IEEE

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 1

Tractor Wheel Tracks Detection, Delineation, andCharacterization in Very High Spatial

Resolution SAR Imagery

1

2

3

Christina Corbane, Nicolas Baghdadi, and Michaël Clairotte4

Abstract—Compacted tractor wheel tracks have been recog-5nized as key factors controlling runoff and erosion processes in6agricultural landscapes. In this context, the availability of an au-7tomatic tool for wheel tracks detection and characterization would8be very useful. In this letter, an original algorithm based on Radon9Transform is proposed for automatic wheel tracks detection on10Synthetic aperture radar images with a spatial resolution of 1 m.11Compared to on-screen measurements, wheel tracks orientations12and widths were accurately estimated for the images acquired with13shallow incidence angles whereas poorer results were observed for14sharp incidence angles.15

Index Terms—Detection, radon transform, Synthetic Aperture16Radar (SAR), tractor wheel tracks.17

I. INTRODUCTION18

WHEEL TRAFFIC used in certain agricultural manage-19

ment practices such as tillage, planting and harvesting20

operations may affect both soil erodibility and hydrological21

properties due to severe soil compaction [1]. Wheel tracks play22

the role of water guiding channels and may, hence, influence23

the drainage direction and intensity depending, respectively,24

on their orientation with respect to the terrain slope and on25

the spacing between wheels. Knowledge of the orientation and26

width of tractor wheel tracks, at the scale of an agriculture plot,27

can give partial evidence of the magnitude of soil compaction28

and is therefore essential for modeling runoff and erosion29

processes. Because remote sensing is considered as the favorite30

tool to provide spatial and temporal information, it can facil-31

itate the detection and monitoring of wheel tracks over large32

areas, representing a promising alternative to conventional field33

surveying methods. Typically, Very High Spatial Resolution34

(VHSR) Synthetic Aperture Radar (SAR) sensors could be used35

for monitoring wheel tracks at the scale of the agricultural36

plot thanks to their well-known sensitivity to variations of37

soil roughness and surface geometry and to their ability to38

image during all-weather conditions. The issue of tractor wheel39

tracks detection and delineation in VHSR SAR images can40

be described as follows: wheel tracks exhibit periodical linear41

patterns consisting of parallel rows with almost invariant inter-42

Manuscript received March 8, 2011; revised April 22, 2011; acceptedMay 23, 2011.

C. Corbane and M. Clairotte are with the European Commission, JointResearch Center, 21020 Ispra, Italy (e-mail: christina.corban@jrc.ec.europa.eu;michael.clairotte@jrc.ec.europa.eu).

N. Baghdadi is with CEMAGREF, UMR TETIS, 34093 MontpellierCedex 5, France (e-mail: baghdadi@teledetection.fr).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/LGRS.2011.2158184

row widths but with a large range of possible orientations which 43

prevent using classical textural approaches. Due to the periodic 44

organization of wheeled plots into straight lines both frequency 45

analysis (e.g., Fourier Transform) and linear feature detection 46

approaches (e.g., Radon Transform) seem suitable for wheel 47

tracks detection and characterization. Fourier transform (FT) 48

is suited for oriented and periodic texture detection. Its ef- 49

ficiency has been demonstrated to characterize and monitor 50

natural periodic vegetation [2]. Both Delenne et al. [3] and 51

Wassenaar et al. [4] applied FT to extract information on 52

the type of vineyard planting as well as crop spacing and 53

orientation on VHSR optical data. In the current study, SAR 54

imagery known for its inherent noise-like phenomenon is ex- 55

plored, thus limiting the use of FT approaches and privileging 56

a method that relies on linear feature detection such as the 57

Radon transform (RT). The RT integrates values of the pixels 58

along every line while each integral becomes a single point in 59

the transform space; this process of averaging diminishes noise 60

perturbations and hence increases the signal-to-noise ratio of 61

features of interest. For that reason RT has been considered a 62

quite appropriate method for the enhancement and detection of 63

linear patterns on SAR imagery. Several applications in SAR 64

exploited this property like ship wake detection [5], extraction 65

of roads and railways for the update of geographic maps [6] and 66

characterization of internal waves in oceanography [7]. 67

In this letter, an original algorithm based on RT is proposed 68

for the automatic wheel tracks detection and characterization 69

of previously delimited plots on VHSR SAR images. The 70

accuracy and the robustness of the method are analyzed for both 71

wheeled and nonwheeled plots observed at different dates with 72

varying SAR acquisition parameters. 73

II. STUDY SITE AND DATA SET 74

The selected study site is located in the Orgeval watershed, 75

East of Paris (France; Lat. 48◦ 51′ N and Long. 3◦ 07′ E). The 76

soil has a loamy texture and is very flat (slope < 5%). This 77

predominantly agricultural area is composed of wheat and corn- 78

growing plots. From September 2008 to April 2009, the winter 79

wheat covered approximately 50% of the watershed total sur- 80

face. The remaining surface portion corresponded to ploughed 81

soils awaiting future cultivation. The wheat height was about 82

5 cm in January, 10 cm February, 20 cm in March, and 35 cm 83

at the end of April. Following the sowing season (September 84

to October), the tractors are usually operated starting February 85

or March for spreading agricultural fertilizers. In some cases 86

fertilizer application operations take place from March until 87

June with two to five passages of tractors. The repeated passage 88

of wheel traffic over a plot has a compacting action on the soil 89

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2 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

Fig. 1. (a) Illustration of a typical wheeled plot. (b) A wheeled plot observedon a SAR scene acquired on 30 April 2008.

TABLE ITHE EXPERIMENTAL DATA SET CONSISTING OF A TOTAL OF

85 WHEELED (WP) AND NONWHEELED PLOTS (NWP)MANUALLY DELINEATED ON FOUR TERRASAR SCENES

in the wheel track that is observable in the field by the presence90

of parallel rows [Fig. 1(a)].91

For this experiment, four TerraSAR-X images (∼9.65 GHz),92

acquired at low and high incidence angles (26◦ and 50◦) in93

‘Spotlight mode’, in April 2008 and 2009, were analyzed. The94

TerraSAR images were taken with HH and VV polarized beams95

and with a ground pixel spacing of 1 m. The characteristics96

of the SAR scenes are summarized in Table I. For the vali-97

dation of the algorithm a total of 85 manually delimited plots98

were defined on the four SAR scenes. The experimental plots99

were selected in such a way they included both patterns of100

wheeled and nonwheeled plots (Table I). For each delineated101

plot, measurements of the rows’ orientation (α) and inter-102

row’s width (w) have been done by photo-interpretation as103

follows: assessment of the mean angle of all visible rows for104

row orientation and of the mean distance of all visible rows105

for inter-rows’ width [Fig. 1(b)]. The brightest radar returns in106

linear forms at the interior of some agricultural plots are caused107

by the double-bounce scattering between the compacted tractor108

wheel tracks and the wheat plants [Fig. 1(b)].109

III. METHOD110

A. Algorithm Overview111

The method to be developed to detect accurately tractor112

wheel tracks on SAR images described above under all con-113

ditions encountered, needs to fulfill a number of requirements:114

1) it should be able to detect linear structures, 2) it should allow115

the estimation of the rows directions and inter-rows’ width,116

3) it should be able to detect structures without user interaction,117

4) it should be robust to noises.118

To meet these requirements, a three step algorithm centered119

on the RT was developed (Fig. 2): In a first step, RT was120

computed for each pre-delineated agricultural plot allowing121

the detection of the bright lines corresponding to the ridges122

generated by the passage of wheel tracks; in a second step,123

wheeled plots were distinguished from nonwheeled plots and124

the orientation of the previously detected lines was computed;125

Fig. 2. Illustration of the three-step algorithm developed for the automaticdetection and characterization of tractor wheel tracks.

in a final step, inter-rows’ width was estimated though a proper 126

noise filtering, thresholding and peak detection approach. 127

B. Detection of Wheel Tracks’ Rows 128

The developed workflow is based on the assumption that 129

agriculture plots under process are previously delineated using 130

either existing digitized fields boundaries or a proven automated 131

field delineation method. A Gamma speckle filter was first 132

applied on the experimental plots manually delineated in the 133

SAR scenes. This was followed by an edge enhancement using 134

a 3 × 3 Laplacian filter. These two preprocessing operations 135

were meant to improve the visibility of the wheel tracks and 136

to facilitate their detection using the RT. At the heart of the 137

algorithm lies the RT. The RT of a 2-D function f(x, y) is the 138

set of projections along angles θ: 139

R(ρ, θ) =

∫ +∞∫−∞

f(x, y)δ(x cos θ + y sin θ − ρ)dxdy

=

+∞∫−∞

f(ρ cos θ − l sin θ, ρ sin θ + l cos θ)dl

where

[ρl

]=

[cos θ sin θ− sin θ cos θ

][xy

](coordinate rotation) (1)

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CORBANE et al.: TRACTOR WHEEL TRACKS DETECTION, DELINEATION, AND CHARACTERIZATION 3

Fig. 3. Scaled RT corresponding to the agricultural plot of Fig. 1(b).

where δ (x) is the Dirac function, ρ ∈ π(−∞,+∞) and θ ∈140

[0, π]. The RT performs the integration of the image along141

each possible straight line of the image with polar parameters142

(ρ, θ). The RT of an image containing a segment will therefore143

exhibit a prominent peak of coordinates (ρ0, θ0) such that ρ0 =144

x cos θ0 + y sin θ0 is the equation of the straight line along145

which the segment lies [8]. The RT, therefore, contains a peak146

corresponding to every line in the image that is brighter than147

its surroundings and a valley for every dark line. Thus, the148

problem of detecting lines is reduced to detecting the peaks149

and valleys in the transform domain. To differentiate candidate150

peaks from the surrounding clutter, it is necessary to adequately151

threshold the transformed image R(ρ, θ). Therefore, a scaling152

of RT based on the average intensity was applied allowing to153

display only those values greater than the average of R(ρ, θ).154

Fig. 3 illustrates the scaled RT corresponding to a typical155

image of a wheeled plot shown in Fig. 1(b). It reveals the156

presence of several bright spots at the abscissa θ = 0.8 radians.157

These peaks correspond to the parallel rows generated by the158

tractor’s wheels. Two other bright spots can be seen at θ = 1.85159

and one spot at θ = 2.65. They correspond to the left (for160

θ = 1.85), the upper and lower linear borders (for θ = 2.65)161

of the plot.162

C. Estimation of Rows’ Orientation163

It is often the case that for the same date, some agricultural164

plots may be subjected to wheeled traffic while others are165

not. This is reflected in the SAR scene by the presence of166

highly anisotropic wheeled plots and isotropic nonwheeled167

plots. In the presence of an anisotropic plot, the projection168

of the absolute values of ρ along the θ axis (Cumulated ρ:169

ρCum,θ) is characterized by the presence of a prominent peak170

[Fig. 4(a)]. Inversely, in the case of isotropic plots, there is171

a random variation of ρ values [Fig. 4(b)]. The distinction172

between wheeled and nonwheeled plots was implemented using173

an experimentally defined threshold as follows:174

Tanisotropic = max ρCum,θ>(XρCum,θ

+σρCum,θ×5

)(2)

where XρCum,θand σρCum,θ

are, respectively the average and175

the standard deviation values of ρCum,θ.176

For anisotropic plots, the peaks corresponding to the wheel177

tracks observed on the SAR scene have theoretically the same178

Fig. 4. Results of the projection of ρ along the θ axis (ρCum,θ) for(a) a wheeled plot and (b) a nonwheeled plot.

abscissa in the transformed image since they correspond to 179

parallel lines. This abscissa of the projected ρ along the θ axis, 180

with the largest value of ρCum,θ(argmax ρCum,θ) corresponds 181

to the angle of the orientation of the spectrum and thus gives 182

the orientation α of the wheel tracks as follows: 183

α = argmaxρCum,θ(degrees)− 90. (3)

D. Estimation of Inter-Rows’ Width 184

The spectrum of the transformed image R(ρ, θ) allows also 185

the automatic estimation of the inter-rows’ width (w) which can 186

be derived through the projection of θ on the ρ axis (cumulated 187

θ: θcum,ρ). The regularly spaced rows generated by wheel 188

tracks will hence exhibit a series of periodical peaks on the θ 189

profile (Fig. 5). To reduce the effect of undesirable peaks, often 190

related to plot’s boundaries, an interval of 20 pixels centered 191

on the previously determined abscissa was defined. Only those 192

values of θ falling within this interval were hence projected 193

on the ρ axis. To further denoise the resulting θcum,ρ profile, 194

thereby achieving higher precision in the determination of w, a 195

2-D Savitzky–Golay filter [9] was used allowing the smoothing 196

of the peaks while preserving their shapes. Only high amplitude 197

peaks were conserved for the estimation of inter-rows’ width 198

using a threshold value experimentally defined as follows: 199

Tpeak =(max θCum,ρ +XθCum,ρ

)/6 (4)

where XθCum,ρis the average value of θcum,ρ. 200

The remaining peaks Pi (i: 1 → n), are finally used for 201

computing the mean inter-rows’ width 202

w =1

n− 1

n−1∑i=1

(Pi+1 − Pi). (5)

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4 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

Fig. 5. Results of the projection of θ along the ρ axis (θCum,θ) for thewheeled plot shown in Fig. 1(b). A 2-D Savitzky–Golay filter has been appliedto denoise the resulting profile and a threshold (Tpeak) for the conservation ofhigh amplitude peaks.

TABLE IIDIFFERENCES BETWEEN MEASURED AND COMPUTED ORIENTATIONS

AND INTER-ROWS’ WIDTH ON THE FOUR TERRASAR SCENES

IV. RESULTS AND DISCUSSION203

The algorithm was tested for the 85 manually delineated plots204

on the four TerraSAR scenes. The results showed that out of205

the 56 wheeled plots (Table I), 53 were correctly detected as206

anisotropic and only 3 were misclassified as isotropic. From207

the total of 29 nonwheeled plots, 25 were correctly identified208

by the algorithm as isotropic while only 4 were misclassified209

as anisotropic. The overall misclassification rate (around 8%)210

is reasonable suggesting the suitability of the algorithm for211

the identification of wheeled plots. Taking into account only212

the correctly identified plots (78 plots in total), the differences213

between measured and estimated values of angles and inter-214

rows width were calculated in terms of average and standard215

deviation (STD). This assessment results is an approximation,216

because the on-screen measures of rows’ angles and inter-rows’217

widths are not perfect. Table II shows characterization results218

for each of the four TerraSAR scenes.219

From these first results, it is evident that angles and inter-220

rows’ widths calculations are precise for the two scenes of221

30 April 2008 and 28 April 2009, acquired with two differ-222

ent polarizations but with identical incidence angles of 26◦.223

Inversely, for the images acquired on 20 and 27 April 2009224

with an incidence angle of 50◦, lower accuracies were obtained225

especially for the characterization of wheel tracks orientation226

on the image of 27 April 2009. These results suggest a strong227

dependence of the algorithm on the acquisition parameters of228

the TerraSAR scene and more specifically on the incidence229

angle rather than on the polarization. At steep incidence angles,230

the ridges generated by wheeled traffic seem to be more dif-231

ficult to distinguish on the SAR scene compared to shallower232

incidence angles. This is quite evident when examining Fig. 6233

Fig. 6. Same wheeled plot observed on two TerraSAR scenes acquired on27 and 28 April 2009, with incidence angles of (a) 50◦ and (b) 26◦, respectively.

Fig. 7. Comparison of retrieved characteristics and on-screen measurementsfor the manually delineated plots on the TerraSAR images of 30 April 2008 and28 April 2009.

that shows the same plot observed on two images acquired on 234

27 and 28 April 2009 with incidence angles of 50◦ and 26◦, 235

respectively. As a matter of fact, the double-bounce backscat- 236

tering between the tractor tracks and the wheat plants decreases 237

with the increase of incidence angle leading to a poor contrast. 238

This also explains the misclassification of the 3 wheeled plots 239

that were considered as isotropic on the images acquired with 240

incidence angles of 50◦. 241

A careful examination of the results obtained on the images 242

of 30 April 2008 and 28 April 2009 (26◦), when comparing 243

the computed parameters to reference data, shows good charac- 244

terization results for both dates, especially for the computation 245

of rows’ orientation [Fig. 7(a)]. The four outliers observed for 246

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CORBANE et al.: TRACTOR WHEEL TRACKS DETECTION, DELINEATION, AND CHARACTERIZATION 5

Fig. 8. Example of a heterogeneous plot observed on the image of 28 April2009 and incorrectly classified as a wheeled plot by the algorithm.

Fig. 9. Same wheeled plot observed on (a) IKONOS scene acquired on14 March 2009 and (b) the TerraSAR scene acquired on 28 April 2009.

the image of 28 April 2009, correspond to the four nonwheeled247

plots that were falsely detected as wheeled. This misclassi-248

fication concerned mainly heterogeneous plots such as the249

one shown in Fig. 8. This type of plots is usually difficult250

to characterize by the algorithm due to the juxtaposition of251

different patterns. The more heterogeneous is the plot, the252

more likely it is that the algorithm will misidentify wheeled253

and nonwheeled plots. Regarding the characterization of inter-254

rows’ width [Fig. 7(b)], the results were somewhat less accurate255

mainly for the image of 28 April 2009. A number of outliers256

could be observed on Fig. 7(b) suggesting an overestimation of257

inter-rows’ width.258

Some auxiliary tests were performed to assess the potential259

transferability of the algorithm to the detection of wheel tracks260

on VHSR optical data. For that purpose, the 1 m spatial res-261

olution panchromatic band of an IKONOS scene acquired on262

14 March 2009 was used for the test. Once again, 10 manually263

delineated plots with visible tractor wheel tracks and matching264

the ones delineated on the later SAR scenes of April 2009,265

were analyzed. The results showed that 4 plots were detected266

as isotropic by the algorithm while they actually corresponded267

to wheeled plots. A visual comparison of wheel tracks’ patterns268

observed on the IKONOS [Fig. 9(a)] and TerraSAR scenes269

[Fig. 9(b)], of a same agricultural plot, shows clearly less270

pronounced rows on the optical data.271

One explanation may be that in March, when the optical272

data was acquired, the wheat height was almost 15 cm less273

than in the end of April, corresponding to the period of SAR274

images’ acquisition. The higher the vegetation is the better is275

the contrast between wheel tracks and the surrounding wheat276

vegetation cover. The other explanation could be related to the277

better sensitivity of SAR imagery to surface roughness and278

accordingly its better suitability for the detection of tractor279

wheel tracks. Considering only the six correctly classified280

plots, the differences between the measured and the computed281

angles were calculated. The results showed an average of 282

0.56◦(STD = 0.93◦) for the differences between the measured 283

and computed angles, and of 4.2 m (STD = 4.3 meters) be- 284

tween the measured and computed inter-rows’ widths. This 285

demonstrates the potential transferability of the algorithm to the 286

characterization of tractor wheel tracks on optical data. 287

V. CONCLUSION 288

The proposed automatic method has proved its efficiency for 289

tractor wheel tracks detection, delineation and characterization 290

on VHSR SAR imagery acquired with shallow incidence an- 291

gles. The algorithm showed a greater sensitivity to the incidence 292

angle rather than to the polarization of the SAR imagery. It was 293

also demonstrated the potential transferability of the method to 294

the processing of VHSR optical data. The method was initially 295

developed for SAR imagery. Therefore, the experimentally 296

defined thresholds may need to be adjusted to accommodate the 297

particularities of optical data. Some plans to adjust this method 298

to optical imagery are presently under progress. Possible im- 299

provements could be also achieved by combining this approach 300

with directional morphological filters that are suitable for noise 301

removal especially in the case of highly speckled images [10]. 302

The main limit of this method is that it has to be applied on 303

linear row patterns. It is not appropriate to detect curved wheel 304

tracks such as those encountered in certain agricultural areas 305

with significant slopes. However, the RT has already proven 306

its robustness for curve detection [11] suggesting a potential 307

extension of the algorithm to the detection of both linear and 308

curved tractor wheel tracks. 309

REFERENCES 310

[1] D. L. Karlen, D. C. Erbach, T. C. Kaspar, T. S. Colvin, E. C. Berry, and 311D. R. Timmons, “Soil tilth: A review of past perceptions and future 312needs,” Soil Sci. Soc. Amer. J., vol. 54, no. 1, pp. 153–161, 1990. 313

[2] P. Couteron and O. Lejeune, “Periodic spotted patterns in semi-arid vege- 314tation explained by a propagation-inhibition model,” J. Ecology, vol. 89, 315no. 4, pp. 612–628, Aug. 2001. 316

[3] C. Delenne, S. Durrieu, G. Rabatel, M. Deshayes, J. S. Bailly, C. Lelong, 317and P. Couteron, “Textural approaches for vineyard detection and charac- 318terization using very high spatial resolution remote sensing data,” Int. J. 319Remote Sens., vol. 29, no. 4, pp. 1153–1167, Feb. 2008. 320

[4] T. Wassenaar, J.-M. Robbez-Masson, P. Andrieux, and F. Baret, “Vine- 321yard identification and description of spatial crop structure by per-field 322frequency analysis,” Int. J. Remote Sens., vol. 23, no. 17, pp. 3311–3325, 3232002. 324

[5] P. Courmontagne, “An improvement of ship wake detection based on 325the Radon transform,” Signal Process., vol. 85, no. 8, pp. 1634–1654, 326Aug. 2005. 327

[6] V. P. Onana, E. Trouvé, J. P. Rudant, and E. Tonyé, “Extraction of linear 328features in SAR images for geographical maps updating in a tropical forest 329context,” Proc. SPIE, vol. 4173, pp. 111–121, 2000. 330

[7] N. J. Rodenas, N. Mandelert, and R. Garello, “Radon transform for inter- 331nal wave detection and orientation,” presented at the IX European Signal 332Processing Conf. (EUSIPCO), Rhodes, Greece, 1998. 333

[8] E. Magli, G. Olmo, and L. L. Presti, “Pattern recognition by means of the 334Radon transform and the continuous wavelet transform,” Signal Process., 335vol. 73, no. 3, pp. 277–289, Jan. 1999. 336

[9] A. Savitzky and M. J. E. Golay, “Smoothing and differentiation of data 337by simplified least squares procedures,” Anal. Chem., vol. 36, no. 8, 338pp. 1627–1639, Jul. 1964. 339

[10] P. Soille and H. Talbot, “Directional morphological filtering,” IEEE Trans. 340Pattern Anal. Mach. Intell., vol. 23, no. 11, pp. 1313–1329, Nov. 2001. 341

[11] M. van Ginkel, M. A. Kraaijveld, L. J. van Vliet, E. P. Reding, 342P. W. Verbeek, and H. J. Lammers, “Robust curve detection using a Radon 343transform in orientation space,” in Proc. 13th Scand. Conf. Image Anal., 344Halmstad, Sweden, 2003, pp. 125–132. 345

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AUTHOR QUERY

NO QUERY.

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 1

Tractor Wheel Tracks Detection, Delineation, andCharacterization in Very High Spatial

Resolution SAR Imagery

1

2

3

Christina Corbane, Nicolas Baghdadi, and Michaël Clairotte4

Abstract—Compacted tractor wheel tracks have been recog-5nized as key factors controlling runoff and erosion processes in6agricultural landscapes. In this context, the availability of an au-7tomatic tool for wheel tracks detection and characterization would8be very useful. In this letter, an original algorithm based on Radon9Transform is proposed for automatic wheel tracks detection on10Synthetic aperture radar images with a spatial resolution of 1 m.11Compared to on-screen measurements, wheel tracks orientations12and widths were accurately estimated for the images acquired with13shallow incidence angles whereas poorer results were observed for14sharp incidence angles.15

Index Terms—Detection, radon transform, Synthetic Aperture16Radar (SAR), tractor wheel tracks.17

I. INTRODUCTION18

WHEEL TRAFFIC used in certain agricultural manage-19

ment practices such as tillage, planting and harvesting20

operations may affect both soil erodibility and hydrological21

properties due to severe soil compaction [1]. Wheel tracks play22

the role of water guiding channels and may, hence, influence23

the drainage direction and intensity depending, respectively,24

on their orientation with respect to the terrain slope and on25

the spacing between wheels. Knowledge of the orientation and26

width of tractor wheel tracks, at the scale of an agriculture plot,27

can give partial evidence of the magnitude of soil compaction28

and is therefore essential for modeling runoff and erosion29

processes. Because remote sensing is considered as the favorite30

tool to provide spatial and temporal information, it can facil-31

itate the detection and monitoring of wheel tracks over large32

areas, representing a promising alternative to conventional field33

surveying methods. Typically, Very High Spatial Resolution34

(VHSR) Synthetic Aperture Radar (SAR) sensors could be used35

for monitoring wheel tracks at the scale of the agricultural36

plot thanks to their well-known sensitivity to variations of37

soil roughness and surface geometry and to their ability to38

image during all-weather conditions. The issue of tractor wheel39

tracks detection and delineation in VHSR SAR images can40

be described as follows: wheel tracks exhibit periodical linear41

patterns consisting of parallel rows with almost invariant inter-42

Manuscript received March 8, 2011; revised April 22, 2011; acceptedMay 23, 2011.

C. Corbane and M. Clairotte are with the European Commission, JointResearch Center, 21020 Ispra, Italy (e-mail: christina.corban@jrc.ec.europa.eu;michael.clairotte@jrc.ec.europa.eu).

N. Baghdadi is with CEMAGREF, UMR TETIS, 34093 MontpellierCedex 5, France (e-mail: baghdadi@teledetection.fr).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/LGRS.2011.2158184

row widths but with a large range of possible orientations which 43

prevent using classical textural approaches. Due to the periodic 44

organization of wheeled plots into straight lines both frequency 45

analysis (e.g., Fourier Transform) and linear feature detection 46

approaches (e.g., Radon Transform) seem suitable for wheel 47

tracks detection and characterization. Fourier transform (FT) 48

is suited for oriented and periodic texture detection. Its ef- 49

ficiency has been demonstrated to characterize and monitor 50

natural periodic vegetation [2]. Both Delenne et al. [3] and 51

Wassenaar et al. [4] applied FT to extract information on 52

the type of vineyard planting as well as crop spacing and 53

orientation on VHSR optical data. In the current study, SAR 54

imagery known for its inherent noise-like phenomenon is ex- 55

plored, thus limiting the use of FT approaches and privileging 56

a method that relies on linear feature detection such as the 57

Radon transform (RT). The RT integrates values of the pixels 58

along every line while each integral becomes a single point in 59

the transform space; this process of averaging diminishes noise 60

perturbations and hence increases the signal-to-noise ratio of 61

features of interest. For that reason RT has been considered a 62

quite appropriate method for the enhancement and detection of 63

linear patterns on SAR imagery. Several applications in SAR 64

exploited this property like ship wake detection [5], extraction 65

of roads and railways for the update of geographic maps [6] and 66

characterization of internal waves in oceanography [7]. 67

In this letter, an original algorithm based on RT is proposed 68

for the automatic wheel tracks detection and characterization 69

of previously delimited plots on VHSR SAR images. The 70

accuracy and the robustness of the method are analyzed for both 71

wheeled and nonwheeled plots observed at different dates with 72

varying SAR acquisition parameters. 73

II. STUDY SITE AND DATA SET 74

The selected study site is located in the Orgeval watershed, 75

East of Paris (France; Lat. 48◦ 51′ N and Long. 3◦ 07′ E). The 76

soil has a loamy texture and is very flat (slope < 5%). This 77

predominantly agricultural area is composed of wheat and corn- 78

growing plots. From September 2008 to April 2009, the winter 79

wheat covered approximately 50% of the watershed total sur- 80

face. The remaining surface portion corresponded to ploughed 81

soils awaiting future cultivation. The wheat height was about 82

5 cm in January, 10 cm February, 20 cm in March, and 35 cm 83

at the end of April. Following the sowing season (September 84

to October), the tractors are usually operated starting February 85

or March for spreading agricultural fertilizers. In some cases 86

fertilizer application operations take place from March until 87

June with two to five passages of tractors. The repeated passage 88

of wheel traffic over a plot has a compacting action on the soil 89

1545-598X/$26.00 © 2011 IEEE

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2 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

Fig. 1. (a) Illustration of a typical wheeled plot. (b) A wheeled plot observedon a SAR scene acquired on 30 April 2008.

TABLE ITHE EXPERIMENTAL DATA SET CONSISTING OF A TOTAL OF

85 WHEELED (WP) AND NONWHEELED PLOTS (NWP)MANUALLY DELINEATED ON FOUR TERRASAR SCENES

in the wheel track that is observable in the field by the presence90

of parallel rows [Fig. 1(a)].91

For this experiment, four TerraSAR-X images (∼9.65 GHz),92

acquired at low and high incidence angles (26◦ and 50◦) in93

‘Spotlight mode’, in April 2008 and 2009, were analyzed. The94

TerraSAR images were taken with HH and VV polarized beams95

and with a ground pixel spacing of 1 m. The characteristics96

of the SAR scenes are summarized in Table I. For the vali-97

dation of the algorithm a total of 85 manually delimited plots98

were defined on the four SAR scenes. The experimental plots99

were selected in such a way they included both patterns of100

wheeled and nonwheeled plots (Table I). For each delineated101

plot, measurements of the rows’ orientation (α) and inter-102

row’s width (w) have been done by photo-interpretation as103

follows: assessment of the mean angle of all visible rows for104

row orientation and of the mean distance of all visible rows105

for inter-rows’ width [Fig. 1(b)]. The brightest radar returns in106

linear forms at the interior of some agricultural plots are caused107

by the double-bounce scattering between the compacted tractor108

wheel tracks and the wheat plants [Fig. 1(b)].109

III. METHOD110

A. Algorithm Overview111

The method to be developed to detect accurately tractor112

wheel tracks on SAR images described above under all con-113

ditions encountered, needs to fulfill a number of requirements:114

1) it should be able to detect linear structures, 2) it should allow115

the estimation of the rows directions and inter-rows’ width,116

3) it should be able to detect structures without user interaction,117

4) it should be robust to noises.118

To meet these requirements, a three step algorithm centered119

on the RT was developed (Fig. 2): In a first step, RT was120

computed for each pre-delineated agricultural plot allowing121

the detection of the bright lines corresponding to the ridges122

generated by the passage of wheel tracks; in a second step,123

wheeled plots were distinguished from nonwheeled plots and124

the orientation of the previously detected lines was computed;125

Fig. 2. Illustration of the three-step algorithm developed for the automaticdetection and characterization of tractor wheel tracks.

in a final step, inter-rows’ width was estimated though a proper 126

noise filtering, thresholding and peak detection approach. 127

B. Detection of Wheel Tracks’ Rows 128

The developed workflow is based on the assumption that 129

agriculture plots under process are previously delineated using 130

either existing digitized fields boundaries or a proven automated 131

field delineation method. A Gamma speckle filter was first 132

applied on the experimental plots manually delineated in the 133

SAR scenes. This was followed by an edge enhancement using 134

a 3 × 3 Laplacian filter. These two preprocessing operations 135

were meant to improve the visibility of the wheel tracks and 136

to facilitate their detection using the RT. At the heart of the 137

algorithm lies the RT. The RT of a 2-D function f(x, y) is the 138

set of projections along angles θ: 139

R(ρ, θ) =

∫ +∞∫−∞

f(x, y)δ(x cos θ + y sin θ − ρ)dxdy

=

+∞∫−∞

f(ρ cos θ − l sin θ, ρ sin θ + l cos θ)dl

where

[ρl

]=

[cos θ sin θ− sin θ cos θ

][xy

](coordinate rotation) (1)

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CORBANE et al.: TRACTOR WHEEL TRACKS DETECTION, DELINEATION, AND CHARACTERIZATION 3

Fig. 3. Scaled RT corresponding to the agricultural plot of Fig. 1(b).

where δ (x) is the Dirac function, ρ ∈ π(−∞,+∞) and θ ∈140

[0, π]. The RT performs the integration of the image along141

each possible straight line of the image with polar parameters142

(ρ, θ). The RT of an image containing a segment will therefore143

exhibit a prominent peak of coordinates (ρ0, θ0) such that ρ0 =144

x cos θ0 + y sin θ0 is the equation of the straight line along145

which the segment lies [8]. The RT, therefore, contains a peak146

corresponding to every line in the image that is brighter than147

its surroundings and a valley for every dark line. Thus, the148

problem of detecting lines is reduced to detecting the peaks149

and valleys in the transform domain. To differentiate candidate150

peaks from the surrounding clutter, it is necessary to adequately151

threshold the transformed image R(ρ, θ). Therefore, a scaling152

of RT based on the average intensity was applied allowing to153

display only those values greater than the average of R(ρ, θ).154

Fig. 3 illustrates the scaled RT corresponding to a typical155

image of a wheeled plot shown in Fig. 1(b). It reveals the156

presence of several bright spots at the abscissa θ = 0.8 radians.157

These peaks correspond to the parallel rows generated by the158

tractor’s wheels. Two other bright spots can be seen at θ = 1.85159

and one spot at θ = 2.65. They correspond to the left (for160

θ = 1.85), the upper and lower linear borders (for θ = 2.65)161

of the plot.162

C. Estimation of Rows’ Orientation163

It is often the case that for the same date, some agricultural164

plots may be subjected to wheeled traffic while others are165

not. This is reflected in the SAR scene by the presence of166

highly anisotropic wheeled plots and isotropic nonwheeled167

plots. In the presence of an anisotropic plot, the projection168

of the absolute values of ρ along the θ axis (Cumulated ρ:169

ρCum,θ) is characterized by the presence of a prominent peak170

[Fig. 4(a)]. Inversely, in the case of isotropic plots, there is171

a random variation of ρ values [Fig. 4(b)]. The distinction172

between wheeled and nonwheeled plots was implemented using173

an experimentally defined threshold as follows:174

Tanisotropic = max ρCum,θ>(XρCum,θ

+σρCum,θ×5

)(2)

where XρCum,θand σρCum,θ

are, respectively the average and175

the standard deviation values of ρCum,θ.176

For anisotropic plots, the peaks corresponding to the wheel177

tracks observed on the SAR scene have theoretically the same178

Fig. 4. Results of the projection of ρ along the θ axis (ρCum,θ) for(a) a wheeled plot and (b) a nonwheeled plot.

abscissa in the transformed image since they correspond to 179

parallel lines. This abscissa of the projected ρ along the θ axis, 180

with the largest value of ρCum,θ(argmax ρCum,θ) corresponds 181

to the angle of the orientation of the spectrum and thus gives 182

the orientation α of the wheel tracks as follows: 183

α = argmaxρCum,θ(degrees)− 90. (3)

D. Estimation of Inter-Rows’ Width 184

The spectrum of the transformed image R(ρ, θ) allows also 185

the automatic estimation of the inter-rows’ width (w) which can 186

be derived through the projection of θ on the ρ axis (cumulated 187

θ: θcum,ρ). The regularly spaced rows generated by wheel 188

tracks will hence exhibit a series of periodical peaks on the θ 189

profile (Fig. 5). To reduce the effect of undesirable peaks, often 190

related to plot’s boundaries, an interval of 20 pixels centered 191

on the previously determined abscissa was defined. Only those 192

values of θ falling within this interval were hence projected 193

on the ρ axis. To further denoise the resulting θcum,ρ profile, 194

thereby achieving higher precision in the determination of w, a 195

2-D Savitzky–Golay filter [9] was used allowing the smoothing 196

of the peaks while preserving their shapes. Only high amplitude 197

peaks were conserved for the estimation of inter-rows’ width 198

using a threshold value experimentally defined as follows: 199

Tpeak =(max θCum,ρ +XθCum,ρ

)/6 (4)

where XθCum,ρis the average value of θcum,ρ. 200

The remaining peaks Pi (i: 1 → n), are finally used for 201

computing the mean inter-rows’ width 202

w =1

n− 1

n−1∑i=1

(Pi+1 − Pi). (5)

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Fig. 5. Results of the projection of θ along the ρ axis (θCum,θ) for thewheeled plot shown in Fig. 1(b). A 2-D Savitzky–Golay filter has been appliedto denoise the resulting profile and a threshold (Tpeak) for the conservation ofhigh amplitude peaks.

TABLE IIDIFFERENCES BETWEEN MEASURED AND COMPUTED ORIENTATIONS

AND INTER-ROWS’ WIDTH ON THE FOUR TERRASAR SCENES

IV. RESULTS AND DISCUSSION203

The algorithm was tested for the 85 manually delineated plots204

on the four TerraSAR scenes. The results showed that out of205

the 56 wheeled plots (Table I), 53 were correctly detected as206

anisotropic and only 3 were misclassified as isotropic. From207

the total of 29 nonwheeled plots, 25 were correctly identified208

by the algorithm as isotropic while only 4 were misclassified209

as anisotropic. The overall misclassification rate (around 8%)210

is reasonable suggesting the suitability of the algorithm for211

the identification of wheeled plots. Taking into account only212

the correctly identified plots (78 plots in total), the differences213

between measured and estimated values of angles and inter-214

rows width were calculated in terms of average and standard215

deviation (STD). This assessment results is an approximation,216

because the on-screen measures of rows’ angles and inter-rows’217

widths are not perfect. Table II shows characterization results218

for each of the four TerraSAR scenes.219

From these first results, it is evident that angles and inter-220

rows’ widths calculations are precise for the two scenes of221

30 April 2008 and 28 April 2009, acquired with two differ-222

ent polarizations but with identical incidence angles of 26◦.223

Inversely, for the images acquired on 20 and 27 April 2009224

with an incidence angle of 50◦, lower accuracies were obtained225

especially for the characterization of wheel tracks orientation226

on the image of 27 April 2009. These results suggest a strong227

dependence of the algorithm on the acquisition parameters of228

the TerraSAR scene and more specifically on the incidence229

angle rather than on the polarization. At steep incidence angles,230

the ridges generated by wheeled traffic seem to be more dif-231

ficult to distinguish on the SAR scene compared to shallower232

incidence angles. This is quite evident when examining Fig. 6233

Fig. 6. Same wheeled plot observed on two TerraSAR scenes acquired on27 and 28 April 2009, with incidence angles of (a) 50◦ and (b) 26◦, respectively.

Fig. 7. Comparison of retrieved characteristics and on-screen measurementsfor the manually delineated plots on the TerraSAR images of 30 April 2008 and28 April 2009.

that shows the same plot observed on two images acquired on 234

27 and 28 April 2009 with incidence angles of 50◦ and 26◦, 235

respectively. As a matter of fact, the double-bounce backscat- 236

tering between the tractor tracks and the wheat plants decreases 237

with the increase of incidence angle leading to a poor contrast. 238

This also explains the misclassification of the 3 wheeled plots 239

that were considered as isotropic on the images acquired with 240

incidence angles of 50◦. 241

A careful examination of the results obtained on the images 242

of 30 April 2008 and 28 April 2009 (26◦), when comparing 243

the computed parameters to reference data, shows good charac- 244

terization results for both dates, especially for the computation 245

of rows’ orientation [Fig. 7(a)]. The four outliers observed for 246

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CORBANE et al.: TRACTOR WHEEL TRACKS DETECTION, DELINEATION, AND CHARACTERIZATION 5

Fig. 8. Example of a heterogeneous plot observed on the image of 28 April2009 and incorrectly classified as a wheeled plot by the algorithm.

Fig. 9. Same wheeled plot observed on (a) IKONOS scene acquired on14 March 2009 and (b) the TerraSAR scene acquired on 28 April 2009.

the image of 28 April 2009, correspond to the four nonwheeled247

plots that were falsely detected as wheeled. This misclassi-248

fication concerned mainly heterogeneous plots such as the249

one shown in Fig. 8. This type of plots is usually difficult250

to characterize by the algorithm due to the juxtaposition of251

different patterns. The more heterogeneous is the plot, the252

more likely it is that the algorithm will misidentify wheeled253

and nonwheeled plots. Regarding the characterization of inter-254

rows’ width [Fig. 7(b)], the results were somewhat less accurate255

mainly for the image of 28 April 2009. A number of outliers256

could be observed on Fig. 7(b) suggesting an overestimation of257

inter-rows’ width.258

Some auxiliary tests were performed to assess the potential259

transferability of the algorithm to the detection of wheel tracks260

on VHSR optical data. For that purpose, the 1 m spatial res-261

olution panchromatic band of an IKONOS scene acquired on262

14 March 2009 was used for the test. Once again, 10 manually263

delineated plots with visible tractor wheel tracks and matching264

the ones delineated on the later SAR scenes of April 2009,265

were analyzed. The results showed that 4 plots were detected266

as isotropic by the algorithm while they actually corresponded267

to wheeled plots. A visual comparison of wheel tracks’ patterns268

observed on the IKONOS [Fig. 9(a)] and TerraSAR scenes269

[Fig. 9(b)], of a same agricultural plot, shows clearly less270

pronounced rows on the optical data.271

One explanation may be that in March, when the optical272

data was acquired, the wheat height was almost 15 cm less273

than in the end of April, corresponding to the period of SAR274

images’ acquisition. The higher the vegetation is the better is275

the contrast between wheel tracks and the surrounding wheat276

vegetation cover. The other explanation could be related to the277

better sensitivity of SAR imagery to surface roughness and278

accordingly its better suitability for the detection of tractor279

wheel tracks. Considering only the six correctly classified280

plots, the differences between the measured and the computed281

angles were calculated. The results showed an average of 282

0.56◦(STD = 0.93◦) for the differences between the measured 283

and computed angles, and of 4.2 m (STD = 4.3 meters) be- 284

tween the measured and computed inter-rows’ widths. This 285

demonstrates the potential transferability of the algorithm to the 286

characterization of tractor wheel tracks on optical data. 287

V. CONCLUSION 288

The proposed automatic method has proved its efficiency for 289

tractor wheel tracks detection, delineation and characterization 290

on VHSR SAR imagery acquired with shallow incidence an- 291

gles. The algorithm showed a greater sensitivity to the incidence 292

angle rather than to the polarization of the SAR imagery. It was 293

also demonstrated the potential transferability of the method to 294

the processing of VHSR optical data. The method was initially 295

developed for SAR imagery. Therefore, the experimentally 296

defined thresholds may need to be adjusted to accommodate the 297

particularities of optical data. Some plans to adjust this method 298

to optical imagery are presently under progress. Possible im- 299

provements could be also achieved by combining this approach 300

with directional morphological filters that are suitable for noise 301

removal especially in the case of highly speckled images [10]. 302

The main limit of this method is that it has to be applied on 303

linear row patterns. It is not appropriate to detect curved wheel 304

tracks such as those encountered in certain agricultural areas 305

with significant slopes. However, the RT has already proven 306

its robustness for curve detection [11] suggesting a potential 307

extension of the algorithm to the detection of both linear and 308

curved tractor wheel tracks. 309

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[10] P. Soille and H. Talbot, “Directional morphological filtering,” IEEE Trans. 340Pattern Anal. Mach. Intell., vol. 23, no. 11, pp. 1313–1329, Nov. 2001. 341

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