Evaluation of the Effects of Encoding on

AbstractSynthetic Aperture Radar (sAR) data are becoming increas-ingJy important in arcas sucfr os Earth resource monitoring,and sAn data arc now being collected on a routine basisfrom both satellite and airbome platforms. The volume ofdata collected is very large, and this has stimulated interestinto methods of comprcssing both the received and theprocessed sln data. It is common practice rn sucfi studies touse only the mean sguare enor (ust) and uisual inspectionof the rcconstructed imagery to determine the acceptabilityof a compression technique. In this paper we show that thesecriteria are insufficient, and suggest a number of additionalmetrics which aid in the interprctation of the type of enorintoduced, and in the underctanding of the mechanism thatproduced the error.

lntroductionSince the launch of rns-r in Juiy 1991 and of pns-r in Febru-ary 1992, Synthetic Aperture Radar (saR) data have been re-ceived in large volumes on a routine basis (approximately200 GBytes per day). In addition, four other san satellite sen-sors are planned for launch in the next decade. Data com-pression is needed because of the limitations of on-boardtape recorders, downlinks, gxound archives, and data dissem-ination networks.

Many compression algorithms have been studied for im-age data collected by optical sensors. sAR data, however,merit study on their own, because their statistical propertiesare quite different from those of data from other sensors.Most notably, they have a greater htgh frequency content anda lower signal-to-noise ratio (swn) than most other sensors.Thus, compression algorithms which are optimal for othersensors may require modification before the best perform-ance is achieved for SAn data. Also, evaluation metricswhich suffice for other data sets may be insufficient to un-derstand the encoding performance with san data. Therehave been a number of studies undertalcen on the com-pression of san data, for example, Chang ef a/. (1988), Frostand Minden (1980), Gioutsos (1989), |ones ef o1. (19s8), andRead et o1. (1988a;19BBb), but nod.e of these have addressedthe importance of the evaluation criteria.

This paper resulted from a contract with the EuropeanSpace Agency (nsa) to design a Data Encoding EvaluationSystem for SAR data. Thus, the emphasis was on building asystem with thorough evaluation capabilities which wouldbe used for future algorithm development. This part of thestudy did not include the actual development or enhance-ment of encoding algorithms; however, in performing thiswork, the authors realized that encoding performance evalua-

MacDonald Dettwiler & Associates, 13800 Commerce Park-way, Richmond, B.C. VBV 2I3, Canada.

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SAR DataMelanie Dutkiewicz and lan Gumming

tion was very important to the design and optirnization ofsuch algorithms, and that this topic had received little atten-tion in the past. In this paper, we outline tle metrics recom-mended for the evaluation of san data eucoding algorithms,and illustrate how some of the metrics were helpful in un-derstanding algorithm performance.

The main distinguishing feature of saR data is that theyare received at the satellite in the form of a hologram (theinformation governing the spatial distribution of energy inthe image is stored in the phase of the received data). Theseinitial received or "signal" data have a frequency spectrumsimilar to that of white noise. A numerically intensive con-volutional process is required to form the image, i.e., convertthe signal data into a recognizable scene. The image formq-tion is done on the ground, rather than on board the satellite.The image product may be either "single look" complexdata, or t'multi-look" detected data, where multi-look dataare produced by processing selected azimuth frequencybands and averaging the detected results. The product of in-terest for the work reported in this paper was multi-look im-age data, because they generally are the high-volume productin operational san processing.

sAR signal data and multi-look saR image data havequite different statistical properties, particularly with regardto their dynamic range, sample-to-sample correlation, andoverall redundancy. For this reason, and for operational rea-sons, the encoding of san data must be studied in two dis-tinct parts: (a) signal data encoding and (b) (multi-look)image data encoding. It is likely that the optimal encodingalgorithm will be different in the two cases, and that the en-coding performances will be quite different. It is also ex-pected that not all of the metrics which are used foranalyzing the effects of encoding on image data may be ap-propriate for analyzing the effects of encoding in the signaldata domain, and vice versa.

Evaluation MetricsThe evaluation environments for san signal and san imagedata are given in Figures 1 and 2, respectively. As Figure 1shows, the analysis of san signal data encoding has been di-vided into two parts. First, the effects of encoding in tle sig-nal domain will be analyzed in the signal data domain itself,by comparing original and reconstructed signal data setsusing the metrics listed in the section on Signal DomainMetrics. Second, the original and reconstructed signal data

Photogrammetric Engineering & Remote Sensing,vol. 60, No. 7, July 1994, pp. 895-904.

oo99-7tt2l I 41601 1-000$03.00/001994 American Society for Photogrammetry

and Remote Sensing

sets will each be processed, and the "original" and "recon-structed" images ttrus obtained will be analyzed and com-pared in the image domain using the metrics given in thesection on Image Domain Metrics.

For the analysis of san image data, only image domainevaluation is applicable, because, as Figure 2 shows, the sig-nal data are processed before encorling.

SignalDomain MetricsThe properties chosen for evaluation in the signal data do-main are given below. Note that each of the measurements isapplied to (a) the original and (b) the reconstructed data set,and the results of (a) and ft) compared, and/or (c) applieddirectly to the error data set, which is obtained by subtract-ing the reconstructed data from the original data.

Data StatisticsComparison of data range, mean, standard deviation, kurtosis(Press et a/., 1988), and zero order entropy (Curlander andMcDonough, 1991, p. 288) of the original and reconstructeddata sets wil highlight changes in the data characteristics.

Oiginal SAR Sig"al Data

Image hage

FigUre 1. Evaluation environment forSAR signal data encoding.

Original SAR Signqt P6ta

Image Inage

figare 2. Evaluation environment forSAR image data encoding.

Analysis g,f the error data statistics, e.g., mean square error(MsE), will show the characteristics of the error signal.

Signal to Quantization Nor'se flotro ISQNffJThe signal to quantization noise ratio is a measure of the sig-nal--to-noise ratio (svn) due to the encoding process. Its sig-nificance is relative to the original data SXn.-It will beexpressed in two ways: (1) Peak Signal to Quantization NoiseRatio (psQNR), defined as the ratio, in dB, of peck signal toMSE; and (2) Average Signal to Quantization Noise Ratio(ASQNR), defined as the ratio, in dB, of average signal energrto MsE. The vse measure gives the total encoding error be--tween original and reconstructed data sets in an absolutesense. PSQNR and asqNR give relative measures useful forcomparing results of encoding between data sets with differ-ing peak and mean values, respectively.

Data HistogramsComparison of the histograms of the original and recon-structed data sets will show alterations in the probabilitydistribution brought about by coding. The distribution oi theerror will be shown in the error data set histogram.

Phase EnorPhase information is used by the processing algorithm to fo-cus-the SAR image, A measurement of the phase error presentin the reconstructed signal data set will allow comparison ofthe amount of focusing error introduced by different encod-ing algorithms.

lmage Domain MetricsThe metrics used in the image domain include those givenabove for the signal domain, except in the case wherJtheimage data are detected, when tle phase error is not applica-ble. In addition to these given metrics, the metrics listed be-low will be used. Again, each of the measurements isapplied to (a) the original and (b) the reconstructed data set,qnd tle results of (a) and (b) compared, and/or (c) applieddirectly to the error data set.

Image AppearanceCo_mparison of the-reconstructed image with the original imagewill show how well the visual quality of the image is pre-served by the encoding process. The appearance bf thi errorimage will also be analyzed for structural content, to see if theerror is correlated with any specific feature type in the originalimage, and for spatial effects such as error propagation.

SpecbaBoth the one-dimensional and two-dimensional spectra ofthe reconstructed image will be compared with tliat of theoriginal inagg, and examined for discrete frequency artifacts,difference in bandwidths, and difference in frequency con-tent. The -sqecEum of an image portrays the spatial frequencycontent of the data, and thus these results can be used 1ocorrelate image content with algorithm performance. Theone-dimensional and two-dimensional spectra of the errorimage will be examined, as these should highlight any peri-odicities in the error, and will also show whether the error isgreater for high or for low frequency image components.

Image RegistrationCross correlation between the original and reconstructed datasets, and between small corresponding regions within thesedata sets, will show if the coding has given rise to any mis-

896

registration effects, either on a global ot on a local scale, re-spectively. The latter could be important in target locationand tracking, or in mapping applications.

Image RadiometryThe changes in the radiometric properties-mean, standard de-viation, radiometric resolution, and speckle statistics-of ho-mogeneous regions, brought about by coding, will bemeasured. The radiometric linearity of the coding process willalso be determined, by plotting the mean values of homoge-neous regions in the reconstructed image against the mean val-ues of the same homogeneous regions in the original image'

P oint Target C h ar acteristi c sThe position, resolution, and energy distribution of point tar-gets or small discrete reflectors will be compared for theoriginal and reconstructed data sets.

Ap plic ati on Sp e cifi c C rite riaAs sAR data are increasingly being used in quantitative ap-plications, an important class of encoding evaluation criteriais concerned with how the encoding process affects the re-sults of the application. Examples of applications include

o wavelength estimation (e,9., in sea scenes),o edge detection (e.g., in ice scenes),. terain segmentation,. terrain classification,. motion tracking (e.g., ships in ice),o interferometry, andr polarimetry.

Each user should consider his/her own applications and ex-amine the effects of the encoding on them. For the work re-ported in this paper, the estimation of the dominantwavelength was important; thus, the magnitude and fre-quency of the main peaks in the one-dimensional range and-_azimuth spectra of the original and reconstructed images willbe compared.

Many of the evaluation metrics which are applicabll inthe image domain are not applicable in the signal data do-main. For example, because the appearance of signal datasets is similar to white noise, the effects of added quantiza-tion noise on this data will not be discernible to the eye;thus, no information would be gained by visually examiningthe signal data images. These metrics wi-ll however be ap-plied in the second part of the signal data qralysis, i.e', afterprocessing of the original and reconstructed sigaal data sets.-

It is recognized that the list of metrics used in this studyis not exhaustive. However, it is felt that the metrics selectedare sufficient to give a detailed insight into not only the mag-nitude of the error, but also the characteristics of the error.This will help us to understand and optimize the performanceof the encoding algorithms. Also, figures relating to these met-rics will i:r-form the scientist using the reconstructed SAR datawhat information may have been altered in the encoding/de-coding process, and to what extent. It is important that anyend user of a reconstructed san data set should have access tosuch information in order to decide how much trust can be putin conclusions drawn from the data.

Characteristics of Data UsedThe data used in this study were obtained from the 1978SEASAT sAR sensor. The major characteristics of this sensorare (Jordan, 1980)

o radar wavelength of 0.235 m,

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o incidence angles between 19'and 25"o nominal altitude of aoo km,o noise equivalent e of. -2t *5 dB (this implies that the sun

is typically in the 0 to 15 dB region),o sigdl dati digitized to S-bit real samples at an IF of.71.25

MHz, and. data enbopy of 3 to 4 bits'

SAR SignalDataFor the signal data encoding studies, the SBASAT data werefirst downconverted from real samples at the IF of.77.25 MHzto complex samples at baseband, because the focus of thisstudy was the rns-r sAR sensor which records complex b-ase-band data. The components of the complex data are the In-phase (I) and Quadrhture (QJ components. During the base-banding step, the data were scaled to use 8 bits instead ofthe orig-inal-s bits. The scaling w"," perftrmed in such a waythat the data entropy increased. Thus, the characteristics ofthe (baseband) san signal data are as follows:

. SEASAT, downconverted to baseband,o data type: complex I and Q samPles,o data r-epresentation: I bits per I and Q sample,o data entropy after downconversion to I bits: 5 to 6 bits,o signal data iize: 5100 lines by 1250 comple-x samples (as re-

quired to give a four-look detected image of 512 by 512 pix-els).

o data properties which may affect comp-ression:- approximately zero-mean, Gaussian distribution'- no correlation between the I and Q channels,- low inter sample correlation,- slow variation of nvs level with range, and- slow variation of nvs level with azimuth.

SAR lmage DataFor the image data encoding studies, the signal data wereprocessed uiing a Range Doppler sAR processor. The image

was processed to four looks' The characteristics of the SARimage data are as follows:

. SEASAT, four-look detected, resolution 25m, (Kaiser-Besselweighting),

o data type: real (i.e., no imaginary componentl'. data representation: 12 bits per pixel,. data entropy: approximately r0 bits'. single band,o image size:572 by 512 pixels'o data properties which may alfect compression:

- Rayleigh amplitude distribution,- very high dynamic range:

. > 50 dB for Point targets,

. > 30 dB for distributed areas,- statistics dominated bY sPeckle:

. moderate inter-sample correlation,

. Iess spectral roll-off than optical sensors,

. lower sNR than optical data.

Encoding AlgorithmsThe focui of the study presented in this paper is to describean evaluation environment which aids in tle interpretationof the type of error introduced into a sAR data set by an sn-coding process, and also aids in understan{ing thg m9+"-nism lhat produced the error. This part of the study did notinclude thil actual development or enhancement of encodingalgorithms. Thus, the coding algorithms used in setting qpth-e environment have been selected from existing algorithmswhich have previously been applied to sAR signal or image

data. Two encoding algorithms have been used in each partof the investigation:

(7) for encoding in the signal domain:JPL's Block Adaptive Quantization (naq) algorithm (as

used on the Magellan mission to Venus), and UnstructuredVector Quantization (wO have been compared.

(2) for encoding in the image domain:the IPEG Discrete Cosine Transfonn (oCt) algorithm and

WQ have been compared.

BAQ Algodthm:The BAQ algorithm (Kwok and |ohnson, 1989) was selectedfor analysis because of its simplicity, and because it hasgiven very good results for sAn images obtained on the Ma-gellan mission. The simplicity is important when on-boardapplications are considered. The algorithm has been de-signed specifically for the zero-mean Gaussian statistics ofSAR signal data, and is t}erefore not suitable for the Rayleighdistribution of multi-look SAn image data.

The block size used for naq in this study was set to bethe same as that used on the Magellan mission, i.e., 16 com-plex samples by 64 azimuth pulses. The overhead in trans-mitting block tbresholds for this block size is small, and thusthe data are compressed to close to two bits per I or Q sam-ple from the original eight bits, i.e., the version of nae im-plemented operates a fixed compression ratio of four.

A similar algorithm is the Block Floating Point Quan-tizer (nrrq) designed for the Sir-C mission (Curlander andMcDonough, 1991). It differs form aAe in that it uses one-dimensional blocks (e.g., 1 by 128), it is a linear quantizer,and it has a 2:1 compression ratio (eight bits to four bits persample).

WQAlgorithmThe vq algorithm (Gray, 1984) was selected because it is thesubject of much of the current research in encoding, and ithas been shown to give excellent performance on some datasets. Previous studies on the application of vq to SAR signaldata include Jones ef a/. (1988), Read ef o1. (tggga; ISSBb),and Gioutsos (1989). The decision to use unstructured vqwas reached because it was believed to be capable of betterperformance than structured versions, which generally com-promise quality for a reduction in "cost" of execution. Struc-tured versions of vQ include those such as a tree-structuredgain/shape vQ (Gray, 1984).

The algorithm parameters which can be varied for wqare vector (or block) size and codebook size. The latter isquoted in bits required to index the vectors in the codebook,i.e., codebook size is the log to the base 2 of the number ofcodebook vectors. The vector and codebook size determinethe compression ratio, because compression ratio is com-puted as bits per vector divided by codebook size in bits.The vector sizes used most frequently in the literature are inthe range from 2 by 2 pixels to 4 by 4 pixels; thus, this wasthe range of vector sizes selected for investigation in thisstudy. The codebook sizes investigated were limited to amaximum of 12 bits, i.e., 4096 codevectors.

The wQ algorithm was applied in both the image andsignal data domains. The actual vector and codebook sizesused in each domain are given in ttre section on Selection ofAlgorithm Operating Points.

|PEG DCTAlgorithmThe pnc DcT algorithm (Wallace, 1990) was selected as thesecond algorithrn to be applied in the image domain because

it is becoming a globally accepted still image compressionstandard. It is recognized that this algorithm is not optimizedfor SAR data, and compression results can be improved usingan adaptive oCt algorithm such as that of Chen and Smith(7e77).

The pnc ncr algorithm was not applied to sAR signaldata because the prc quantization tables as they stand arenot applicable to complex data. In addition, SAR signal datahave a spectrum similar to that of white noise, with almostas much energy in the high frequency components as there isin the low frequency components, and it is unlikely that theDCT algorithm would produce sufficient energy compactionto enable a useful level of data compression without appre-ciable loss of image fidelity.

The version of the pEG DCT algorithm implemented usesa block size of B by B pixels. The compression ratio is variedby changing the algorithm's "Q' factor. This factor deter-mines which quantization table to apply to the ocr coeffi-cients of the transformed blocks, and hence how severely toquantize each of the coefficients. The lower the Q factor, themore severe the quantization, and the greater the com-qression. The actual compression ratio achieved depends onthe data themselfes, with higher compression ratios being re-alized for images with a predominantly low spatial frequencycontent than for those with a predominantly high spatial fre-quency content.

Methodology of Evaluation ExperimentsIn order to evaluate the effectiveness of the various evalua-tion metrics, the following steps were taken:

o Select suitable data sets (with a diversity of image features);o Select suitable operating points, i.e., compression ratios, for

the comparison of the encoding algorithms;. Do a full comparison of the algorithms, using all the defined

metrics, at the chosen operating points.

Selection ol Data SetsThree SnASAT SAR data sets, which provided a varietv ofscene features, were selected for thdanalysis:

o Cambridge Bay, Canada (sea ice, ocean, snow, and arctic tun-&");

o Flevoland, Holland (sea, polder, forests, urban); the scene

::Trt * approximately 50 Km east of the city of Amsterdam;

. Niagara Falls, USA,/Canada border (urban, roads, river, agr!culture, airfield, orchards).

Selection ol Algorithm Operating PointsThe operating point (compression ratio) for each algorithmwas chosen such as to

o produce acceptable reconstruction results, ando allow a fair comparison between the algorithms,

BAQ and WQin Signal Data DomainAs mentioned in the section on the aaq Algorithm, the BAeparameters were set at those used on Magellan, i.e., the blocksize was 16 complex samples by 64 azimuth pulses, and thecompression ratio is fixed at four.

In order to select a suitable operating point for tlve inthe signal domain encoding, UVe was run at a variety ofcompression ratios, using vectors in the range 2 by 2 pixelsto 4 by 4 pixels, and codebooks in the range 3 to 12 bits, onthe sigual data for the selected scenes. The resulting sqr.rnwas plotted against compression ratio, and compared with

that achieved when sAQ was run on the same signal data atits fixed compression ratio of four. For all the scenes investi-gated, the SQNn produced by the tlvq algorithm was mostsimilar to the SQNR produced by naq at the compression ra-tio four. Thus, the operating compression ratio for the signaldata study was set at four, for wQ in the signal domain, inorder to allow a direct comparison with the BAQ algorithm.The operating parameters for WQ for this compression ratioare 2 by 2 vectors and 2'codevectors.

WQ and JPEG DCT in Image Data DomainTo select the operating point for the image data encodinganalysis, the uvQ and pec DCr algorithms were run on theimage data for the selected scenes with a variety of com-pression ratios; SQNR was graphed against compression ratio,and the reconstructed images obtained at each compressionratio setting were viewed. For IIVQ, the compression ratiowas varied by varying the vector sizes in the range 2 by 2 to4 by 4 pixels, and codebooks in the range 3 to 12 bits; and,for JPEG DCT, the compression ratio was varied by varying thealgorithm Q factor (refer to the section on JPEG DCT Algo-rithm). An operating compression ratio of -7 (the pointwhere the coding errors just began to be visually noticeableJwas selected. The actual operating parameters that were usedfor IJvQ were 2 by 2 vectors and 2' codevectors, giving afixed compression ratio of 6.9 for all wQ experiments. ForIPEG DcT, a quality factor setting of Q : B was required toachieve compression ratios of -7 f.or the given data. It isnoted that, as mentioned in the section on IPEG DcT Algo-rithm, setting a fixed Q factor for the IPEG DcT algorithm doesnot give rise to a fixed compression ratio. The actual com-pression ratios achieved with the Q factor setting of B were7.5 f.ot Cambridge Bay, 7.4 for Flevoland, and 5.7 for Niagara.

Signal Data Encoding ResultsThe naq and wQ algorithms were run on each of the signaldata sets, and the effects of the encoding were evaluated inboth signal and image domains. Comparable results were ob-tained for each of the selected scenes. The results for the Ni-agara data set have been selected for discussion because thisscene contained greater feature diversity than the other twoscenes. The results obtained for the Niagara data are shownin Table 1-A and Table 1-8.

The results for the analysis in the signal domain showthat BAQ produces a higher MSE [and hence a lower SQNR),and alters the standard deviation and entropy more than thewq algorithm. BAQ does, howevet, nEurow the data rangeless than UVQ and also produces a lower avetage phase errorthan WQ.

The results for the second stage ofthe signal data encod-ing analysis (i.e., evaluation in the image domain afterprocessing ofboth original and reconstructed signal dataiets) show that the image data SQNR is lower for sAQ than forwq, and BAQ has altered the image mean amplitude morethan WQ. However, for o/J the remaining statistics, BAQ hasresulted in less degradation of the properties of the final im-age than r;vQ. This implies that the lower SQNR for BAQ inthe image domain can largely be attributed to the offset inthe mean value of the image intensity. This offset reflects thechange in the standard deviation of the signal data broughtabout by the encoding process. Apart from this effect, BAQ isseen to give rise to less degradation of the final image statis-tics than wQ.

Both algorithms altered the appearance of the signal data

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TeeLE 1-A. Resuurs or Stcltel Erucoolr.lc Evelualolt tn Storulu Doulru(Nncmr)

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histograms, but not of the final image domain histogtams, aresult of the smoothing effect of the convolutional operatorused in the image formation process. Typical reconstructodsignal data histograms for naq and wQ apptied in tle signaldomain are shown in Figure 3. As Figure 3 shows, the recon-structed histogram for the BAQ algorithm has four mainpeaks. These correspond to the data being mappe-d t9_-qfr'--GBr,

*GB' ot +GB, where G is a function of the blocknuS level of a block, and the B, values are chosen to mi:ri-mize the RMs error (8, = 0.5, and B, : 7.7),The concentra-tion of the data in four main peaks implies that the nuS levelvaries very little from block to block. Thus, this evaluationshows that the eaQ algorithm could benefit from block sizessmaller than the 64- by 16-(complex) pixel blocks used inthis analysis. For wQ, the histograms, which were smoothGaussian distributions on input, appear very spiky on out-put, but still retained the same overall Gaussian outline' Theipikiness is due to the fact that the use of a limited sizecbdebook restricts the output intensity space, e.8', by limit-ing the combinations of intensities of adjacent pixels.-

The original, reconstructed, and error images for BAQand wQ are shown in Figure 4. The reconstructed images fornaq and rlvQ arc judged to be of good and equivalent qual-ity. The error images show the error to be fairly svsnly dis-tributed for BAQ. For wQ, the dominant structure of theorisinal image is evident in the error image, which impliestha-t the algoiithm is altering some features more thau others.Examination of this error image shows that it is, in essence, anegative of the original, implying that bright areas have be-coire darker, and dark areas brighter. This is attributed to the

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fact that the wQ algorithm tends to allocate fewer codevec-tors to parts of the data intensity lange where the histogramis sparse, in this case the extremes of the range, aad that, forcentroid based codebooks, ttre codevectors allocated to theseregions are an average of the outlying vector patterns. The re-sult is to effectively draw pixels towards the more heavilypopulated parts of the intensity 1e.ge. This effect is morelikely to occur in noisy (e.9., high speckle) regious than invery smooth regions where a sufficient number of similarvectors exist for the region to be accurately represented inthe codebook. The phenornenon implies that the radiometriclinearity of the coding has been disturbed. This last deduc-tion is corroborated by ttre fact that the linearity is measuredto be 0.94 for WQ as compared with 0.98 for BAe, i.e., de-spite the higher MSE value, BAe has disturbed the linearityless than t..rvQ. Preservation of linearity is important, for ex-ample, if the data are to be used for sAR sensor calibration,or if quantitative radiomebic measurements are to be takenfrom the data.

In terms of the other image domain metrics, both algo-rithms performed very well, e.g., image domain histograms,spectra, radiometric resolution, speckle, and point targetcharacteristics are well preserved (i.e., the error is too smallto be statistically significant). BAe did, however, perform bet-ter for wavelength estimation, preserving both the magnitudeand frequency of the dominant peaks in the one-dimensionalspectra. UVQ presewed the frequency but reduced the magni-tude by approximately 6 percent.

To summarize, when evaluated in both the siggal and im-age domeins, BAQ gave a higher MSE than UVe. However, BAegave better results in terms of preserving data statistics, radio-metric linearity and wavelength estimation, and equivalent re-sults for &s rcrnnining mehics. Thus, in this analysis, nAq isdeemed to have performed better overall than tlve, with thegiven operating parameters, for SAR signal data sassding.

An improvement in linearity for the vq technique couldbe obtained using the BairVshape version of vq, but for thesame compression ratio and vector size, this would be at theexpense of codebook size. Thus, one would expect the over-all use to be essentially the same, but with the errors moreevenly distributed. This last point illustrates another of theshortcomings of Mss: because it is a global measure, it givesno indication of the spatial distribution of the error, and noindication of the extent to which the enor affects the variousproperties important to SAR image analysis.

lmage Data Encoding ResultsThe wQ and prc ncr algorithms were run on each of thethree image data sets, and the effects of the encoding wereevaluated using the image domain metrics. Comparable re-sults were obtained for each of the selected scenes. The re-sults for the Niagara data set have again been selected fordiscussion, because this scene contained greater feature di-versity than the other two scenes; the results are presented inTable 2.

Table 2 shows that the sqNn is lower for we than forIPEG DcT, and, in general, WQ has distorted the data statis-tics more than the IPEG DCT algorithm. This may outwardlyappear to be due to the fact that the compression factor forthe Niagara scene using the pec algorithm with quality fac-tor 8 is 5.7, well below the fixed value of 6.9 used for ttrewq algorithm. However, very similar results were obtainedfor the remaining two scenes, despite the fact that at thesame quality factor setting, the compression ratios achieved

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IPEG DCT uVQ

CR at operating pointMSEASQNR (dB)PSQNR (dB)A data rangeA mean intensityA aA kurtosisA entropyhistogramsradiometric resolutionspeckleradiomotric linearitymis-registrationpoint targetswavelength estimation

for Cambridge Bay and Flevoland were 7.5 and 7.4, respec-tively, i.e., above that of the wQ algorithm (see the sectionon Methodology of Evaluation Experiments). Thus, the factthat t-rvQ introduces more distortion into the data than thepEG algorithm is deemed to be attributed more to the man-ner in which the two elgorithms operate than to ttre actualcompression ratios achieved. wQ introduces quantization er-rors in the spatial domain, whereas 1PEG introduces errors inthe transform domain. In both cases the errors are expectedto be greater in "busy" parts of the image, i.e., those withpredominantly high frequency components where pixel val-ues change rapidly. For wQ, it is more difficult to produce afinite set of representative vectors for a busy area, whereasJPEG quantizes high frequency components more severelythan low frequency components. However, the effect of thetransform stage of the prc algorithm is to distribute the er-rors across the block, which has the effect of reducing themagnitude of the individual pixel euors. trvQ has no equiva-lent process, and severe individual pixel errors are morelikely to occur, contributing to the lower SQNR and greaterstatistical distortion recorded here for this algorithm.

The original, reconstructed, and error images for IPEGDCT and WQ are shown in Figure 5. The reconstructed im-ages for both Jenc DCt and r.rvQ are judged to be of goodqualrty, though both of the reconstructed images haveslightly degraded resolution compared with the original, dueto attenuation of the high frequency components, or of rareevents. The wQ reconstructed image also shows some lossof contrast, due to the codebook having a more limited inten-sity space than the original data. The error images show theerror to be fairly evenly distributed for both algorithms, withthe wq error image having slightly more structure than theIPEG DCT error image.

Typical reconstructed histograms for JPEG DcT and wQapplied in the image domain are shown in Figure 6. The his-togram characteristics were well preserved by the IPEG DCTalgorithm. For the wq algorithm, although the overall shapeof the histogram was preserved, the histogram was muchmore "spiky" in appearance. Again, this is due to the dis-crete nature of the codebook, which limits both the intensityspace and the intensity combinations of adjacent pixels.

Typical error spectra for IPEc Dcr and t.rvQ applied inthe image domain are shown in Figure 7. For both algo-rithms, the reconstructed spectra appeared very similar to

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the original spectra, with all dominant peaks reproduced,and no extra peaks introduced. The error spectra containedno dominant peaks, i.e., they did not show evidence of auyperiodic error effects having been introduced. The errorspectra did however highlight the fact that the error for IPEGDCT is much greater in the high frequency portion of thespectrum than in the low frequency portion. For IJvQ, the er-ror was much more uniformly distributed with frequency.

Both algorithms preserved the radiometric resolution,and both reduced the speckle component by approximately 5percent. The radiometric linearity was 1.0 for prc Dcr; how-ever, IJVQ was again shown to disturb the linearity, with theslope of the linearity plot being 0.96.

Neither algorithm produced any measurable mis-registra-tion effects, either globally or locally. For almost all thepoint targets studied, IPEG DcT was found to produce onlyvery slight differences in peak widths, energy, and position.LIVQ, however, was found to be more likely to alter these

902

characteristics. This is mainly due to the fact that point tar-gets are essentially "rare events" of dimension not muchgteater than the vectors themselves. A centroid based code-book is not geared to meet the needs of reproducing the indi-vidual characteristics of each point target. Point targetpreservation is important, as it is often used in SAn imageanalysis, e.g., in the data calibration stages.

In terms of wavelength estimation, IPEG DCT preservedboth the magnitude and frequency of the dominant peaks inthe one-dimensional range and azimuth specha. tIvQ pre-served the frequency, but reduced the magnitude by up to 3percent. It is, however, recognized that the peaks studied oc-curred in the low frequency portion of the spectrum. Forhigher frequency peaks it is likely that pEG nCt would alsoalter the magnitude, because ttre evaluation of the spectrashowed that IPEG attenuates high hequency componentsmore than low frequency components (an inherent propertyof the pnc quantization scheme).

To summarize, in this analysis the pEG DCr algorithm per-

Figure 6. Typical image data encoding histograms. (a) Orig-inal histogram. (b) Reconstructed histogram using JPEGDCT. (c) Reconstructed histogram using UVQ.

Figure 7. Typical image data encoding one-dimensional er-ror spectra. (a) One-dimensiona[ error spectrum for JPEGDCT. (b) One-dimensional error spectrum for UVQ.

formed consistently better than the wQ algorithm at similarcompression ratios. The use of the additional metrics specifiedin this paper served to highlight the way in which the highererror inboduced by the wQ algorithm manifested itself.

The evaluation metrics also serve to highlight areas inwhich the algorithm could benefit from improvements and,thus, help the user to decide on alternative techniques worthinvestigating. For example, the linearity of vq could be im-proved using gain/shape vQ, and it may be possible to repro-duce the point target characteristics more accurately usingsome form of classified vq as opposed to designing a purelycentroid based codebook.

ConclusionsIn this paper we have suggested a number of evaluation cri-teria especially suited 1e dslslrnining the manner and extentto which an encoding algorithm distorts SAR data. Both sig-nal and image domain encoding were studied, with two algo-rithms having been applied in each domain. The results

PE&RS

obtained using the given algorithms were described and ana-lyzed using the given metrics.

The measurement of MSE or SQNR is important, as itgives a measure of the severity of the unstructured errorErought about by encoding; thus, Msn should always be in-cluded as an evaluation metric. Howevet, in order to gainadditional insight into the characteristics of tle error, andalso into the m-anner of operation of the encoding algorithm,additioual mehics are needed.

The metrics which produced the most insight into themenne in which the algorithms acted on ttre data and theresulting degradations were felt to be

o the data statistics,o the original versus reconstructed data histograms,a enor images,. error spectra,o radiometric linearity, ando point target analysis.

The measurement of data statistics was useful, for exam-ple, in showing that the higher MSE obtarled for BAQ com-pared to UvQ, in the signal domain encoding experiments,Could largely be attributed to the greater change itt tlaldarddeviation-ofthe signal data, which gave rise to a larger globaloffset in the mean intensity of the reconstructed image data,as opposed to indicating a degradatiou of image features.-fhe

data histograms were useful in both the signal andimage domain analysis in showing how the algoritbms actedon tle data. For example, for BAQ they showed how little theblock nvs value varied for the chosen block size. This in-sight would be useful when selecting a block size. For wqthe histograms showed the effects of mapping data onto asmaller intensity space and limiting the allowed intensitycombinations of adjacent pixels.

The error images, in conjunction with other metrics suchas the data histograms, showed which properties of the origi-nal images were giving rise to the highest errols, e.g', for WQit was found tlaf regions with an iutensity haviag a lowprobability of occurrence, in this case, those on the edges ofthe input intensity space-predominantly bright or darkareas-produced the largest errors.

The spectra highlighted which frequency components-were most affected by the coding. The measurement of radio-metric linearity consolidated the results seen in the error im-age, and showed the severity of this error. The p,oint-targetanalysis showed how well rare events are reproduced by thegiven algorithm.

AcknowledgmentsThis work was performed under ESA Contract No. 9122190/NL/PR(SC). The authors would l&e to thank the technicalauthority, fean-Luc Marchand of ESTEC, for his advice anddirection during the course of this study'

ReferencesChang, C.Y., R. Kwok, and J.C. Curlander, 1988' Spatial Compression

of Seasat SAR Imagery, IEEE Trons. on Geoscience ond fiemoteSensing, 26(5):673-685.

Chen, W.H., and H. Smitb, 1987. Adaptive Coding of Monochromeand Color Images, IEEE Trans. on Communicattbns, COM-25(r7J:7285-L292.

Curlander, ].C., and R.N. McDonough, 1s91' Synthetic Aperlure Ra-dar-systems and Signal Processing, |ohn Wiley and Sons Inc'

Frost, V.S., and G.J. Minden, 1986' A Data Compression Technique

for Synthetic Aperture Radar Images, IEEE Tians. on Aero. andEle ct. Syst., AES-22(7) :47 -5 4.

Gioutsos, T,, 1989. Vector Quantization Used To Reduce SAR DataRates, SPIE Mrllmeter Wave and Synthetic Aperture Radar,1101:116-128.

Gray, R.M., 1984. Vector Quantization, IEEE ASSP Magazine, l:4-29.

|ones, R.V., V.D. Vaugb-n, R.L. Frost, and D.M. Chabries, 1988. TheEffect of Codebook Size on the Vector Quantization of SAR Data,IEEE 1988 National Radar Conference, pp. 129-133.

fordan, R.L., 1980. The SEASAT-A Synthetic Aperture Radar Sys-lem, IEEE loumoJ of Oceanic Engineering, OE-5(2):154-164.

Kwok, R., and W.T.K. Johnson, 1989. Block Adaptive Quantisationof Magellan SAR Data, IEEE Trans. on Geoscience and RemateSensing, 27 (a)37 5-383.

Press, W.H., B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,1988. Numerical Recipes lh C, Cambridge University Press.

Read, C.J., D.V. Arnold, D.M. Chabries, and R.W. Christiansen,1988a. A Computation Compression Technique for SAR basedon Vector Quantization, IEEE 1988 National Radar Conference,pp.723-728.

Read, C.J., D.V. Arnold, D.M. Chabries, P.L. Jackson, and R.W Chris-tiansen, 1988b. Synthetic Aperture Radar Image Formation FromCompressed Data Using A New Computation Technique, IEEE'{ES Magazine, pp. 3-10.

Wallace, G.K., 1990. Overview of the ]PEG Still lmage CompressionStandard, SPIE Image Prccessing Algorithms and Techniques,7244:22O-233.

(Received 2 September 1992; revised and accepted 3 March 1993)Melanie DutkiewiczMelanie Dutkiewicz received the B.Sc. degree inPhysics and the B.Sc. degree in Electronic Engi-neering from the University of Cape Town in1983 and 1985, respectively. She received the

,fft pffi.A;t.r in Electronic Engineering fromCambridge University in 1990. Since then she has been em-ployed by MacDonald Dettwiler, Canada, where she has pri-marily been involved in projects concerning syntheticaperture radar data analysis and compression.

Ian CummingIan Cumming received his B.Sc. in EngineeringPhysics at the University of Toronto in 1961,and a Ph.D. in Computing from Imperial Col-

Ili#:ifl- lege, University of London, in 1968. Followingwork in steel mill automation and sonar signal processing,he joined MacDonald Dettwiler and Associates in 1977.Since that time, he has developed synthetic aperture radarsigrral processing algorithms, and worked on systems forprocessing polarimetric and interferometric radar data, andfor the compression of radar data. In 1993, Dr. Currmingjoined the Dept. of Electrical Engineering at the University ofBritish Columbia, where he is the chairholder of the Mac-Donald DettwilerA.{SERC Industrial Research Chair in RadarRemote Sensing.

Proceedings: International Symposium on Spatial Accuracy ofNatural Resource Data BasesUnlocking the Puzzle

16-20 May 1994, Williamsburg, VirginiaRussell G. Congalton, Editor

1994. 280 pp, 565 (soficover); ASPRS Members $40. Stock # 4536.

The International Symposium on Spatial Accuracy of Natural Resource Data Bases was organized to bring together a group of individualswith common interest in the spatial accuracy of natural resource data bases so that the latest information could be exchanged, and todevelop communication pathways that will hopefully long outlive the meeting.

The workshop was sponsored by the International Union of Forestry Research Organizations Forest Inventory and MonitoringSubject Group (54.02) and the American Society for Photogrammetry and Remote Sensing (ASPRS). The workshop was also endorsedby the Society of American Foresters, GIS Working Group.

Topics Include:

- Importance of Accuracy I - Remote Sensing I - Dealing With Accuracy II- Imporiance of Accuracy II - Terrain DEM's - Example Applications III- Accuracy of Basic Data I - Dealing With Accuracy I ' Example Applications IV- Accuracy of Basic Data II - Dealing With Accuracy II - Posters- Example Applications I - Remote Sensing II

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