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characterization using very high spatial resolution
remote sensing data
Carole Delenne, Sylvie Durrieu, Gilles Rabatel, Michel Deshayes,
Jean-Stéphane Bailly, Camille Lelong, Pierre Couteron
To cite this version:
Carole Delenne, Sylvie Durrieu, Gilles Rabatel, Michel Deshayes, Jean-Stéphane Bailly, et al..
Textu-ral approaches for vineyard detection and characterization using very high spatial resolution remote
sensing data. International Journal of Remote Sensing, Taylor & Francis, 2008, 29 (4), pp.1153-1167.
�10.1080/01431160701311259�. �hal-01196900�
Textural approaches for vineyard detection and characterization using very high
spatial resolution remote-sensing data
C.DELENNE
∗
†
,S. DURRIEU
†
, G.RABATEL‡
,M.DESHAYES†
, J.S.BAILLY†
, C.LELONG†
, P.COUTERON
§
†
UMRTETIS,CEMAGREF -CIRAD-ENGREF,34093Montpellier,France‡
UMRITAPCEMAGREF-ENSA-CIRAD,34196Montpellier,France§
FrenchInstituteofPondicherry,PB33,605001Pondicherry,India (Received00Month200x;Innalform 00Month200x)Vine-plotmappingandmonitoringarecrucialissuesinlandmanagement,particularlyforareas wherevineyardsaredominantlikein someFrench regions.Inthiscontext, theavailabilityof anautomatictoolforvineyard detection andcharacterizationwouldbevery useful.Theobjectiveofthestudyistocomparetwodierentapproachestomeetthisneed.Therstoneusesdirectionalvariationsof thecontrastfeaturecomputedfromHaralick'scooccurrencematricesandthesecondoneisbasedonalocalFourierTransform.Foreach pixel,a`vineindex'iscomputedonaslidingwindow.Tofosterlarge-scaleapplications,testandvalidationwerecarriedoutonstandard veryhighspatialresolutionremote-sensingdata.70.8%and86% ofthe271plotsofthe studyareawerecorrectly classiedusingthe cooccurrenceand the frequencymethod respectively.Moreover, the latterenabled an accuratedetermination (less than3% error) of interrowwidthandroworientation.
Keywords:Texture;Imageanalysis;Cooccurrence;FourierTransform;Vineyard
1 Introduction
Thanks to the increased availability of remote sensing data and of more powerful computers, automatic
analysismethodscan be developedto buildorupdategeographicaldatabasesforlandmanagement.
Accu-ratedigitalmappingofvineyardsforwine-growingregionssuchasLanguedoc-Roussillon(France)couldbe
extremely usefulfor many reasons. For example,these mapscan beintegrated within Geographical
Infor-mationSystems(GIS)whichcanbeusedbywinegrowercooperativestoimprovethemonitoring ofquality
compliance in areas registered in the list of Controlled Origin Denomination. The management of
pollu-tion, erosion and ood risks is another eld that can take advantage of these maps. Indeed, these risks,
depending on culture and soil surface condition, are worsened by mechanization and intensive cropping
practices (Wassenaar etal., 2005;Vincini etal., 2004).
User demand usually concerns 1) locating vine plots and 2) identifying some characteristics that can
be connected to cropping practices or cropquality(interrow width, orientation of rows, presence ofgrass
between rows...).
Mostvineyard relatedstudies usingremotesensingdatameet thesecondrequirement bydetecting vine
rows(Bobilletetal.,2003)forexample,orbycharacterizingtrainingmode(Wassenaaretal.,2002)orfoliar
density(Hall et al., 2003) for previously delimitedplots. Those dealing with vineyard plots identication
anddelineation often usemulti-spectralinformation onover-metricspatial resolution images, provided by
satellites Landsat, Ikonos or airborne sensors (Rodriguez et al., 2006; Johnson et al., 2001; Gong et al.,
2003). However, the increasing availability of Very High Spatial Resolution (VHSR) images oers a lot
of new potential applications: the object shape and spatial structure are becoming more distinguishable,
providing greater discrimination and characterization opportunities. Indeed, according to the
Shannon-Nyquist theorem
1
,periodic patterns resulting from the spatial arrangement of vine plants (often in lines
or grid),become perceptible withaspatial resolution thatisat leasttwiceassmallasthepattern period.
1
In the study area, like in many wine-growing regions, the minimum distance between two vine rows, is
about 1.5 m; consequently, image spatial resolution should be lower than 0.75 m. However, as they deal
withspatialstructuresor shapes,these newapplications alsorequire newimage processing approaches.
Inarecentstudy(Warneretal.,2005),aclassicationalgorithmbasedonananalysisofautocorrelograms
was developed and tested using Ikonos panchromatic imagery of Granger (Washington). This method,
althoughprovidinggoodresultsintheapplicationpresented,couldhardlybegeneralizedinolderEuropean
wine-growing regions where the heterogeneity among researched patternsis high. Because of theperiodic
organization of vineyards, frequency analysis appears as a suitable approach for vine detection. Wavelet
analysispresented in(Ranchin et al., 2001)is applied to 25 cm resolution images for vine/non-vine pixel
classication. Using a plot basis validation, 78 % of plots were accurately classied; but this approach
is complex and needs signicant user intervention. A Fourier Transform based analysis should be more
straightforward and quite as eective since this tool is perfectly suited for oriented and periodic texture
detection. Its eciency has been demonstrated to characterize and monitor natural periodic vegetation
(CouteronandLejeune, 2001;Couteron,2002). Wassenaar(Wassenaaretal.,2002)successfullyuseditfor
vine/non-vineclassication and characterization ofpreviously delimitedplotson 25cmresolution images.
Onasampleof46`extremelyvariedeldpatterns',vine/nonvineclassicationwascorrectforalltheplots
andonlyveerrorswereencounteredconcerningtrainingmodeclassicationofthe41vineplots.Moreover,
this method gave a very precise (less than 1 % error) estimation of interrow width and row orientation.
Prat(Prat, 2002)employed asimilar methodto identify vineplots inanimage.Thisone wasrstdivided
into small square windows (of 12.5 m side) on which ve indices were deduced from Fourier spectrum
andimageradiometry.Then, amultidimensional supervisedclassicationusingmaximumlikelihoodledto
correctclassication of 81%of vine pixels.
OtherverypopularapproachesfortexturalanalysisarebasedonHaralick'sresearches,accordingtowhom
`the texture informationin an image iscontained inthe overall or average spatial relationship which the
gray tones in the image have to one another' (Haralick et al., 1973). He then introduced the `gray-level
spatial dependency' (cooccurrence) matrices, which had remained unused for many years as they were
tootime-consuming. Withtheamazingincreaseof computerpower, cooccurrencebecameone of themost
popular characterization toolsbecauseitisbasedonsecondorderstatistics, wellsuited forthedescription
of textural properties, which the human eye is most sensitive to. A lot of studies have demonstrated its
relevancefor texturalanalysis(Chenet al.,1979)andits usefulnessfor manyapplications: urbanplanning
(Moralesetal.,2003),medicine(Smuteketal.,2003),scienticpolice(Vermaetal.,2002),textileindustry
(Abdulhady etal., 2002)... and even remote-sensingfor agro-forestry (Arvis et al.,2004).
The general objective of this work was to develop an automatic method for vineyard detection and
characterization usingveryhighspatialresolutionremote-sensingdataandwithoutanyaprioriknowledge
oftheparcel plan. Indeed,this latterisnot available inmostEuropean wine-growing regionsand,when a
georeferencedcadastreisavailable,itgenerallydoesnotcorrespondtoagriculturalplotsactuallyobservable
in the eld. To foster large-scale applications, image used was a `standard' orthophotography in natural
colour, with a 50 cm spatial resolution, similar to data available on the whole French territory. In this
paper, the relevanceof cooccurrencebased analysisisevaluated incomparison witha frequencyapproach
to meet the need for vine plot detection. Moreover, characterizations of row orientation and/or interrow
width,deduced fromthese approaches, arecompared.
2 Study area
The study area is part of theLa Peyne watershed(110 km
2
) and is located in theLanguedoc-Roussillon
region - France (Figure 1). This zone is representative of the French Mediterranean coastal plain with
respectto geology,agriculturalpracticesandvineyard management(Wassenaaretal.,2002). Two subsets,
of 2 km
2
and 1 km
2
, have been selected from this area near Roujan municipality (43
◦
30'N, 3
◦
18'E).
Despitea general decrease, vine cultivationis still predominant and coversabout 70% ofthe271 plots of
thestudy area.
The diversity of agricultural practices in the study area leads to a great heterogeneity among vine
Paris
Montpellier
Roujan
Figure1. LocalizationofthestudyareainFranceandEurope.
(a)
(b)
Figure2. Vinetrainingmodes:a)Goblet(gridpattern),b)Trellis(rowpattern).
spectral information for vineyard detection. However, on VHRS remote sensing data, two main patterns
can be observed accordingto trainingmode (gure 2):
•
Grid pattern: about a quarter of the vineyard considered in this study is trained as `goblet'. This old methodofvinetraininginvolvesnowiresorothersystemofsupport:vinestocksareplantedaccordingtoa gridpattern,oftensquare,withapproximately 1.5m
×
1.5mspacing inthestudyareabut sometime up to 3mspacing indryregions.•
Linepattern:mostoftherecent vineyardsaretrainedusinghorizontalwirestowhichthefruitingshoots are tied. Spacing separating two wires is higher than spacing between vine stocks guided by thesamewire (often 1 m
×
2.5 m spacing in the study area), which leads to row patterns. More adapted to mechanization, thistraining mode namedtrellis or wire-training, ismainly used.These patterns can be observed on each spectral band and are less dependant on the previously cited
heterogeneities. Then, withvineyard detectioninaim,methods should be more robust whendealing with
0
25
50
100
Meters
´
Figure3. Zoomonthestudyarea.Alltheplotsofthestudyareahavebeensurveyedandintegratedwiththeircharacteristicsina geodatabase.
3 Data acquisition
DataacquisitionwasmadeduringtherstweekofJuly 2005,whenfoliardevelopmentwassuchthatboth
vineandsoilwerevisibleonaerialphotographs.AdigitalcamerawasusedaboardanUltraLightMotorized
(U.L.M.) to acquire photographs in natural colours (Red, Green and Blue). Images were geometrically
correctedandgeoreferencedusingArcGis
r
(ESRI),mosaickedusingERDAS
r
Imagine(LeicaGeosystem)
andresampled toa50 cmresolution.Theresultingimages have characteristics similartothose ofthe
BD-Ortho
r
coverage oftheFrench geographicinstitute(IGN),which iswidelyusedand coversalmostallthe
French territory.
For result validation, ground-truth information was collected at the same time as image acquisition.
Each ofthe 271 vine and non-vine plots of the site hasbeen digitized ina GISdatabase (gure 3) which
alsocontains information concerninglanduseand aseries of characteristics for vine plots:training mode,
interrow width, orientation, rough estimates of vine height and width, soil surface condition... Row
orientation and interrow width were obtained by precise on-screen measurements: row orientation was
measured with a 1
◦
precision and interrow width wascalculated by dividing thewidth of the whole plot
bythenumber ofinterrows.
4 Textural analysismethods
Both methods compared inthis paper were implemented to calculate textural characteristics on the
sur-roundingof each pixelusing asliding window.
4.1 Cooccurrence analysis: use of Haralick's contrast feature
Therstmethodpresentedinthisstudyhasbeendevelopedfromcooccurrencematricesdenedin(Haralick
et al., 1973). Element
p
i,j
of each matrixP
d
x
,d
y
contains the number of transitions from grey level
i
toj
between twopixels of imageI
,distant fromd
x
pixels incolumn andd
y
inline (equation1):(1,0)
(1,1)
(1,-1)
(0,-1)
(-1,-1)
(-1,0)
(-1,1) (0,1)
x
0°
45°
90°
135°
d=1
26.5°
d=2.24
d=1.41
~60°
d=21.9
d: distance in pixels
y
Figure4. Cooccurrencecalculationdistancesvsorientations.Left:nearestneighbours.Right:moreorientationsimplieslonger calculationdistances(greaterthaninterrowwidths).
p
i,j
= P
d
x
,d
y
(i, j) = #
((x, y), (x
0
, y
0
)) / x
0
= x + d
x
, y
0
= y + d
y
;
(I
x,y
= i & I
x
0
,y
0
= j) or (I
x,y
= j & I
x
0
,y
0
= i)
(1)where # denotes the number of elements inthe set and
I
x,y
is thegrey level of pixel of coordinate (x,y). Eachcooccurrencematrix isthensymmetricand for aN
g
grey levelimage, itssize is equaltoN
g
× N
g
.Depending on the spatial resolution used (50 cm) and row spacing encountered (from 1.4 mto 2.5 m)
analysismust be done on transitions between one pixeland its direct neighbours inorder to characterize
soil-vinetransition:
|d
x
|
,|d
y
| ∈ {0, 1}
.Onlyfourdirectionsarethenexplored:θ
=0◦
,45◦
,90◦
or135◦
.Withimage coordinates increasing from upper leftto lowerright corner:
d
0
= (d
x
, d
y
) = (0, −1)
,d
45
= (1, −1)
,d
90
= (1, 0)
andd
135
= (1, 1)
.Searchformoredirectionswouldimplylongercalculationdistances,unsuited to interrow widths(gure 4).Fromcooccurrencematrices, Haralickdened 14textural characterization features, someofwhichbeing
correlated. As preliminary comparative analysis (unpublished), they have been computed on a sliding
windowapplied ona synthetic image imitating3 vine plots(with roworientedat 0
◦
,45◦
and90◦
)aswellasanon-vineplot,modeledbyarandomtexture.Somefeatures(e.g.correlationorangularsecondmoment)
could be used to highlight vine plots, but their histogram have a highdispersion, which would hamper a
good pixel classication in vine/non-vine. That is not the case for contrast feature (equation 2), which
appeared to be well suited for vineyard detection. The higher the local variations inthe sliding window,
the higher the contrast, strongly depending on orientations of both vine row and feature calculation.
Consequently,contrast is highwhen calculated in a direction thatis perpendicularto vine rows and very
lowwhen calculatedinrow direction.
f
2
(P
d
x
,d
y
) =
N
g
−1
X
n=0
n
2
X
|i−j|=n
p
i,j
(2)Wethenproposea `vineindex'basedonthisproperty,whichcan beusedto distinguishrowpatternsfrom
othernon-orientedhighcontrastedpatterns(e.g.checkerboard-like). Indeed,vineyardwillbecharacterized
byahighdierenceofcontrastcalculatedintwoperpendiculardirections.Foreachpixel,signeddierences
between thefour pairs of perpendicular directionsare compared. The highest dierence is thevine index
andthetwo directionsassociated provide a classof row orientation to thefocalpixel. Theoretically,when
calculatedonvineyardswithroworientation
θ ∈ [23, 68]
,contrast shouldbehighfordirectiond
135
andlow
for
d
45
,sothatthese vineyardsshouldbeclassiedinclass
C
45
;likewise,classC
90
correspondstovinerow orientations inθ ∈ [69, 112]
,C
135
toθ ∈ [113, 158]
,andC
180
toθ ∈ [158, 180]
orθ ∈ [1, 23]
. Contrast is all themore interesting asitcan becomputed directlyonimage withoutpreviouscalculation ofcooccurrencematrices; this considerably reduces calculation time. Figure 5 describes the classication method applied
Contrast
V=max D
q
Vine index V
Orientation q
1
2
3
Original image:
0
255
synthetic image with
3 oriented and 1 non
oriented texture
d
135
d
45
-d
135
d
45
d
135
-d
45
d
90
d
0
d
90
-d
0
d
0
-d
90
D
0
D
90
D
45
D
135
C
180
C
135
C
90
C
45
Figure5. VineyarddetectionusingHaralick'scontrast.1)Contrastcalculationonslidingwindowinorientations:0
◦
,45◦
,90◦
and 135◦
.Thebrighterthepixel,thehigherthecontrast.2)Dierenceofcontrastbetweentwoorthogonaldirections.3)Themaximum valueofthedierencesgivesthe`vineindex'andanestimationofroworientationwithaclassicationinfourclasses.
4.2 Frequency analysis:use of local Fast FourierTransform
Thesecondmethoddevelopedinthisstudy isbasedontheworksof(Wassenaaretal.,2002)whousedthe
Fourier Transform to characterize already delimited plots.Here, we test thesame kind ofapproach when
theonly availabledatais theaerialimage (the maingoal beingvineyard detection).
Fourier theory statesthat almost anysignal, including images, can be expressedas a sum of sinusoidal
waves oscillating at dierent frequencies. The discrete Fourier transform (FT) of an image is computed
usingtheFastFourierTransform(FFT)algorithm.Takingthemodulusofthecomplex-valuedFFTresults
yields the FT amplitude (or spectrum), which can be represented in the frequency domain as an image
of the same size as the initial image,
I
. In theconventional representation, this image is symmetricwith respecttoitscentre,whichcontainstheaverageofI
i.e.theamplitude ofthenullfrequencyF
0
.Eachpixel corresponds to a particular spatial frequency increasing the further it is from centre. Its value codes theamplitudeofFourier spectrum,whichdependsonthe frequencypresenceinimage
I
.Theamplitudeofthe discrete Fourier Transform ofI
isdened byequation3:a(u, v) =
1
N
x
N
y
N
x
−1
X
x=0
N
y
−1
X
y=0
I
x,y
exp
−j2π
ux
N
x
+
vy
N
y
(3)
where
(N
x
, N
y
)
is the size (column, line) of both images,x = 0 . . . N
x
− 1
,y = 0 . . . N
y
− 1
are spatial indexes(inimageI
)andu = 0 . . . N
x
− 1
,v = 0 . . . N
y
− 1
arefrequencyindexes(intheFourierspectrum). The method consists inapplying the FFT algorithm on a sliding window. When this window containsvineyard arranged in rows, two peaks will be present on the Fourier image, and will be symmetric with
respectto thecentre; for the gridpattern ofa gobletvine, fourpeaks willbepresent at90
◦
(see gure6).
TheFFT algorithm assumes thatthe data isperiodical, i.e. theimage repeats from end to end innitely.
Therefore,FFT calculation on a nite window maylead to aliasing artefacts (Gibbs'phenomenon) when
pixel values at the edges of the window do not match. To avoid these artefacts, which could introduce
additional peaks,pixel values arerst ofall multipliedbya Hanningwindow (byVon Hann)which shape
ishalfacycleofacosine wave andisnullattheedges(see gure7).Threecharacteristics canbededuced
fromthepeakvalueand position:
•
The distancer
of the peaks from theimage centre correspondsto thepattern frequency inthewindow and,consequently,isconnectedtothevineyard interrowwidth,whichisequaltothesizeN
ofthesliding windowdivided byr
.Peaks can thenbesought inan annular ring, correspondingto potential vineyard interrowwidthsto avoidconfusion withother periodicpatterns(e.g.orchards, characterized a bylargerinterrow).
•
The angleθ
,between horizontal lineand one peak,determines thewave direction inapolarcoordinateFigure6. ExamplesofFourierTransform.Theleft-handsideshows3images,whicharethesamesizeastheslidingwindow,extracted fromagobletvine(up),atrellisvine(middle)andanon-vine(down);thecentreshowstheirrespectiveFourierTransformandthe
right-handsideshowsthefrequenciesremainingafterthresholding(thesamethresholdisusedforthethreeimages).
FFT
Vine Index V
Orientation q
Interrow N/r
r
q
Fourier spectrum of
the sliding window
synthetic image with
3 oriented and 1 non
oriented texture
Original image:
1
2
Peak V
Hanning window
3
1m
3m
1°
180°
0
255
Figure7. VineyarddetectionusingFourierTransform(FT).1)UseofaHanningslidingwindowofsizeN
×
N.2)FTcalculationon thewindow.3)SearchforthemaximumFTamplitudeamongthepotentialvineyardfrequencies;foreachpixel,thisvalueissavedas`vineindex'anditspositiongivesroworientationandinterrowwidth.
system, whichis equal torow directionina geographical coordinate system(90
◦
oset).
•
Peak amplitude is the `vine index': the higher theamplitude, thehigher theprobability of thewindow beingina vineyard.5 Implementationof the texturalanalysismethods
Bothmethods wereimplemented inClanguage andapplied on thestudy area.
Asensitivityanalysistothewindowsize hasbeencarriedout sinceaccuracyofdetectionand
characteri-zationdependsonthenumberofpixelsinthewindow.Ononehand,thiswindowmustbe largeenoughto
takeintoaccounttherepetitionofroworgridpatterns,soalargewindowprovidesmorepreciseinformation
whenlocatedinsideaplot. Ontheother handitdecreasesclassication resultsnearplots boundariesasit
cancontainseveralpatternsatthe sametime,andofcourse,increasesthecalculationtimes. Eightwindow
sizes have been tested from
11 × 11
to39 × 39
pixels. For the frequency based method, results become acceptablefora27 × 27
windowsize.(13%ofbadlyclassiedpixels)andthelowestrateofmisclassication (12.2%)isreachedfor31 × 31
.Extendingwindowsize upto39 × 39
pixelsdoesnot improveresults(12.4%(a)
(b)
(c)
Figure8. Resultsonasubsetofthestudyarea.a)originalimage,b)vine-indexissuedfromthecontrastmethod,c)vine-indexissued fromthefrequencymethod.Themanualsegmentationissupperposedingreyoneachimage.
ofmisclassiedpixels)whiledoublingcomputationaltime.Consequently,thebesttrade-ofor thewindow
sizeisabout
31 × 31
pixels,which cancontainfromveto tenvinerows inthestudyarea.Through visual assessment ofthe dierent window sizes, thislatter alsoappears tobe thebest forthecontrast approach.Themethods have been testedoneachofthe threechannelsoftheimage.Vineindex,produced byboth
methods, is an indicator of the probability for a pixel to belong to a vineyard. For vineyard detection,
a threshold has been dened to separate two classes: `vine' and `non vine'. The pixels whose vine index
is lower than the threshold are classied as `non vine', the others as `vine'. Threshold determination is
often empirical; here, it was chosen to minimize global classication error for a representative sample of
thedatabase plots.Therefore,omissionerror (vinedetected asnonvine)is chosen lowerbut almostequal
to commissionerror (non vine detected asvine).Some testshave shownthat asamplecontaining10 %of
theplots was large enough to determine a threshold valuethat isvery close to the one obtained using all
theplots. The sample must be representative enough of the study area, particularly in termsof landuse
andvineyard trainingmode.
6 Validationmethod
Validation is performed on a plot basis using all the 271 vine and non-vine digitized plots of the study
area. A simple classication rule is employed: a plot is classied as `vine' if at least 75 % of its pixels
are `vine', as `non vine' if at least 75 % of its pixels are `non vine' and not classied otherwise. Then,
vine plot characteristics (orientation class obtained by Haralick's contrast and orientation and interrow
widthgiven byFourier Transform) arechosen to be the majority value amongthe pixels of the plot. For
validation,resultsof plotclassication and characterization arecompared to theinformation contained in
theground-truthdatabase.
7 Results
BestresultswereobtainedwiththeRedchannel,probablybecauseitprovidesthehighestcontrast between
vine and soil surface, even when covered by grass. Therefore, we only present results derived while using
this channel. Figure 8shows vine-indexof bothmethods on asubset of thestudy area.
7.1 Classicationresults
Table1 gives confusionmatrices which enablethe estimation ofplot classication qualitybycomparingit
togroundtruth data.Consideringboth vineand non-vineplots,70.8%ofthe271 plotsarewell-classied
and81nonvineplotsusingRedchannel:aplotiswell-classiedifatleast75%ofitspixelsarewell-classied, badlyclassiediflessthan25%ofitspixelsarewell-classied,notclassiedotherwise.
Contrastmethod Frequencymethod
vine nonvine notclassied Total vine nonvine notclassied Total
Vine 72% 7% 21% 100% 89% 5% 6% 100%
Nonvine 1% 68% 31% 100% 2% 79% 19% 100%
Becauseof the thresholdchosen, both methods lead to a worse classication for non vine than for vine
plots.Themaincauseofnondetectioniswhenthevineistooyoungi.e.lessthanthreeyearsold.Vegetation
isthusnot suciently developed for therows tobevisible onaerialphotographs; consequently,these vine
plotshavegoodclassication ratesofonly 26%and47%for contrastandfrequencymethods respectively
(seegure 9bfor an example).
Globally,results provided bycontrast methodarepoorerthan thoseprovided byfrequency method.For
nonvineplots,oneexplanation maybethatcontrastdoesnottakeintoaccounttheperiodicityofpatterns:
aroad,forexample,canleadto adierenceofcontrastintwoorthogonaldirectionsthatisashighasthat
ofavine but doesnothave apeakoffrequency correspondingto vine interrow width.Figure 9ashows an
exampleofnonvinedetectedasvinebycontrastmethodbutnotbyfrequencymethod.Likewise,confusion
could theoretically appear between vine and orchards. However, since the sliding window size is adapted
tovineyardsinterrowwidths, vineindex willbelower onorchardsbecausetheir interrow widthsaremuch
larger.
For vine plots, results must be analyzed according to training mode. Indeed, goblet vines benet from
a good classication rate of only 49 %using contrast method against 89.7 % for `adult' trellis vines (all
gobletvinesareadult). Likewise,frequency method leads toa goodclassication rateof 88.6%for goblet
vinesand 95.7%for `adult'trellis vines.
For both methods, thepoorer results obtained for goblet vines mainly have two originswhich lead to a
lowvisibilityof soilbetween rows.Firstly,gobletvinesarenot stressed bywires andcan growfreelyinall
directions;secondly,interrowwidthsofgobletvinesaregenerallysmallerthan thoseoftrellisvines(onthe
study area, 67 % of the goblet vines have an interrow width lower than 160 cm against only 2 % of the
trellisvines).
Concerning goblet vine classication, the big dierence (39.6 %) between methods is due to the fact
that goblet vines are often planted on a square grid so that contrast is identical in both perpendicular
directions,whichhamperdetectionbythecontrast method(seegure9cforexample).Infact,most goblet
vines properlyidentied by the contrast method are pruned along one direction, which leads to a higher
contrast intheperpendicular direction.
7.2 Results of vine plot characterization
Estimationof orientation andinterrowwidthobtained byboth methods arenowexamined.
With the contrast method, 78 % of the plots correctly classied as vine have been allocated with the
correctorientation class(amongthe fourused). Asevoked previously,deningmore than four orientation
classescannotbeconsideredwiththisresolutionbecauseitwouldimply,forcontrastcalculation,adistance
toolargeinfront ofinterrowwidth(gure4).Indeed,ifa30
◦
classissought,thedistanceinpixelsneeded
to compute contrast feature will be at least
(d
x
, d
y
) = (5, 3)
i.e. anEuclidian distance ofabout 2.9m(for 30.96◦
and 10.98mfor 30.07
◦
),largerthan most ofinterrowwidth.
Figure10a showscharacterization results for well classied vine plots, using frequency method.Fourier
Transformleadstomoreaccurateresultsforvineroworientation.Indeed,betweenon-screenmeasurements
and method estimation, an average absolute dierence of 3.5
◦
was found, which is lessthan a 2 % error.
Moreover, errordistribution is almostcentered (gure10b).
Interrow widthcalculation is also very precise with an average absolute dierence of 6.2 cm, i.e. about
3 % error (see gure 10d for error distribution). The four outliers shown in gure 10c, concern two vine
(a)
(b)
(c)
(d)
Figure9. Examplesofplots.a)Noncultivatedplotrecognized asvinebycontrastmethod:anorientedpatternisvisiblebutwithno particularfrequency.b)Veryyoungtrellisvine,badlyclassiedbybothmethods:rowsarehardlyvisible.c)Gobletvineclassiedas
nonvinebycontrastmethodandwell-classied(with98%ofpixels)byfrequencymethod.d)Trellisvine,wellclassiedbyboth methods.
y = 0,9769x
R
2
= 0,9961
0
20
40
60
80
100
120
140
160
180
0
20
40
60
80
100
120
140
160
180
Row orientation measured (°)
R
o
w
o
ri
e
n
ta
ti
o
n
c
o
m
p
u
te
d
(°
)
Orientation
Bisector
Regression line
0 0 0 0
3
2
5
6
4
12
14
16
20
16
13
16
13
5 5
7
6
4
2
0
0
5
10
15
20
25
-11 -9
-7
-5
-3
-1
1
3
5
7
9
11
Errors (°)
N
u
m
b
e
r
o
f
p
lo
ts
1
0
1
5
13
22
51
33
29
4
4
2
0
0
0
10
20
30
40
50
60
-30
-20
-10
0
10
20
30
Errors (cm)
N
u
m
b
e
r
o
f
p
lo
ts
y = 1,0035x
R
2
= 0,9457
100
150
200
250
300
100
150
200
250
300
Interrow measured (cm)
In
te
rr
o
w
c
o
m
p
u
te
d
(c
m
)
Interrow
outliers
Bisector
Regression line
b)
a)
c)
d)
Figure10. Orientationandinterrowcharacterizationusingfrequencymethod.Right:comparisonofretrievedcharacteristicsandplot measurements;regressionandbisectorlinesarealmostconfounded.Left:errordistributions.
interrow width, and two vine plots ploughed between rows, for which interrow width determined is half
on-screenmeasurement. Characterization resultshighly depend on thesize ofthecalculation window (see
below),whichiswhy(Wassenaaretal.,2002),whoappliedtheFFTalgorithm ontheentireplot,obtained
1%errorsfor both orientation and interrowwidth.
8 Conclusionand discussion
Twomethodswerecomparedforvineyarddetectionandcharacterizationfromaerialphotographpresenting
`standard' characteristics. The rst one was based on Haralick's cooccurrence analysis, which had been
successfullytested onmanyapplications but notyetfor vineyard detection. Thesecondone wasbased on
Fourier analysis, awell-triedapproach for periodic andorientedpattern recognition.
The originality of the proposed cooccurrence approach lies in the comparison of the contrast feature
than withthefrequencymethod mainlyfor two kindsof plots:
•
non-vine: contrast is sometimes higher in one direction than for its perpendicular, due to ploughing or roads for example, and this leads to a classication as `vine' of 32 % of non-vine plots. However,theresulting patternshaveno particular frequencyand aregloballywell-classiedbyfrequencymethod
(21%of commission error).
•
Goblet vines: with this training mode, when there is no privileged direction of pruning, contrast isthe same inboth perpendicular direction,which leads to a `non-vine' classication of 51 % ofgoblet vines.Onthecontrary,since their overall patternis periodic, theseplots arewell-classied withthefrequency
method(only21.4 %oferror).
The poorer results of goblet detection using both methods are strongly linked to the relation between
thepattern period (interrow width) and the image resolution: thelimit of theShannon-Nyquist theorem
is reached. However, this highlights the fact that a coarser resolution could be usedin many other
wine-growing regions, especially dry ones such as Castilla-la-Mancha in Spain, where interrow widths are up
to three meters. Theseapproachescould also be applied to orchardswith other resolutionsas longasthe
periodicpattern isvisible.
Incomparison withthecontrast method,the frequencyapproachnot only permitsa better
`vine'/`non-vine'classication (86%against70 %),but alsoa verypreciseestimation ofroworientation andinterrow
width (2 % and 3 % errors respectively) whereas only four classes of orientation could be dened and
distinguishedwith anaccuracy of78 %usingthe contrast feature.
In addition to the utility of characterization as such, orientation and interrow width estimation could
be usedto increaseplot classication qualityin prospectfor asegmentation stage.Ontheone hand,they
could be used to better separatedetected plots; indeed, some plots,which are spatially very close, would
be grouped within a same polygon unless they have dierent row orientation or interrow width. On the
other hand, these characteristics could help the discrimination of badly classied non-vine plots; indeed,
vine classied plotswithno particular orientation or interrow widthcouldbereclassied asnon-vine.
Inprospect,to meet the second userrequirement, characteristics of roworientation and interrow width
couldalso beused for anautomatic detection of eachvine row. Thiswould enable theevaluationof more
vineyard characteristicssuchasmissingvinetrees or soilsurface condition(e.g.presenceof grass between
rows). Moreover, vine index could also be used on vine plots as an indicator of vine quality, since its
intensitydependson the pattern contrast.
Acknowledgements
This work is part of two projects: Bacchus European Project (http://www.bacchus-project.com) and
Mobhydic project ofthe French national program of research inhydrology (PNRH).The authors thank
C.Debain (CEMAGREF)for his contribution concerningthe cooccurrenceapproach.
We wish to pay tribute to the late J.M. Robbez-Masson who died in a mountain accident. We are
deeplygratefulfor hiskindness, availabilityand for the very constructive discussions weshared withhim
concerningthis work atmeetings and on eldtrips.
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