015. The test area comprises many urban areas, rural districts; various land types and a few agricultural lands. Figure 2 shows the geographical location of Riyadh test area.

5.2 Kassala Test Area

The second test area used in this study is the Kassala Town area. This test area lies in the eastern part of Sudan, near the border with Eritrea. The centre of the town is at latitude 15o27`21``N and longitude 36o23`56`È with an average elevation of about 500m above mean sea level. Kassala Town is essentially a market centre and a rail transport hub. The area consists of extensive fruit gardens, many villages and settlements, and extensive barren lands. The area is transversed by the famous Al-gash River basin which has an average elevation of 450m. Administratively, the area consists of 36,710 km² with an estimated population of approximately 1,400,000 (2000). Figure 3 shows the geographic location of the Kassala test area. One reason for selecting this area for the present study is its extensive flat ground, thus minimizing errors due to relief radar displacement. Another reason relates to the availability of an acceptable number and level of ground control points.

5.3 Test Materials

Images used in this study are obtained from the Remote Sensing Centre of King Abd Alaziz City for Science and Technology in Riyadh, Saudi Arabia. The Radarsat images were in the standard, fine and wide modes with different resolutions ranging from 8m to 25 m and at SGF product level (Table 3). The Radarsat imagery standard mode S1 for the Riyadh test area was acquired on 1998/05/04 at time 02:47:22 (local time) and covers an area of about 10 000 square kilometress. This area lies between latitudes 24o23`47`ànd 25o27`21``N, and longitudes 46o11`14`` and 47o16`29`È.

A.A. Alsalman


Figure 2. Geographic location of Riyadh test area.

15o25`

15o00` 35o45` 36o30`

Figure 3. Geographic location of Kassala test area. The fine mode imagery F2 which was acquired on

1999/11/26 at time 14:50:35 (local time) covers an area of about 2500 square kilometers in extent and lies between latitudes 24o18`18`` and 24o47`54``N, and longitudes 46o36`12`ànd 47o12`20`È. The Radarsat imagery standard mode S7 for Kassala area was acquired on 1999/07/28 at time 03:19:04 (local time) also covers an area of about 10 000 square kilometers, while the wide mode imagery W2 of the same area was acquired on 2000/04/27 at time 05:27:17 (local time) and covers an area of 20 000 square kilometers. Figures 4 and 5 show the images of the Riyadh area, and Figures 6 and 7 show the images of the Kassala test area used in the present study. For the sake of completeness and comparison, two Spot-5 images of the test areas were also available. Both images have nominal resolution of 5m. Figure 8 shows the Riyadh Spot-5 image while Figure 9 shows Spot-5 image of the Kassala area.

5.4 Ground Control and Check Points

In remote sensing a ground control point (GCP) is a feature that can be clearly identified in the raw image and has known ground coordinates in some specified georeference frame system. The appropriate number of

Figure 4. Riyadh Radarsat image fine mode F2.

Figure 5. Riyadh Radarsat image standard mode S1.

Figure 6. Kassala Radarsat image standard mode S7.

Figure 7. Kassala Radarsat image wide mode W2.



Figure 8. Spot-5 image of Riyadh test area.

Figure 9. Spot-5 image of Kassala test area.

Figure10. Layout of available ground control in Riyadh area.

Figure 11. Layout of available ground control in Kassala area.

GCPs per image required to solve a certain problem is dependent on various factors e.g. the method of data collection, the sensor type and resolution, the area covered by the image, the geometric model used, the study site and its characteristics, the prevailing physical environment, GCP accuracy and the final expected accuracy. If GCPs are determined apriori without any knowledge of the images to be processed, 50 percent of the points may be rejected. If GCPs are determined aposteriori with knowledge of the images to be processed, the rejection factor may be around 20-30 percent[3]. Consequently, all aspects of GCP collection do not need to be considered separately but as a whole in order to avoid large discrepancies in the accuracy of the final products.

For the Riyadh test area, sixty cadastral ground control points and a digital map of Riyadh were made available by Riyadh Mayorship. Cadastral control points derived from large scale topographic maps (scale 1/1000) were found to be of little use because the majority of them were unidentifiable on the test images. On the Riyadh image, ten control points whose map coordinates were scaled off 1/25000 topographic maps of the area were used for transforming the image from the pixel coordinate system to the ground coordinate system, thus allowing computation of the transformation parameters, while three check points were utilized to check the internal accuracy (or geometric fidelity) of the test images by applying the already computed transformation parameters. The control points used in the Kassala test area were derived from the Al-gash Project carried out by the Ministry of Irrigation in Sudan in 2001 using GPS techniques. The planimetric accuracy of the control points is about ±20 m. Some of the elevations of the points were extracted from existing profiles, while other points are measured with ordinary levelling field work with a vertical accuracy of around ±1 m.

Figures 10 and 11 show the layout of the control points in the Riyadh and Kassala areas, respectively. It will be noticed that great effort was exerted in creating a suitable distribution of the control and check points so that the latter ones lie conveniently between the former to avoid some check points being extrapolated, thus reducing the efficiency of the mathematical transformation. Different control point distributions were used for the different polynomial solutions. The actual control and check points used are shown in the relevant tables of results.

An existing source of vertical GCPs was not directly available in the Kassala area, and it was prohibitively expensive to collect new ground control points, especially for areas inaccessible by road. This made the authors of the present work develop a method for extracting vertical ground control points to complete the geometric accuracy tests of the images as will be explained later in this report.

A.A. Alsalman


5.5 Software Processing System

The PCI Geomatica Software was utilized for all processing of the Radarsat data. PCI Geomatica is a Canadian commercial image processing package. Its functions are very similar to those of ERDAS. The authors had at their disposal an old version of ERDAS package (version 7.4) and this Geomatica PCI. Recent experiences with both packages made the authors choose the latter for the present research work. PCI's multi-channel image file "(PCI.pix" structure is also very similar to that of ERDAS multi-band image file (.lan), but PCI's I/O functions give more options than those of ERDAS. The unique real-time FLY module in the PCI is attractive and very verstile. However, ERDAS software and its file structure are still very popular and widely accepted. The PCI Geomatica includes various modules. These are FOCUS, Orthoengine, Moduler, FLY and EASI.

Orthoengine is a powerful photogrammetric tool designed to handle small and large production workloads to efficiently produce quality geospatial products. It supports images from standard aerial, digital and video cameras, and data from satellite sensors such as ASAR, EROS, JERS, QUICKBIRD, ASTER, ERS, LANDSAT, RADARSAT, AVHRR, IKONOS, MERIS, SPOT, EOC and IRS. Orthoengine’s interface is organized along logical workflows to produce orthorectified or geometrically corrected images; digital elevation models (DEMs), three-dimensional vectors and mosaics.

6. METHODS OF ACCURACY TESTS

The purpose of these tests is to transform the image coordinates to the terrain coordinates in order to extract mapping information from the image. In this study, different transformation algorithms have been used to effect this transformation. Essentially, the method of approach is to create a well-defined test field of ground control points on all images whose ground coordinates are accurately scaled off large scale maps. Some of these are used as control points on which the transformation had to be based (i.e. to compute the transformation parameters); others were used as check points for the purpose of assessing the geometric fidelity of the test images. The following subsections discuss the simple conformal linear transformation, polynomial transformation and satellite orbital model.

6.1 Measurement of Image Coordinates

Initially, hard copy versions of the test images were prepared in order to be used for manual radar image interpretation. This preliminary stage proved useful in identifying the different control points in the measurement stage , and in fact , helped in designing the control distribution patterns used in the various geometric tests of this research work (Figures 10 and 11).

Measurement of image coordinates was carried out using the PCI software. When adding the imageries to the project, and opening the ground control panel, the software will give many alternatives to collect control points and image coordinates. When moving the cursor to the exact location of a control point, clicking in the image and using the point icon, the software reads and displays the image coordinates. Zoom capability can be used to locate the control points as precisely as possible.

6.2 The Linear Conformal Transformation

The simplest transformation is the linear conformal (similarity) transformation comprising a scale change, a rotation and two independent translations. This transformation has the following form:

daybxE

cbyaxN

(4)

where N= Northings of a point in the terrain system; E= Eastings of the point in the terrain system; x,y = measured image coordinates of the point; and a,b,c,d = transformation parameters.Then least squares techniques can be used to compute the transformation parameters as follows :

LPAAPAX TT 111 )( (5)

where X = matrix of the unknown transformation parameters X = Tdcba ;

A = matrix of coefficients =

10

01

ii

ii

xy

yx

(i.e. matrix of observed quantities); P= weight matrix of observed quantities (assumed equal to unity in this study); L= vector of constants (also called misclosure matrix). In fact, it is the matrix of measured ground control points; and i= designation number of control point "i".

The solution of the linear equations could be done by several methods e.g. using Matlab, Excel packages with add-ons, or individual user programming. The authors compiled a FORTRAN program to affect the solution of these linear equations. However, in this study, only two control points are used for the computation of the transformation parameters (without redundancies). This is believed to give a better picture of the internal geometric accuracy of the images[2,9].

6.4 Polynomial Transformation

The polynomial transformation test computes the unknown transformation parameters and these are then used to derive the ground coordinates of the check points, thus allowing derivation of the geometric strength of the imagery. The form of the polynomial of equation (2) was used for the purpose. The solution is normally carried out using least squares as in equation (5). Thus: where X = matrix of unknown transformation parameters =



Tbbbbbbbbaaaaaaaa 7654321076543210 ;

A = matrix of the coefficients =

nnnnnnnnnn

nnnnnnnnnn

yxxyxxyxyx

yxxyxxyxyx

yxxyxxyxyx

yxxyxxyxyx

3322

3322

131

311

21

211111

131

311

21

211111

100000000

000000001

.................................

100000000

000000001

n= number of control point "i"; and L= matrix of constant values = T

nn NENENE ].....[ 2211. In

this study the polynomial transformation test is carried out using the Geomatica PCI software.

7. RESULTS AND ANALYSIS

The discussion of the various results of the test carried out in this research runs along the following strands:

- Results and analysis of the geometric fidelity tests using linear conformal transformation;

- Results and analysis of geometric fidelity tests using polynomial transformation;

- Results and analysis of satellite orbital model tests;

- Results and analysis of stereo SAR tests; and - Analysis of produced orthoradar image.

7.1 Results and Analysis of the Geometric Fidelity Test Using Linear Conformal Transformation

The linear conformal transformation test is carried out using two well-defined and widely-spaced control points for computation of the transformation parameters. Check points are then used for assessing the geometric accuracy of the test images. The coordinates of the ground control points, the check points and the residuals are calculated using these computed transformation parameters. The root mean square errors of the check points were then computed to assess the efficiency of this mathematical model in transforming radar images from the image system to terrain system. These are shown on Table 4 for both the Riyadh and Kassala test areas.

Table 4 shows that the planimetric accuracy figures obtained with these Radarsat imaging modes is in the order of ±300m, pointing to the presence of large scale errors in this kind of imagery in its original unprocessed state. The reader should note that the accuracy with which the ground control points and the check points were derived is in the order of ±7.5 m. For the sake of comparison, Table 5 shows the same linear conformal transformation applied to the Spot-5 image of Riyadh and Kassala test areas. The corresponding results for the check points is P= ±29m and ±44m respectively, demonstrating clearly the superiority of the push-broom scanning technique of Spot-5 over radar as exemplified by the system onboard Radarsat.

Table 4. Results of radarsat images using linear conformal transformation.

Radarsat

Easting r.m.s.e Easting

(m)

Northing r.m.s.e

Northing (m)

Planimetric Accuracy

(m)

Riyadh test area using two control points

Fine Mode(F2) ±227 ±131 ±262

Standard Mode(S1) ±285 ±173 ±333

Kassala test area using two control points

Wide mode(W2) ±245 ±183 ±306

Standard Mode(S7) ±219 ±170 ±277

Table 5. Results of spot-5 image using linear conformal transformation.

Linear Conformal Transformation

r.m.s.eEasting

(m)

r.m.s.e Northing

(m)

PlanimetricAccuracy

(m) (Riyadh test area) ±22 ±17 ±29(Kassala test area) ±34 ±28 ±44

* number of control points used = 2

7.2 Results and Analysis of the Geometric Fidelity Test Using Polynomial Transformation

Several forms of polynomials of varied degrees were attempted, ranging from linear polynomial (affine transformation) to higher order polynomials. The number and distribution of control points used to compute the transformation parameters varies according to the order of the polynomial used. In reality, many distributions were attempted and the results reported here pertain to the patterns that gave the best results. The transformation is effected automatically by the software when the control and the check points are identified and selected for measurement. Table 6 shows the results obtained. Figures 12 to 17 show the vector plots of the residual errors of the Riyadh images F2 and S1, and similarly Figures 18 to 23 show the vector plots of the residual errors of Kassala images S7 and W2 using polynomial transformations.

Considering all these results, and noting that the geometric accuracy figures improve as more terms are added to the polynomial, there is conclusive evidence that these Radarsat images contain substantial geometric errors that have not been compensated while processing the data. Figure 24 summarizes all results of the Radarsat image for Riyadh test area and Figure 25 summarizes the results of the Radarsat image of Kassala test area. Depending on the order of the polynomial transformation, the image can be corrected geometrically using the PCI software. The quality of the geometrically corrected image is directly related to the quality, distribution and number of the ground control points used (i.e. more ground control points lead to higher accuracy figures).

A.A. Alsalman


Table 6. Results of the accuracy of the two test areas using polynomials.

Type of Transformation

Type of Test Image

LinearConformal

Transformation

First orderPolynomial

Transformation

Second orderPolynomial

Transformation

Third order Polynomial

Transformation CheckPoints n =6

Controlpoints n =9

Checkpoints n =4

Controlpoints n =11

Checkpoints n =3

Control points n =12

Check points n =3

Riyadh test area (Standard mode S1)

X (m) Y (m) P (m)

±285 ±173 ±333

±102 ±119 ±157

±74 ±110 ±132

±49 ±19 ±52

±132 ±48

±141

±9 ±3 ±10

±61 ±64 ±88

Riyadh test area (Fine mode F2)

X (m) Y (m) P (m)

±227 ±131 ±262

±2 ±205 ±206

±37 ±146 ±151

±107 ±235 ±258

±153 ±164 ±225

±24 ±13 ±28

±39 ±49 ±62

Kassala test area (Standard mode S7)

n =6 n =6 n =5 n =7 n =4 n =9 n =3 X (m) Y (m) P (m)

±219 ±170 ±277

±70 ±134 ±151

±101 ±274 ±292

±56 ±84

±101

±101 ±150 ±181

±20 ±42 ±47

±84 ±103 ±133

Kassala test area (Wide mode W2)

X (m) Y (m) P (m)

±245 ±183 ±306

±62 ±156 ±168

±114 ±296 ±317

±82 ±196 ±213

±66 ±312 ±319

±20 ±20 ±28

±50 ±88

±101

Figure 12. Vector plot of first order (affine transformation) polynomial of Riyadh F2 mode image.

Figure 13. Vector plot of first order (affine transformation) polynomial of Riyadh S1 mode image.

Figure 14. Vector plot of second order polynomial transformation of Riyadh F2 mode image.

Figure 15. Vector plot of second order polynomial transformation of Riyadh S1 mode image.

Figure 16. Vector plot of third order polynomial transformation of Riyadh F2 mode image.

Figure. 17. Vector Plot of third order polynomial transformation of Riyadh S1 mode image.



Figure 18. Vector plot of first order (affine transformation) polynomial of Kassala S7 mode image.

Figure 19. Vector plot of first order (affine transformation) polynomial of Kassala W2 mode image.

Figure 20. Vector plot of second order polynomial transformation of Kassala S7 mode image.

Figure 21. Vector plot of second order polynomial transformation of Kassala W2 mode image.

Figure 22. Vector plot of third order polynomial transformation of Kassala S7 mode image.

Figure 23. Vector plot of third order polynomial transformation of Kassala W2 mode image.

Figure 24. Type of transformation versus planimetric accuracy (Riyadh test area).

From Table 6, it is clear that the higher the image resolution the better will be the accuracy of the polynomial, with higher order polynomials resulting in more accurate fit (the reader should remember that Radarsat images are processed to different levels of resolutions; i.e. the so-called processing modes). However, higher order polynomials may introduce significant errors in the image away from the ground control points since they can be rather unstable and flimsy mathematical models.

For the sake of comparison, Table 7 shows the various transformations applied to the Spot-5 test image of Riyadh and Kassala areas. Figures 26 to 29 show the corresponding vector error plots obtained from Spot-5 data regarding the Riyadh test area, and Figures 30 to 33 show the corresponding vector plots obtained from Spot-5 data of the Kassala area.

A.A. Alsalman


Table 7. Results of the Riyadh and Kassala Spot-5 image using polynomials.

Polynomial r.m.s.e Easting

(m)

r.m.s.e Northing

(m)

Planimetric Accuracy

(m) Riyadh Area First Order Control

points ±14 ±16 ±22

Check points

±17 ±21 ±27

Second Order

Control points

±15 ±11 ±19

Check points

±18 ±17 ±25

Third Order

Control points

±7 ±9 ±11

Check points

±9 ±11 ±14

Kassala Area First Order Control

Points ±10 ±14 ±17

Check Points

±24 ±26 ±35

Second Order

Control Points

±7 ±10 ±12

Check Points

±19 ±21 ±28

Third Order

Control Points

±10 ±9 ±13

Check Points

±14 ±12 ±18

Figure 25. Type of transformation versus planimetric accuracy (Kassala test area).

Figure 26. Vector plot of linear conformal transformation of Riyadh Spot-5 image.

Figure 27. Vector plot of first order polynomial transformation of Riyadh Spot-5 image.

Figure 28. Vector plot of second order polynomial transformation of Riyadh Spot-5 image.

Figure 29. Vector plot of third order polynomial transformation of Riyadh Spot-5 image.

Figure 30. Vector plot of linear conformal transformation of Kassala Spot-5 image.



Figure 31. Vector plot of first order polynomial transformation of Kassala Spot-5 image.

Figure 32. Vector plot of second order polynomial transformation of Kassala Spot-5 image.

Figure 33. Vector plot of third order polynomial transformation of Kassala Spot-5 image.

The results reported show that Radarsat imagery

will definitely support mapping at scales 1/250000 and smaller. At this scale, the range of cartographic accuracy required is in the order of ±75 m and lower which is achievable by images used in this experiment. In comparison, the higher accuracy obtained in this experiment with SPOT data is attributed to the nature of the scanning system onboard SPOT i.e. the pushbroom system. However, radar has the extra merits of all-weather capability, large coverage, and relatively low image prices.

7.3 Analysis of Satellite Orbital Model Tests

Satellite orbital model of the PCI Geomatica and ground control points are used to correct both single and stereo Radarsat synthetic aperture radar images. The PCI executes the processing, entry of ground control and measurement of image coordinates computation of the rigorous mathematical models and the resulting residual errors for each test.

7.4 Results of Riyadh Test Area

Image coordinates of the nine control points and the three check points are remeasured using the PCI software. The satellite orbital mathematical model is computed and used to calculate the coordinates of the control and the check points. Table 8 shows the Satellite Orbital Model report of the S1 and F2 mode images of the Riyadh test area, and Table 9 shows the Satellite Orbital Model report of the S7 and W2 Mode images for the Kassala test area. Figures 34 and 35 show the vector errors for these images, respectively, using this technique. The plots are fairly random in nature as they point to different directions.

The root mean square error values for the S1 mode image are X = ±88m, Y = ±89m, giving rise to a planimetric error ofP = ±126 m; the corresponding values for the F2 mode image are X = ±80m, Y = ±56m and P = ±98m. The root mean square error values for the S7 mode image are X = ±52m, Y = ±62m, and P = ±81m; for the W2 mode image the corresponding figures are X = ±55m, Y= ±50m and P =±74m. The vector plots of the F2 mode image (Figure 35), although of lower magnitude, seem to point to almost the same direction i.e. northward, indicating existence of some sort of systematic error. The special circumstances that made this happen are unknown to the authors.

The vector plots of the residual errors of the S7 mode image of the Kassala area are shown on Figure 36. The vector plots, although slightly larger in magnitude, point to random directions, thus indicating that these errors are fairly stochastic in nature. It is then clear that the satellite orbit algorithm from Radarsat is effective in correcting this test image. To sum up then, some of these figures again show that, there seems to be some kind of systematic errors in the radar images used in this experiment since all vectors point approximately to the same direction. Elhassan and Ali[9] and Ali[2] experienced the same. The authors therefore appeal to radar system designers to investigate into this matter.

7.5 Analysis of Stereo-SAR Geometric Test

In this test, pairs of Radarsat images are used. The test was performed using ground control points and the satellite orbital model. With Geomatica orthoengine, the software uses image correlation to extract matching pixels in the two images and then employs the sensor geometry from the computed mathematical model to calculate X, Y and Z point positions. Therefore, the DEM can be built. The stereo-SAR test is carried out for both test areas (F2 and S1 image of the Riyadh and the S7 and W2 images of the Kassala test area). Tables 10 and 11 show the results of stereo images of the Riyadh and Kassala test respectively. The results are tabulated as residual errors in easting (X), northing (Y) and in elevation (Z) for each test area.

A.A. Alsalman


Table 8. Residual errors of satellite orbital model Riyadh test area.

Control points Check pointsPoint number 8 14 4 7 13 5 11 12 3 1 6 10Residual error

(m) S1 Mode 70 45 31 17 12 11 11 7 2 188 98 23F2 Mode 56 46 81 47 83 11 17 16 13 74 111 104

Table 9. Test results of Kassala area using satellite orbital model algorithm.

Control points Check points Point number 9 1 7 6 8 3 5 2 4 10 11 12 13 14 15 Residual error (m)

S7 Mode 1 81 74 45 30 22 15 10 5 2 6 31 57 98 109 W2 Mode 68 63 63 54 25 4 3 1 1 1 7 24 69 95 87

Figure 34. Vector plot of Riyadh S1 mode image using satellite orbital model algorithm.

Figure 35. Vector plot of image F2 of Riyadh area using satellite orbital model algorithm.

Figure 36. Vector plot of residual errors of Kassala area mode S7 image.

Figure 37. Vector plot of residual errors of W2 image of Kassala area.

Inspection of Table 10 shows that for the control

points of the Riyadh area, the root mean square error of X and Y is in the order of ±12m with a planimetric vector error of ±17m. The corresponding values at the check points are X = ±49m, Y = ±33m and P =±59m. The root mean square error in elevation for the control points is in the order of ±3m while the check points gave a corresponding figure of ±8m. Table 11 which list the results of the Kassala area shows that for the control points, the root mean square error of X is in the order of ±25m while that of Y values amounts to ±21m with a planimetric vector error of ±33m. The corresponding values at the check points are X = ±34m, Y = ±44m and P = ±56m .According to map accuracy standards, these figures are commensurate with mapping requirements at scales 1/100000 to 1/200000 (Figures 49 and 50). The root mean square error in elevation for the control points is in the order of ±3m while the check points gave a corresponding figure of ±6m. These figures show clearly the suitability of stereo-SAR tests in deriving metric information from radar data.

As an extension to this test, the orthoengine part of the PCI software was utilized to construct a digital elevation model of the test areas using the calculated coordinates X, Y, Z from the stereo-SAR test. For the Riyadh area, this is shown on Figure 38 with the red colour showing areas where the software failed to find appropriate image matches. Although this figure does not look so satisfactory, it does demonstrate the possibility of generating useful DEMs from side-



Table 10. Accuracy results of stereo-SAR mapping of Riyadh area

Point ID X (m)

Y (m)

Z (m)

Residual Error (m)

Control Points 4 21 -14 2 30 8 17 -1 6 18 13 -13 17 -1 21 14 -17 -7 -34 19 7 -9 19 -1 21 11 5 -16 1 17 5 -2 -11 1 11 12 -1 9 1 9 3 3 -6 1 7

X =±12 m Y =±12 m Z =± 3m P =±17m

Check Points 1 50 32 -11 61 6 51 25 4 57 10 46 41 -8 62

X =±49 m Y =±33 m Z =±8 m P =±59m

Table 11. Accuracy results of stereo-SAR mapping of Kassala area

Point ID X (m)

Y (m)

Z (m)

Residual Error (m)

Control Points 7 -50 -30 -7 59 8 42 23 5 48 1 47 8 3 47 6 -24 -35 -12 44 9 6 33 12 36 3 -3 5 -11 6 5 11 -34 1 4 4 1 -1 1 2 2 -1 1 -1 2 10 1 1 -1 1 11 2 1 5 6

X =± 25m Y =±21 m Z =±3 m P =±33m

Check Points

15 22 56 5 60 12 34 42 4 54 13 47 36 7 59 14 28 39 5 48

X =±34 m Y =±44 m Z =±6 m P =±56m looking radar images which may serve a multitude

of purposes e.g. reconnaissance surveys, thematic mapping applications and other surveys of similar nature.

Figure 39 shows the digital elevation model of the Kassala test area image, again the red areas representing the failed areas due to dissimilarity in the stereo images. As will be noted, the percentage of failure areas is much larger than that found in the Riyadh test area. An immediate reason for this pertains to the topographic nature of this Sudanese territory where most parts of the test image are fairly flat and contain few prominent cultural, man-made or natural features the presence of which would have eased the process of digital matching and/or correlation.

Figure 38. Digital elevation model of Riyadh test area obtained from stereo-SAR test "failed areas in red".

Figure 39. Digital elevation model of Kassala test area obtained from stereo-SAR test "failed areas in red".

The generated digital elevation model contains

pixels with failed or incorrect values. These need further editing in order to fill in the failed areas, smooth out the irregularities and create a more pleasing DEM as shown in Figures 40 and 41 for the Riyadh area. The editing of generated DEMs is carried out using the orthoengine tools of the software. This process includes noise removal, hole filling, interpolation and smoothing of the DEMs. Figure 42 shows the raster digital elevation model of the Kassala test area. The elevation information is shown in the accompanying legend, and Figures 43 (a, b) show rendered samples of the digital elevation model of the Kassala test area.

A.A. Alsalman


Figure 40. Edited digital elevation model of Riyadh test area (raster).

Figure 41. Digital elevation model of Riyadh test area "rendered sample".

Figure 42. Raster digital elevation model of Kassala test area.

A quick glance at all these versions of DEM

representations shows that orbital radar imageries, such as those acquired by Radarsat, may provide useful visual relief impressions or visual ground relief presentations that may serve useful purposes in thematic mapping applications where stringent accuracy figures are not of paramount importance. Such imageries may be useful addendums as ‘radar hyper links’ in geographic information systems. The authors were not able to investigate these possibilities because of the difficulty and/or non-availability of such systems for the test areas of this study.

(a) Rendered sample of the DEM of Algash basin Kassala test area.

(b) Rendered sample of the DEM of the eastern part of Algash basin Kassala test area.

Figure 43. Rendered digital elevation model of Part of Kassala test area (Algash River and environs).

7.6 Stereo-SAR Three Dimensional Viewing Test

In the present test, a new layer is created using the orthoengine tools, and all the required settings (adjusting the alignment in the stereo viewer, creating a vector layer and editing the vector layer) are performed. Using anaglyph technology, the epipolar images are loaded for the Kassala test area and the stereo cursor is used to pick up the points. The elevations of twenty points down stream and 9 points up stream were measured. Tables 12 and 13 show the bank levels measured by this method and line levelling methods (up-stream and down-stream respectively) from the Algash bridge. Figure 44 shows a comparison of the profile of the Algash bank levels down-stream and up-stream from line leveling and stereo radar data.

The two sets of elevation data for the two profiles were then compared in order to calculate the standard deviation of height measurement using Radarsat imagery. The results are as follows: Up-stream r.m.s.e in levels is ±2m, Down-stream r.m.s.e in levels is ±1m. In this test, the accuracy of the levels seems to be dependent on the satellite orbital model used and is greatly affected by the experience, skill and ability of the operator to perform stereoscopic viewing.



Table 12. Algash bank levels up-stream.

Chainage (m)

Ground Levelling

(m)

Stereo- SAR Test (m)

Chainage (m)

Ground Levelling

(m)

Stereo- SAR

Test (m)

0 515 516 4600 508 511500 515 515 5100 508 5111000 513 515 5600 508 5101500 513 515 6100 507 5092100 512 514 6600 506 5072600 512 513 7100 506 5073100 511 513 7400 505 5073600 509 512 7600 509 5064100 508 512 7900 505 506

Table 13. Algash bank levels down-stream.

Chainage (m)

Ground Levelling

(m)

Stereo- SAR Test (m)

Chainage (m)

Ground Levelling

(m)

Stereo-SAR Test (m)

100 505 505 10000 496 494600 505 505 10500 495 4941100 504 503 11000 494 4931600 502 503 11500 493 4932100 503 503 12000 494 4922600 504 504 12500 492 4913100 501 501 13000 494 4933600 500 499 13500 492 4924100 501 500 14000 490 4894600 500 498 14500 491 4915100 500 498 15000 490 4895600 501 498 15500 490 4886000 500 497 16000 490 4906500 499 497 16500 488 4887000 499 497 17000 488 4887500 498 496 17500 487 4888000 498 496 18000 486 4878500 497 496 18500 486 4879000 496 496 19000 486 4869500 496 495

7.7 Orthophoto Test Results

When orthorectifying SAR images, it is expected that most systematic errors were eliminated (e.g. inner orientation and outer orientation errors) using the orbital mathematical model. As regards relief displacement, the generated DEM is used to eliminate its effects. In this test, the Radarsat images of the two test areas were geometrically and radiomatrically corrected. The test was carried out using the satellite orbital model to correct for sensor position, and the generated digital elevation model to correct for relief displacement using the PCI orthoengine. The orthorectfied images S1 and F2 of the Riyadh test area are shown in Figures 45 and 46 respectively, and the corresponding orthorectfied images S7 and W2 of the Kassala test area are shown in Figures 47 and 48 respectively.

Figure 44. A comparison of Algash bank level Profile up-stream

and down-stream as derived from stereo-SAR and ground line Levelling.

Figure 45. Riyadh standard mode S1ortho-image.

Figure 46. Riyadh fine mode F2 ortho-image.

A.A. Alsalman


7.8 Testing the Geometric Accuracy of the Orthorectified Images

At this point, a keen reader may like to enquire about the accuracy of the orthorectified images produced. For this sake, a further test was carried out to explore this issue. The coordinates of the control points and the check points used in the series of the previous geometric tests were again measured from the orthorectfied images and used for testing the accuracy of the orthoimages using the PCI software. Table 14 shows the accuracy results obtained.

Table 14 then shows that the geometric accuracy of Radarsat images of the test areas is greatly improved after orthorectification. This is true for all images used in the test. Thus the planimetric accuracy of the Riyadh test area images ranged from ±12m with the control points for mode F2 to±31m for standard mode S1 when the check points are used for the solution. The corresponding figures for the Kassala test area are ±13m with control points to ±115m for check points. In both cases, this is a large improvement when compared with most values shown on Table 6. It should be noted that the accuracy of the orthorectified synthetic aperture radar imagery is dependent on many factors e.g. the accuracy of the digital elevation model, the satellite orbital model used and the resolution of the imagery. Figure 49 shows the relationship between map scale and Radarsat orthoimage accuracy obtained while Figure 50 presents the well-known relationship between map scale and map accuracy[10].

Figure 49 and Table 11 show that orthorectified fine mode Radarsat imagery would adequately support mapping at 1/50 000 scale, while the standard mode will be sufficient for mapping at scales of 1/100 0000 and smaller. Images of the W2 mode seem to be only suitable for very small scale mapping applications (1/400 000 and smaller) i.e. those thematic applications requiring only modest positional accuracy in the order of ±110m to±150m. These conclusions were reached by Elhassan and Ali[9] for a Russian Almaz SAR image taken over the Riyadh area. The reader is referred to this publication for details on the merits and demerits of Almaz imagery compared with Radarsat images used in this study and the accuracy levels arrived at by those two investigators.

Figure 47. Kassala standard mode S7 ortho-image.

Figure 48. Kassala wide mode W2 ortho-image.

Figure 49. A Comparison of Cartographic and Radarsat Accuracy.

Map Scale

Figure 50. Map scale versus map accuracy[2].

Table 14. Accuracy of the orthorectified SAR images.

Radarsat Imagery

Resolution (m)

X (m) Y (m) P (m)

ControlPoints

CheckPoints

ControlPoints

CheckPoints

Control Points

CheckPoints

Riyadh test area Mode F2 9 ±10 ±12 ±7 ±7 ±12 ±15 Mode S1 13 ±15 ±16 ±21 ±26 ±26 ±31 Kassala test area Mode S7 13 ±13 ±15 ±22 ±20 ±26 ±25 Mode W2 25 ±53 ±67 ±48 ±94 ±72 ±115

0

20

40

60

80

100

120

140

160

1/300000 1/10000 1/5000001/4000001/200000 1/100000

Map

Acc

urac

y



8. CONCLUSIONS

The present work is concerned with the investigation of Radarsat imagery for deriving metric information for the purpose of compiling topographic maps at medium to small scales. For this purpose, a number of Radarsat images with different imaging modes and covering the City of Riyadh in Saudi Arabia and Kassala Town area in Sudan were available to the authors. Collateral and supplementary data were available in the form of medium scale paper maps, digital maps, control point coordinates and field visit reports. A series of geometric accuracy tests using various mathematical models was then carried out first using an in-house written FORTRAN programme and then the various modules of a Geomatica PCI Software package. The following results were obtained:

1. Metric information satisfying 1/50000 scale mapping requirements could be obtained from Radarsat imagery if fine image F2 data was used. The geometric accuracy actually achieved depends heavily on the mode initially used to acquire the data and on the method of processing. In this respect, the results obtained with the fine mode F2 proved to be superior to that of data acquired using the standard mode S1. The effects of topographic relief variation present on the ground are also noticeable on the images tested. Also, the images seem to contain scale errors which have not been compensated for during processing. This is clear from the rather poor accuracy figures obtained after applying the linear conformal transformation algorithm and the improvement of these figures when applying the affine transformation solution.

2. The standard mode imagery S1 gave metric accuracy figures commensurate with mapping requirements at scales 1/100000 and smaller.

3. The stereo-SAR three-dimensional viewing test gave accuracy values of about ±2 metres.

There is no doubt that there are a number of unsolved problems of varying nature and importance that still exist in the development of ways and means of mapping from satellite-borne synthetic aperture radar images.

1. Some of the limitations and uncertainties encountered in the experimental work carried out in this present study arise from the lack of a large test field of suitably signalized points with accurately known positions. Any future researchers into this subject are recommended to invest in such a field of reflectors since it would eliminate many of the ambiguities and difficulties which arose from the use of poorly-defined natural points such as road intersections, woodland corners, building edges, etc. The use of such a field of targeted control points would make it possible to judge the

effectiveness of particular rectification procedures without the additional uncertainties arising from the difficulties in accurately locating and measuring the control points.

2. The vector plots of the residuals obtained throughout the series of tests carried out by the authors displayed a highly systematic pattern. This was apparent even after application of polynomial corrections. Since the reasons for these residual errors are not well apparent, further work is recommended to find the source of these errors.

Thus the results obtained in this present research and the recommendations made for further future work will be a contribution towards a better understanding of the possibilities of satellite-borne SAR systems for the purpose of topographic mapping.

REFERENCES

1. Van der Sanden, J.J. and Ross, S.G. (2001). Applications Potential of Radarsat, A Preview. http://www.ccrs.nrcan.gc.ca.

2. Ali, A.E. (1982). Investigation of Seasat-A Synthetic Aperture Radar (SAR) for Topographic Mapping Applications, A Ph.D Thesis, University of Glasgow, 460.

3. Toutin, T. (2003). Review Article: Geometric Processing of Remote Sensing Images: Models, Algorithms and Methods, International Journal of Remote Sensing, 25, 1893-1924.

4. Ali, A.E. (1986). Digital Monoplotting Experiment from Orbital Radar Imagery, Australian Journal of Geodesy, Photogrammetry and Surveying, 44, 61-77.

5. Ali, A.E. (1987a). Geometric Accuracy Testing of Orbital Radar Imagery, Photogrammetric Engineering and Remote Sensing (U.S.A.), 53, 1533-1537.

6. Ali, A.E. (1987b). Mathematical Modelling of Side-looking Radar Imaging Geometry and Errors, International Journal of Computers and Mathematics with Applications, 14, 2, 91-117.

7. Elhassan, I.M. (1990). Population Estimation of Riyadh City Using Aerial Photography, Australian J. Geodesy, Photogrammetry and Surveying, 53, 75-90.

8. Ali, A.E. (1993). Population Estimation from Aerial Photographs: Riyadh Case, J. Urban Planning and Development, 119, 4, 190-197.

9. Elhassan, I.M. and Ali, A.E. (1995). Radargrammetry with Almaz SAR Imagery, Geomatics Research Australasia, No. 62, June 1995, 63-92.

10. Keates, J. (1986). Cartographic Design and Production. Longmans (London) 346.

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