Adv.SpaceRes.Vol. 7, No. 11, pp. (11)281—(11)284,1987 0273—1177/87 $0.00+.50Printedin GreatBritain. All rights reserved. Copyright© 1987 COSPAR

SIMULATION OFSPACEBORNESARIMAGERY FROMAIRBORNESAR DATA*

H. Kimura,N. MotomuraandN. Kodaira

RemoteSensingTechnologyCentreofJapan(RESTEC),7-15-17,Roppongi,Minatoku, Tokyo106,Japan

ABSTRACT

This paper discusses the simulation of the spaceborne SAR imagery using airborne SAR dataobtained by the SAR—580experiments of 1983 in Japan. Simulation parameters are a spatialresolution, a received signal—to-noise ratio, number of bits of raw data sample and number oflooks. Simulation method is described, and first analysis results are given on the receivedsignal—to—noise ratio.

INTRODUCTION

This simulation has been executed as a part of the research and development program ofJapanese Earth Resources Satellite—i (ERS—1). Purposes of this simulation study are toevaluate the radar parameters and to make clear the system design for resources explorationusing spaceborne Synthetic Aperture Radar(SAR). RESTEC has already developed digitalprocessors for Seasat SAR and CCRS SAR-580 data /i,2/. This simulator has been built basedon these results.

Simulation parameters are a spatial resolution, a received signal—to—noise ratio, number ofbits of raw data sample and number of looks. Recently, a considerable concern was caused Inthe relationship between the received signal-to—noise ratio and image quality /3/. Thispaper describes and demonstrates two (S/N)r simulation methods and the first analysis resultsof (S/N)r simulation images are given. L—Band HH SAR—580 data obtained by the experiments of1983 in Japan /2/ were used as the input data.

SIMULATION METHOD

Spatial resolution

The spatial resolution of the SAR is proportional to a signal bandwidth. Range resolutiondepends upon the bandwidth of a chirp pulse, and azimuth resolution depends upon thebandwidth of a doppler signal caused by a relative motion between the SAR and a surfacetarget. Generally, the resolution of the airborne SAR is finer than that of the spaceborneSAR. To simulate the spaceborne SAR resolution, a reduction of signal bandwidth is performedwith a lowpass filter in the process of Imaging. The bandwidth of the filter is defined bythe destined resolution. The range resolution is constant over the axis of slant range.Note that the range resolution will vary with a ground range distance in the image of groundrange presentation.

Received signal—to—noise ratio

The received signal—to—noise ratio is defined as:

(S/N)r = Pa / Pr (1)

where Ps and Pr are the average signal power and the average receiver noise power,respectively. In this paper, two (S/N)r simulation methods are studied. One Is a precisemethod which add an artificial noise to a original raw data and then compress it. The otheris a simple method which add an artificial noise to processor output image data. Blockdiagrams of both methods are shown In Fig. 1.

Precise method. A normally distributed white noise is added to each of the in phase andquadrature components of the complex raw data. Consideration for an amplitude distributionof radar shadow given In Fig. 2 shows that normally distributed noise is available for theartificial noise. A schematic representation of the SAR-58O received signal spectrum isgiven in Fig. 3. S(f) is the spectrum of a true return signal, R(f) Is the spectrum of areceiver noise. When the bandwidth for the destined resolution is 2V, Ps and Pr are:

*This study is a part of the national research and development project on observation system

for Earth Resources Satellite-i, conducted under the program set up by the Agency of

Industrial Science and Technology, Ministry of International Trade and Industry.

(11)28 1

(11)282 H. Kimura, N. Motomuraand N. Kodaira

(SIN)r (SIN)’r (SIN)’o

SIMULATION~SIMULATIoN LSJPROCESSINPUTI ‘RECEIVER 1 ‘A/D CONVEI~~’‘IMAGING 1~~~PUT~

Ldata noise) noise) noise)‘raw (receiver ](~uantization~ ‘J(Processor image :

(a) Precise Method ~

(S/N)r (S/N)o (S/N)’o

‘T~’‘IMAGING PROC~NOISE SIMULATION ~-,

raw (processor (receiver noise and IJ~OUTPUTIAMPLITUDE~ noise) quantization noise)

1 ~imageFig. 2. Amplitude histogram of radar(b) Simple Method shadow from L—band SAR-580 image. Thecurve is amplitude distribution drawn on the

Fig. 1. Block diagrams of two methods for received assumption that real and imaginary partssignal—to—noise ratio simulation, of complex noise is normally distributea.

SPECTRAL

TABLE 1 Normalized Quan— INTENSITY ________________________________

Normally DistributedSignal with Ideal Quan-tization R(f) (a) precise method

Normalizedtization Noise Power forBits Quant~zation R(f) ~_.__

Noise — -

1 0.36342 0.11753 0.03454 FREQUENCY4 0.0094975 0.002499 Fig. 3. Spectrum of the SAR—580 (b) Simple method

received signal. S(f) Is the spectrum Fig. 4. Example of received*Normalized by input of the true radar return signal. R(f) signal—to—noise ratio simu—

signal power is the spectrum of the receiver noise. lation results.

Ps = f_~lS(ffl2df / As Pr f_~IRnI2df / As (2)(3)

where As is the number of pixels of Imaging area. If no artificial noise is added to theoriginal raw data, (S/N)r can be expressed as follows:

(S/N)r = f_~ls(nI2df / f_~lR(f)I2df (4)

If the spectrum of the additional noise Is R’(f), the simulated (S/N)’r is written as:V~S(f)t2df I f_~(IR(f)I2+IR’(f)l2)df (5)

(S/N)’r =

If the bandwidth of the original raw data is 2U, the total power of the noise to add to theoriginal raw data for (S/N)’r simulation at the first step Is given by:

Pn = f_~IR’(f)l2df (6)

Simple method. This method assumes that the output signal-to-noise ratio (S/N)o is equalto the received signal—to-noise ratio (S/N)r, because the signal power and the noise powerare conserved before and after the process of imaging and processor noise Pp is small enoughto neglect. The original (S/N)r can be estimated as:

(S/N)r = ( Po — Pr ) / Pr (7)

where Po and Pr are the average output power and the average noise power, respectively. Pris measured at a radar shadow or so. When the average additional noise power is Pr’, thecorresponding (S/N)’r can be written by:

(S/N)’r Ps / ( Pr + Pr’ ) (8)

Normally distributed noise is added to each of real and imaginary parts of image data tosimulate (S/N)’r according to equations (7) and (8). Total additional noise by this methodconsists of the receiver noise and the quantization noise. The quantization noise will bedescribed in the next paragraph.

Number of bits of raw data samole and looks

Quantizatlon noise power varies with the number of bits of raw data sample. This simulatorassumes the ideal quantization for a normally distributed signal. Table 1 shows thenormalized noise power against the number ofquantization bits /4/.

Simulation of Spaceborne SAR Imagery (11)283

TABLE 2 Study Area Properties and Simulation Parameters_______________________________________________ 515

Area Simulation Parameters* (S/N)r~~

Name Description Rs S/N Bt Lk

Cm) (dB) ,, r = 0.986

Futatsui Residential and mountain area 18 3,6, 3 3with anticlinal structures 10,19

Kosaka Residential and mountain area 18 3,6, 3 3 ~with a collapse structure 10,16

Hanawa Residential and mountain area 18 3,6, 3 3 ~with faults 10,18, 1~ _~ 0 5 10 15

Hachimantai Gently sloping area in 18 3,6, 3 3 OUTPUT POWERHO PRECISE METHOD(dB)

geothermal field 10,16 Fig. 5. Normalized averageOhshima Volcanic island with caldera 18 3,6, 3 3 output power by the precise

at the top 10,16 (S/N)r simulation method and

* that by the simple method.Rs=Resolution, S/N=(S/N)r, Bt=Bits and Lk=looks Average power of each lOOm

square is normalized and 0dBThe precise (S/N)r simulation method simulate the ideal corresponds to the powerquantization for the normally distributed signal. The relation which occurs most frequentlybetween threshold and quantization level is given in the image.theoretically. For the simple (S/N)r simulation method, thequantization noise Is added to the image data. The average quantization noise power becomes

Pq = e( Ps + Pr + Pr’ ) (9)

where e is the normalized quantization noise power shown in Table 1. If the number of bits of

original data is 5 or more, original quantization noise can be negligible.

For the simulation of the number of looks, the look extraction process, azimuth compression

and look summation Is executed in the usual way.

APPLICATION

Spatial resolution

The acutual resolution of 1 look L—Band SAR—580 data is 2m in slant range and 3m in azimuth.When a incident angle at a center of range is 45 degrees, the ground range resolution becomes3m. To simulate 3 look and i8m resolution image at the center of range, the reduction ratioof the signal bandwidth is 1/6 for range process and 1/2 for azimuth process.

Received signal—to—noise ratio

Two (S/N)r simulation methods are demonstrated. Fig. 4 shows the results. Both imagesproduced by two (S/N)r simulation methods look very similar. To compare the results quanti-tatively, a correlation of output power between two results is investigated. Fig. 5 shows ascatter plot of normalized average output power by two methods. Correlation coefficient rcomes to 0.986. This means that analogous results can be generated by both methods. Thesimple method is more convenient than the precise one. When several (S/N)r values aresimulated, the processing time of the simple method is much shorter than that of the precisemethod. It is necessary for the simple method to estimate the original noise power correctlyfrom the radar shadow or so.

SIMULATION RESULTS AND ANALYSIS

Results

Some images of typical land features except urban area are simulated from SAR—580 data.Study area properties and simulation parameters are shown In Table 2. Especially this studyconcentrates on a problem of the received signal-to—noise ratio. The images of 3, 6 and 10dBin (S/N)r are simulated for each study area. Fig. 6 shows the examples. Fig. 7 is a scatterplot of normalized average output powers from high (S/N)r image and three low (S/N)r Imagesfor Futatsul area. High (S/N)r image is produced by adding no artificial noise. High (S/N)rvalue is 16 to 19dB. Fig. 7 shows that noise equivalent power Fe becomes higher as (S/N)r Islower. Fig. 8 is a histogram of normalized average output power from high (S/N)r Image forFutatsui area. The noise equivalent power Pc of each three low (S/N)r images are indicatedin the figure.

Analysis

Special concern of this study Is the relationship between the received signal—to—noise ratioof the SAR data and the loss of land surface Information. In this paper, the degree of theInformation loss is defined as the amount of pixels for which radar cross section is lessthan noise equivalent cross section. It can be thought that the non—cumulated histogram ofoutput power from high (S/N)r image in Fig. 8 almost corresponds to the probability densityfunction of radar cross section In the study areaand the noise equivalent power correspondsto the noise equivalent cross section. Therefore, the cumulated frequency less than the the

(11)284 H. Kimura, N. M000mura andN. Kodaira

Fig. 6. Examples of simulation results.

15 — — I —0,..

1~1T12~ ~j TABLE2 InformationI 0.33 Loss Percentage

1C — — . ~ ~ 0.15 r ~ against the Received10.06 ___~ Signal—to—noise Ratio

= — — ..,~ — pe~3~’~’\ Area- 3 ~ Ipel (6dB),,~ \

~ ~. ~ :j~j (1OdB)_~(’ Futatsui 6 15 33______ — ..=44~I..Jj_ _-_ . Kosaka 4 11 33

~ j~ ,/‘ -15 -10 -5 0 5 10 Hanawa 4 12 28— _..~ __ — OUTPUT POWER(dB) Hachimantai 7 17 33

Fig. 8. Cumulated and non- Ohshima 6 13 25~-15 — cumulated histogram of normalized

_____ — — _._. average output power from the high Mean 5 13 30OUTPUT POWER HIGH (S/N)r IMA (dR) (S/N)r image for Futatsui area.

Fig. ~g~N zedaveraou ~ noise equivalent power Pc in Fig. 8 shows the degree of

images of 3, 6, 10dB in (S/N)r for the information loss percentage due to the noise.Futatsui area. Average power of each70m square is normalized and 0dB Table 3 shows the information loss percentage versuscorresponds to the power which occurs the received signal—to—noise ratio from simulationmost frequently in the high (S/N)r image. images by the same manner. Input data for the area 2 to

4 is from the same flight pass, then the noiseequivalent power of the area 2 to 4 is defined by that of the area 3, where the averagereceived signal power Ps Is lowest of the three areas. The difference of the informationloss percentages of area 2 to 4 is due to the difference of land features among three areas.The probability density function of radar cross section depends upon land features. The lossof land surface information reach to about 30% in case of (S/N)r of 3dB. The loss values for(S/N)r of 6 and 10dB go down to about 15% and 5%, respectively. Note that the average noisepower is equal to the average received signal power in case of (S/N)r of 0dB.

CONCLUSION

Simulator for certain parameters of SAR is developed. Especially two (S/N)r simulationmethods are studied. Analogous images can be produced by both methods, while the simplemethod Is convenient for several (S/N)r values simulation. The analysis of (S/N)r simulationimages shows that the loss of information comes to about 30% in (S/N)r of 3dB for thegeneral land surface.

ACKNOWLEDGEMENT

The authors wish to thank Dr. H. Hirosawa for his many thoughtful suggestions made in the

course of this work.

REFERENCES

1. A.Tsubol, T.Iijlma, H.Kimura and N.kodaira, A SAR image autofocusing using lineardistortion azimuth matched filter, in: Proc. 13th ISTS, Tokyo 1982, p.1299—1304.

2. NASDA, RePort of the SAR-580 Exoeriments in Japan, NASDA Publlcatlon,1986.3. H.Hirosawa, Quantitative Representation of SAR Image Qualities, Journal of The Remote

Sensing SocietY of Japan, Vol. 5, No. 3, 225—234 (1985)4. S.Arimoto, Digital ProcessIng of signal and ima~e(in Japanese), Sangyo Tosho, 1980.