Low-Pass Spatial Filtering of Satellite RadarDataPaul W. Mueller' and Roger N. HoffertDepartment of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907

ABSTRACT: Thirty-four low-pass spatial filter treatments were applied to a multi-angle SIR-B data set to reduce speckleeffects and improve classification performance. These treatments were based on four algorithms: square mean, separablemean, square median, and separable recursive median. The filtered images were evaluated using both quantitative andqualitative techniques. lt was determined that the square median algorithm implemented at two iterations with awindow size of 3 by 3 produced the best overall results with the 28.5-m SIR-B data.

INTRODUCTION

I N THE PAST FEW YEARS, interest in assessing and mappingforest resources with synthetic aperture radar (SAR) data hasbeen steadily increasing. Many of the digital analysis techniquesand procedures developed and tested for use with optical (mul-tispectral scanner) data do not work as well with SAR data, dueto the distinctly different inherent characteristics of SAR data,such as speckle. Radar speckle increases the variance withinSAR imagery, which adversely affects the ability to discriminatedifferent cover types when using various pattern recognitiontechinques.

Many techniques to suppress radar speckle have been pro-posed. In general, these fall into one of two broad categories:pre-image and post-image SAR correlation processing. Tech-niques in the first category improve SAR image appearance byaveraging several frames for the same area. This multiple-lookapproach is achieved by not using the entire synthesized an-tenna length, but by breaking up the synthesized length into anumber of subsections and looking at the scene from slightlydifferent aspects (Skolnik, 1980).

Post-image speckle reduction techniques include spatial fil-ters. Whereas pre-image multi-look processing smooths only inthe azimuth direction, spatial filters can be chosen to filter inboth the azimuth and range directions. The most common spa-tial filtering operations are the calculation of mean and medianvalues for a moving window. To minimize the speckle effectsin SAR data prior to computer classification (using standard pat-tern recognition techniques), several earlier studies have usedboth mean and median filters (Brisco et aI., 1983; Knowlton andHoffer, 1983; Sader, 1987; Wu, 1984).

In addition to mean and median filters, more sophisticated-patial filtering techniques also have been developed (Crim-mins, 1985, 1986; Lee, 1981a, 1981b, 1983a, 1983b, 1986; Qianand Haralick, 1985). However, there has been little previouswork involving detailed qualitative and quantitative compari-sons of various filters and filter parameters for the purpose ofdetermining the most effective filter treatment to aid in im-proving computer-aided classification results. A comparison ofseveral filters was conducted by Durand et al. (1987), but it wasfelt that their results were not directly applicable to this studybecause the data utilized were aircraft X- and C-band SAR im-agery obtained over agricultural landcover. The current studyconcentrated on evaluation of mean and median filters for re-ducing speckle in L-band satellite SAR data obtained over for-ested terrain. Such filters are readily available and relatively

'Presently with Autometric Inc., Alexandria, VA 22312.'Presently Professor of Forestry and Remote Sensing, Colorado State

University, Fort Collins, CO 80523.

PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING,Vol. 55, No.6, June 1989, pp. 887-895.

easy to implement. In order to evaluate the various filters bothquantitatively and qualitatively, it was found that some newapproaches, which are discussed in this paper, were needed.

OBJECTIVE

The objective of this research was to identify an effective low-pass spatial filter treatment(s) for reducing speckle effects inShuttle Imaging Radar (SIR-B) digital data that were to be uti-lized for assessing forest resources. To meet this objective, acomparison was made of the effectiveness of four spatial filter-ing algorithms: square mean, separable mean, square median,and separable recursive median using quantitative and quali-tative evaluation methods. In addition to comparing algorithms,the most appropriate number of iterations and window sizewere determined.

STUDY SITE

The data utilized for this study cover an area of approximately380 square kilometres, primarily in Baker County in northeast-ern Florida (Figure 1). The area is forested with the major forestcover types being pine palmetto flatwoods, cypress swamps,creek swamps, and mixed bay swamps (Avers and Bracy, 1974).The pine flatwoods are composed of slash pine (Pinus elliottiz)and/or longleaf pine (P. palustris). The swamps largely consistof such deciduous species as pondcypress (Taxodium distichumvar. nutans), baldcypress (T. distichum), tupelo (Nyssa spp.),sweetbay (Magnolia virginiana), red maple (Acer rubrum), andsweetgum (Liquidambar styraciflua), often with some scatteredslash pine and/or pond pine (P. serotina).

FIG. 1. Location in Florida of the SIR-B dataset utilized in this filtering experiment.

0099-1112/89/5506-887$02.25/0©1989 American Society for Photogrammetry

and Remote Sensing

888 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1989

MATERIALS AND METHODS

IMAGE DATA

The primary data analyzed were a multi-angle SAR data setobtained by the Shuttle Imaging Radar-B (SIR-B) during SpaceShuttle Flight 41-G in October 1984. The data were collected on9,10, and 11 October, each day at a different angle of incidence(Table 1). Additional characteristics of these data are given byMueller et al. (1985) and Hoffer et ai. (1985). The data weredigitally processed by the Jet Propulsion Laboratory (JPL) in Pas-adena, California. A description of the SIR-B Digital ProcessingSubsystem utilized at JPL is given by Curlander (1986). For thesake of uniformity, all SIR-B data processed by JPL was digitallycorrelated to 12.5-m by 12.5-m pixels. The three different datasets corresponding to the different angles of incidence weredigitally registered.

The research described in this paper was part of a larger studywhIch investigated the utility of multi-angle SAR data for iden-tifying forest cover when used singly and in combination withLandsat Thematic Mapper (TM) data (see Hoffer, 1984). There-fore, the registration of the SIR-B data to a TM data set was highlydeSIrable. One day after the acquisition of the 28° incidenceangle SIR-B data (i.e., 12 October 1984), a cloud-free Landsat TMscene was acquired. The original registered 12.5-m by 12.5-mSIR-B data were resampled to a ground resolution of 28.5 m by28.5 m and registered to the TM data by JPL. This process re-duced some of the speckle characteristics present in the originaldata, but there was still a large amount of speckle (i.e., spatialvanablilty) present In the final (28.5-m pixels) data set used inthe present study. A 770 line by 610 column portion of the SIR-B scene was selected for filtering. Reference data utilized forinterpretation of the SIR-B data included aerial and ground pho-tography, vegetation and soil samples, field notes, and forestinventory information and maps provided by timberland own-ers. The registered TM data were also considered to be an im-portant reference data set.

SPECKLE

Radar speckle is characterized as random variability in imagetone among pixels corresponding to different pixels of a uniformtarget. It is caused by the instantaneous radar return randomlyfluctuating (or fading) as the radar beam passes over an extendedtarget (Fung and Ulaby, 1983; Ulaby et aI., 1982). Target featuresnormally consist of a large number of randomly distributedscatterers, and it is this randomness that is responsible for image

speckle OPL, 1986). Skolnik (1980) states that constructive anddestructive interference result in a breakup of distributedscatterers, causing the speckled appearance of radar imagery.Thus, true speckle IS caused by the coherent imaging processof SAR systems and is not a result of spatial variability in thephysICal or electromagnetic properties of the target feature.However, there may be a random variation in the radarbackscatter that is caused by the physical characteristics of thetarget (e.g., rough textured forest canopies) and which also hasth~ appearance of t~ue radar speckle on the image. SpecklenOIse IS usually consIdered to be multiplicative in nature (Lee,1986), meaning that, as the amplitude of the return increases,the noise also increases. Properties of speckle are discussed indetail by Goodman (1976) and Porcello et al. (1976). Becausespeckle noise increases variance and inhibits interpretation anddigital analysis of SAR data, the removal of speckle effects fromSAR data is highly desirable.

FILTERING

Low-pass spatial filtering was selected as the method forreducing variance in the SIR-B imagery. The most commonarithmetic operations utilized for smoothing of SAR data aremean and median procedures. In both cases, the center pixelof the active window is replaced by the calculated value (i.e.,mean or median). For both of these operations, a separable anda non-separable algorithm were chosen for use with the SIR-Bdata. A separable filter utilizes successive applications of a one-dImensional window in separate filter passes of the image, firstalong the rows and then along the columns, or vice-versa. Non-separable filters utilize a two-dimensional window and the imageis filtered in one pass.

The four algorithms thus defined for this study were squaremean (non-separable), separable mean, square median (non-separable), and separable recursive median. Therefore, both one-dimensional (s~parable) and two-dimensional (non-separable)WIndows were Included in the selected treatments. By varyingthe WIndow dimensions', a variety of treatments were availablefor i~plement~tion and evaluation. The last algorithm isrecurSIve, meanIng that, as the filter window is moved acrossthe image and output values are calculated, these values replace

'For purposes of discussion, a separable 1 by n window and a non-separable /1 by n window are considered to have a window size n. Thisis not inappropriate because the two passes (rows and columns sepa-rately) of a separable 1 by n filter simulate an n by n non-separablewmdow.

TABLE 1. CHARACTERISTICS OF THE DIGITALLY CORRELATED SIR-B DATA ACQUIRED OVER THE FLORIDA TEST SITE DURING SPACE SHUTTLE FLIGHT

41-G. CHARACTERISTICS LISTED PERTAIN TO THE ENTIRE SIR-B SCENE OBTAINED FOR THE AREA.

Parameter Data Set CharacteristicCenter incidence angle 58.4° 45.3° 28.4°Acquisition data 9 October 1984 10 October 1984 11 October 1984Center acquisition time: GMT 0934 0917 0900

EDT 0534 0517 0500Data take scene number AK-064.2-003 AK-080.2-003 AK-96.2-003Orbital track

(Azimuth from true north) 45.0° 45.0° 45.6°Platform altitude 229.45 km 230.12 km 225.67 kmSlant range to near edge 409.81 km 311.37 km 249.46 kmCenter resolution

(ground range x azimuth) 16.5 m x 31.5 m 19.8 m x 34.1 m 29.6 m x 25.5 mNumber of looks 4 4 4Correlated pixel size 12.5 m x 12.5 m 12.5 m x 12.5 m 12.5 m x 12.5 mResampled pixel size 28.5 m x 28.5 m 28.5 m x 28.5 m 28.5 m x 28.5 mQuantization levels 256 256 256Wavelength 23.5 em 23.5 em 23.5 emPolarization HH HH HH

LOW-PASS SPATIAL FILTERING OF SATELLITE RADAR DATA 889

CONSTANT

Cover Type B

TRANSITION

ZONE

Cover Type A

CONSTANTNEIGHBORHOOD NEIGHBORHOOD

f Cover 1Edgel

If Cover 1T~pe Boundary T~peA B""I'"9 9 9 7 6 5 3 1 1 1

9 9 9 8 7 5 4 1 1 1

9 9 9 7 6 6 4 1 1 I

MeenDigitel

NumberVelue

Column Meens 9 9 9 7 6 5 4 1

P

L

oT

FIG. 2. Example of horizontal transect across theboundary between cover type A and cover type B.Column means are first calculated for the transectdigital numbers. These means are then utilized togenerate transect plots.

length was adjusted so that each end of the transect includedat least nine pixels located completely within the cover-type umton that side of the boundary. Across the width of each transect,the average of the three DNs was calculated. Plots were madeof the average ON values along the length of each transect (Figure2). For each of the five transects, thirty-five plots were made(corresponding to the filtered and unfiltered data sets listed inTable 2). For each of the treatment plots, a grade was assigned,based on a set of rules used to judge the shape of the curverepresenting the transition between the two cover-type units.

The first step of the grading procedure was to determine theshape of the curve. When the boundary/edge was retained, adistinct level or plateau was found at both ends of the transectplot. For purposes of evaluation, a plateau was defined as atleast three points (i.e., three column averages) with nearly equalvalue (± 2 digital numbers) found on either side of an edge. Ifa distinct plateau was discernible at each end of the transect,the shape was determined to be a "step." If only one plateauor no plateau was discernible, it was caBed a "ramp," and ascore of zero was assigned to that transect. For those plots judgedas having a step shape, the number of transition points betweenthe two plateaus was counted. A higher score was then assignedto those step transitions having the least number of transitionpoints and a low score assigned to the shaBow sloping transitions.If there was an enhancement in the boundary (e.g., caused bycorner reflector effects) and this enhancement was retained, thescore was increased by one for that treatment. To be consideredan enhancement, there had to be an increase of at least 10 digitalnumbers as compared to the adjacent cover-type unit. For eachfilter treatment, the scores from the five transects were addedto yield an aggregate score. The filter treatments were then

TABLE 2. FILTER TREATMENTS ApPLIED TO THE SIR-B DATA

AlgorithmWindow Square Separable Square Separable

Dimension Mean Mean Median Recursive Median

3 3SQMNl 3SMNl 3SQMDl 3SRMDl3SQMN2 3SMN2 3SQMD2 3SRMD23SQMN3 3SMN3 3SQMD3 3SRMD3

3SQMD45 5SQMNl 5SMNl 5SRMDl

5SQMN2 5SMN2 5SRMD25SQMN3 5SMN3 5SRMD3

7 7SQMNl 7SMNl 7SRMDl7SQMN2 7SMN2 7SRMD27SQMN3 7SMN3 7SRMD3

9 9SRMDl9SRMD29SRMD3

m = window dimensionAAAA = filter algorithm

SQMN - Square meanSMN - Separable mean

SQMD - Square medianSRMD - Separable recursive median

n = iterations

EVALUATION

Evaluation of the filtering treatments was based on bothquantitative and qualitative assessment techniques, includingan EdgelBoundary Retention Study, Cover-Type ClassificationStudy, and a Visual Assessment Study. Before conducting theevaluation studies, representative blocks of data were selectedfor a variety of cover types. A total of 78 areas of known covertype were selected and their pixel coordinates were recordedfor use in the EdgelBoundary Retention Study and the CoverType Classification Study.

Edge/Boundary Retention Study. The purpose of this study wasto provide a measure of the edge/boundary retention propertiesfor each of the filter treatments. Low-pass filters are also knownas smoothing filters; smoothing in the sense of reducing thevariance in the data was desired, but the blurring of edges andboundaries was not wanted. This evaluation test was based ondigital number (DN) plots for transects across boundaries betweencover type units - similar to those used by Cushnie and Atkinson(1985) with TM data. Boundaries were selected such that theywould divide cover-type units that had distinctly differentappearances on the imagery. Only the 28° incidence angle imagewas used for this study, because its speckle characteristics wererepresentative of all three incidence angle data sets.

A variety of orientations within the SIR-B data set were selectedto reduce the directional biases, if any, of the filters: two vertical(along columns), one diagonal (approximately 45°), and twohorizontal (along lines/rows) transects were selected. Transectswere oriented perpendicularly to the boundary. The transectswere three pixels in width and at least ten pixels in length. The

the corresponding values in the original input image for purposesof calculating future filter outputs. With the other threealgorithms, only the original pixel values are considered in eachoperation. Therefore, three variables of interest were definedfor this study: the filter algorithm, window size, and numberof iterations. This resulted in a total of 34 filter treatments thatwere applied to the SIR-B data. These are listed in Table 2.

mAAAAn = filter treatment codewhere

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1989890

ranked by their aggregate scores. A higher score was the mostdesirable as this indicated that, on average, the associated filtertreatment had the shortest transition zone between the twocover type plateaus.

Cover-Type Classification Study. The 78 fields selected previouslywere divided into training and test fields for computerclassification purposes. Twenty-seven fields, considered to berepresentative of the cover types present and providingrepresentation of classes proportional to their presence in thescene, were utilized for training purposes. These training fieldsand their cover-type deSCriptions are listed in Table 3. For trainingthe classifier, training field statistics were not combined by covertype. Each field was treated as a separate class during theclassification process. However, the fields were later groupedby cover class for evaluation purposes. The remaining 51 fieldswere utilized as test fields (totaling 2881 pixels). These fieldswere classified utilizing a per-point Gaussian MaximumLikelihood classifier applied to the three-incidence-angle SIR-Bdata set. Thus, 35 separate three-incidence-angle classificationswere conducted, one for each of the filter treatments beingevaluated.

For evaluation purposes, both the training and test fields weregrouped into five cover classes as indicated in Table 3. The fivegroups were selected after viewing plots of the training fieldstatistics, and five "natural" groupings were found. t The overallclassification performances, given as the percent of all pixelscorrectly classified (PCCo), of the test areas were then comparedto determine differences produced when using filtered versusunfiltered SIR-B data. The statistical significance of thesedifferences based on PCCos were then tested using the Studentized

TABLE 3. TRAINING FIELDS USED FOR THE COVER-TYPE CLASSIFICATIONSTUDY, THEIR COVER TYPE, FIELD SIZE, AND COVER CLASS GROUP.

Newman-Keuls multiple range test with an alpha of 0.05. Priorto this calculation, the pccos were transformed with an arc sinetransformation to convert the binomially distributed proportions(PCCo) into a normal distribution (Steel and Torrie, 1980). Thestatistical analysis was conducted on a stratified basis to evaluatethe effects of algorithm, window size, and the number ofiterations.

Visual Assessment Study. This study was based on the visualanalysis of photographic prints of the various filter treatments.A 256 by 256-pixel block subimage was utilized. Photos wereobtained using both black-and-white and color film types (35mm). The visual analysis procedure can be broken down intoseveral steps as outlined in Figure 3. Thus, a qualitative selectionof the best treatments was made, based on visual assessmentof these photos of the unclassified images.

For the purposes of providing a visual check of the tabularclassification results, a visual assessment of the classified imagerywas also conducted. For each of the subgroup filter treatmentsselected while analyzing the unclassified images, the subimage(256- by 256-block) was classified using the training statisticsdeveloped in the Cover-Type Classification Study. Color photoswere taken of these classified images displayed as five cover-type groups. These photos were used to interpret and verifythe results obtained in the Cover-Type Classification Study andto assess the effects of filtering on classification for a larger area(rather than only test fields).

RESULTS AND DISCUSSION

The results from the three evaluation studies (i.e., Edge/Boundary Retention, Cover-Type Classification, and Visual As-sessment Studies) were compared. Besides the selection of thebest filter treatment, information on trends related to iterations,

Cover TypeForest clearcutForest clearcutSlash pine plantation, age 2 yearsSlash pine plantation, age 3 yearsSlash pine plantation, age 3 yearsSlash pine plantation, age 5 yearsSlash pine plantation, age 9 yearsSlash pine plantation, age 17 yearsSlash pine plantation, age 22 yearsSlash pine plantation, age 24 yearsSlash pine plantation, age 26 yearsSlash pine plantation, age 30 yearsSlash pine (natural origin), age 39 yearsSlash pine (natural origin), age 45 yearsLongleaf pine (natural origin), age 62 yearsLongleaf pine (natural origin), age 77 yearsSlash pine - cypress swampTupelo - cypress swampBlackgum - sweet bay - maple swamp,

age 62 yearsSlash pine - hardwood swampSlash pine - cypress swampCypress swampBare soilBare soilPastureWater

Total

Numberof

Pixels2081637228

108816098

10810881328466

110992481

906690708181

1001982

CoverClassGroup

PINElPINElPINElPINElPINElPINElPINE2PINE2PINE2PINE2PINE2PINE2PINE2PINE2PINE2PINE2SWAMP1SWAMP1SWAMP1

SWAMP2SWAMP2SWAMP2SMOOTHSMOOTHSMOOTHSMOOTH

Analy ze black and white photos

of all 28 degru filter treatments

ISelect subgroup of filter treatments

II I

Analy ze 45 deg Analyze color

and 58 deg black compos;te

and white photos (28,45, 58 deg)

of subgroup of subgroup

treatments treatments

I I~

Select best filter treatment(s)

tThe plots consisted of the digital number mean from one incidenceangle plotted against the mean of another incidence angle for eachtraining field.

FIG. 3. Flowchart of the Visual Assessment Study pro-cedure. The term "deg" refers to degrees of incidenceangle.

LOW-PASS SPATIAL FILTERING OF SATELLITE RADAR DATA 891

window size, and algorithm were obtained through analysis ofthe data obtained from the various evaluation studies.

ITERATIONS

With all algorithms, the second iteration provided the bestresults. It was found that the separable mean, square mean,and separable recursive median algorithms implemented witha single iteration produced visually inferior images to thoseobtained after two iterations. A distinct gridding effect wasproduced by a single iteration of the first two algorithms (Figure4), while a single iteration of the separable recursive medianalgorithm produced streaking* (Figure 5). The second iterationalleviated these gridding and streaking effects. The value of thesecond iteration was also confirmed quantitatively. For the threenon-recursive filter algorithms (separable mean, square median,and square mean) implemented at window sizes of three andfive, the second iteration showed a significant" improvementin the classification results (see Table 4).

A third iteration was not found to be beneficial. The third

FIG. 4. Example of gridding produced by the mean filters. Gridding pro-duced by the first iteration (top) of the separable mean algorithm wasgreatly reduced by the second iteration (bottom). These highly magnifiedimages represent the 7SMN1 (top) and 7SMN2 (bottom) treatments.

*The separable recursive median algorithm filtered the columns androws separately, with the rows being filtered last. This caused the elon-gation of features (Le., streaking) along the rows for the first iteration.

HThe term significant, as used herein, refers to statistical significancetested at alpha of 0.05 using the Studentized Newman-Keuls multiplerange test.

FIG. 5. Example of the streaking effect produced by the separable recur-sive median filter. The streaking produced by the first iteration (top) wasreduced by the second iteration (bottom). These highly magnified imagesrepresent the 7SRMD1 (top) and 7SRMD2 (bottom) treatments.

iteration of the separable recursive median showed littleimprovement in visual appearance of the SIR-B imagery ascompared to the second iteration. This was confirmed statisticallywhen no significant differences in classification accuracy werefound (see Table 4). This indicates that a near root image wasachieved in two iterations. Even though Table 4 indicates anapparent improvement in classification performance for the thirditeration of some algorithms, visual inspection of the classifiedand unclassified imagery showed that the second iteration wassuperior to the third because many smaller features were lostwhen more than two iterations were used.

WINDOW SIZE

Visually, the window size of three was selected as providingthe best results for all algorithms applied to the SIR-B data withtwo iterations. The window size of five produced significantlybetter classification results than the window size of three (seeTable 5). However, visual inspection of the classified imagesand the classified test fields showed that the window size ofthree was more appropriate - far more features were retainedand there was much less blurring of boundaries. Figure 6illustrates that the window size of three was more appropriatefor the square mean algorithm, as was the case with the otheralgorithms.

892 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1989

TABLE 4. STATISTICAL EVALUATION OF OVERALL CLASSIFICATION

PERFORMANCES SHOWING THE EFFECT OF NUMBER OF ITERATIONS FOR

EACH ALGORITHM AND WINDOW SIZE COMBINATION. THE PCCoS ARE

BASED ON 51 TEST FIELDS (2881 POINTS) AND FIVE COVER-TYPEGROUPS. STATISTICAL DIFFERENCES WERE CALCULATED USING THE

STUDENTIZED NEWMAN-KEULS MULTIPLE RANGE TEST (ALPHA = 0.05).NON-SIGNIFICANT DIFFERENCES ARE UNDERLINED.

TABLE 5. STATISTICAL EVALUATION OF OVERALL CLASSIFICATIONPERFORMANCES SHOWING THE EFFECT OF WINDOW SIZE FOR EACH

ALGORITHM IMPLEMENTED AT Two ITERATIONS. THE PCCoS ARE BASED

ON 51 TEST FIELDS (2881 POINTS) AND FIVE COVER-TYPE GROUPS.STATISTICAL DIFFERENCES WERE CALCULATED USING THE STUDENTIZED

NEWMAN-KEULS MULTIPLE RANGE TEST (ALPHA = 0.05). NON-SIGNIFICANT DIFFERENCES ARE UNDERLINED.

ALGORITHM

The EdgelBoundary Retention Study demonstrated that theseparable recursive median algorithm most effectively preservededges and boundaries (Table 6). The square median algorithmdid preserve edges also, though not as well. The two meanalgorithms blurred the edges and turned the transitions intoramps. These results showing the effectiveness of median filtersfor preserving edges are in general agreement with previouswork (e.g., Gonzalez, 1986; Heygster, 1982).

FIG. 6. Classified images for the 3SQMN2 (top) and 5SQMN2 (bottom) filtertreatments. The five cover-type groups (defined in Table 3) are depictedas follows in order of increasing brightness: SMOOTH, PINE1, PINE2, SWAMP1,and SWAMP2. More cover-type features were preserved with the windowsize of three, while small swamps and other features were lost with thelarger window size. (Scale 1 :80,000)

95.77SMN2

95.45SRMD2

92.8

7SQMN2

95.15SMN2

94.93SRMD2

85.7

5SQMN2

3SQMN1 3SQMN2 3SQMN387.5 90.7 93.4

5SQMN1 5SQMN2 5SQMN393.6 95.1 95.6

7SQMN3 7SQMN1 7SQMN289.2 94.9 95.7

3SMN1 3SMN2 3SMN387.6 90.7 93.3

5SMN1 5SMN3 5SMN2

93.0 93.3 94.9

7SMN3 7SMN1 7SMN292.1 94.5 95.4

3SQMD1 3SQMD2 3SQMD3 3SQMD4

85.1 88.2 89.7 90.43SRMDl 3SRMD3 3SRMD2

85.2 85.6 85.75SRMDl 5SRMD2 5SRMD3

91.0 92.8 92.87SRMD2 7SRMD3 7SRMD1

83.9 83.9 84.09SRMD1 9SRMD3 9SRMD2

79.2 81.0 81.1

3SQMN2

90.73SMN2

90.77SRMD2

83.9

COMBINED ASSESSMENT OF RESULTS OBTAINED

When the results from the EdgelBoundary Retention, theCover-Type Classification, and the Visual Assessment Studieswere compared, they did not all point to the same filtertreatments. The merits and weaknesses of each test had to beconsidered and a compromise made as to which treatmentsshould be selected. As is indicated in Table 5, window sizes of

five and seven provided higher classification accuracies than awindow size of three. However, visual evaluation of the classifiedimagery clearly showed that window sizes of five and sevenresulted in the loss of many linear or small features (e.g., roads,small forest stands, fields, etc.). Thus, based on the results ofthe Visual Assessment Study, as well as the more quantitative

LOW-PASS SPATlAL FILTERING OF SATELLITE RADAR DATA 893

FIG. 7. Unfiltered (upper left) and 3SQMD2 filtered (upper right), 28° incidence angle SIR-S data (scale 1: 191,000). Below each of these two images,

there is a 4 x enlargement.

evaluation techniques, the number of candidate treatments tobe considered in the final selection process was considerablyreduced. The most appropriate number of iterations was found

to be two. For the 28.5-m SIR-B data, the best window size wasselected to be three (either 1 by 3 separable or 3 by 3 non-separable). Therefore, this left four treatments for further analysis:

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1989894

3 by 1 Separable Recursive Median 2 Iterations (3SRMD2), 3 by3 Square Median 2 Iterations (3SQMD2), 3 by 1 Separable Mean2 Iterations (3SMN2), and 3 by 3 Square Mean 2 Iterations(3SQMN2).

As can be seen in Table 7, there was no significant differencebetween the classification accuracies achieved with the 3SMN2and 3SQMN2 treatments. However, visual inspection of theclassified images showed that the square mean treatment(3SQMN2) produced more acceptable results. The choice betweenthe two median algorithms was fairly straightforward. Theseparable recursive median (3SRMD2) algorithm produced imagery

TABLE 6. RESULTS OF THE EDGE/BOUNDARY RETENTION STUDYSHOWING AGGREGATE SCORES FOR THE TRANSECT CURVES BASED ON

FIVE TRANSECT LOCATIONS. THE HIGHER THE SCORE, THE BETTER THE

EDGE/BOUNDARY RETENTION.

Treatment Score

7SRMDl 239SRMDI 239SRMD2 239SRMD3 237SRMD2 227SRMD3 225SRMD3 205SRMD2 205SRMDI 193SRMD3 183SRMD2 183SRMDl 183SMNI 123SQMNI 123SQMDI 123SQMD2 93SQMD3 93SQMD4 85SMNI 53SQMN2 53SMN2 45SQMNI 43SQMN3 23SMN3 15SMN2 05SMN3 05SQMN2 05SQMN3 07SQMNI 07SQMN2 07SQMN3 07SMNI 07SMN2 07SMN3 0UNFILT 0

TABLE 7. STATISTICAL EVALUATION OF OVERALL CLASSIFICATION

PERFORMANCES SHOWING THE EFFECT OF ALGORITHM FOR EACH

WINDOW SIZE IMPLEMENTED AT Two ITERATIONS. THE PCCoS ARE BASED

ON 51 TEST FIELDS (2881 POINTS) AND FIVE COVER-TYPE GROUPS.STATISTICAL DIFFERENCES WERE CALCULATED USING THE STUDENTIZED

NEWMAN-KEULS MULTIPLE RANGE TEST (ALPHA = 0.05). NON-SIGNIFICANT DIFFERENCES ARE UNDERLINED.

3SRMD2 3SQMD2 3SMN2 3SQMN285.7 88.2 90.7 90.7

5SRMD2 5SMN2 5SQMN292.8 94.9 95.1

7SRMD2 7SMN2 7SQMN2

83.9 95.4 95.7

that was blocky in appearance, while the square median (3SQMD2)imagery had a more natural appearance (and thus better) witha more gradual transition between tones. Classification resultswere significantly better with the 3SQMD2 treatment than withthe 3SRMD2 (88.2 percent versus 85.7 percent). Therefore, the3SQMD2 treatment was judged to be the better of these twotreatments.

When the best mean and median treatments were compared,it was found that the best mean treatment (3SQMN2) providedsignificantly higher classification results than the best mediantreatment (3SQMD2) (90.7 percent versus 88.2 percent). However,the Edge/Boundary Retention Study (see Table 6) and the VisualAssessment Study evaluations indicated that the mediantreatment retained edges better and preserved more detail.Although the difference in classification accuracies between thebest mean and median treatments is statistically significant, itwas decided that the actual difference in classification accuracy(i.e., 2.5 percent) was not large enough to outweigh the resultsobtained in the Edge/Boundary Retention Study and VisualAssessment Study. Additionally, it was felt that the test fieldselection procedure biased the results in favor of the 3SQMN2treatment. Test fields were limited to a rectangular shape bythe software utilized. This caused the fields to be centered withinrelatively large cover-type units. Therefore, the test fields werenot sensitive to the loss of small spatial features (i.e., cover-type units). The Visual Assessment Study showed that the3SQMD2 treatment was superior to the 3SQMN2 treatment inretaining such features. It should be pointed out, however, thatthe data utilized in this study contained a complex mixture ofsmall cover-type units. In cases where per-pixel classification oflarge homogeneous areas is to be conducted, the mean filtermay be more effective as indicated by the improved classificationaccuracy.

Thus, in summary, after the 34 filter treatments were evaluatedfrom both a quantitative and qualitative standpoint, the 3 by 3square median filter implemented at two iterations (3SQMD2)produced the best overall results with the 28.5-m SIR-B data.Figure 6 shows the improvement in the visual appearance ofthe SIR-B imagery due to filtering with the 3SQMD2 treatment.

CONCLUSIONS

The major conclusions from this research with 28.5-m ShuttleImaging Radar-B digital data using four filter algorithms (Le.,square mean, separable mean, square median, and separablerecursive median) were that

• Both quantitative and qualitative evaluation techniques were neededto effectively identify the best filter treatment,

• The 3 by 3 square median filter implemented at two iterations(3SQMD2 treatment) was the best filter treatment for reducing spec-kle effects in this data set,

• The second iteration provided much better results than were ob-tained with a single iteration, and

• A window size of three was determined to be best.

ACKNOWLEDGMENTS

This research was supported by Subcontract 956952 from theJet Propulsion Laboratory under NASA contract NAS7-918.Champion International Corporation, Owens-Illinois Incorpo-rated, Southern Resin and Chemical Company, and the USDA-Forest Service are acknowledged for providing forest stand in-formation and maps and also access to their landholdings inthe study site area. The Jet Propulsion Laboratory is acknowl-edged for its prepocessing of the SIR·B data which included reg-istration to the coincident TM data set.

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(Received 25 July 1988; accepted 31 October 1988; revised 1 February1989)

Call for PapersThe Promise of Low-Input Agriculture

31 August Deadline for November-December 1989 Issue of the Journal of Soil and Water Conservation

The Soil and Water Conservation Society (SWCS) invites contributions for a special issue of the Journal of Soil and Water Conserva-tion, "The Promise of Low-Input Agriculture," that will appear in November-December 1989. The deadline for contributions to thisissue is 31 August.

SWCS is publishing the special issue as a spinoff from its March 1989 conference that looked at how modem-day farming practicescan best be adjusted to help sustain the natural resources on which agriculture depends. The conference and special issue are beingsponsored in cooperation with the U.S. Department of Agriculture and the U.S. Environmental Protection Agency.

The two main themes covered in the conference, and subsequently, in the special issue are:• How sustainable farming systems can be developed and fostered to achieve soil conservation, water quality, and related

natural resource management goals.• How sustainable agricultural concepts and practices can best be incorporated into mainstream agriculture.

Feature articles, research reports, and commentary type articles will be considered. Feature articles should not exceed 15 pages ofdouble-spaced, typewritten copy; research reports should not exceed 12 pages; and commentary should not exceed 6 pages. Allmanuscripts submitted will be subject to peer review. Authors will be notified of acceptance or rejection by 1 October 1989. For a copyof JSWC editorial guidelines and to submit manuscripts contact:

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