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Banqué, Xavier (1); Lopez-Sanchez, Juan M (2); Monells, Daniel (1); Ballester, David (2); Duro, Javier (1); Koudogbo, Fifame (1)

1. Altamira-Information S.L. , Barcelona, Spain

2. Universidad de Alicante, Alicante, Spain

PolInSAR 2015

Polarimetry-based land cover classification with Sentinel-1 data

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OUTLINE

• Introduction

• Sentinel-1 data classification potential

• Methodology

• Land cover classifiers

• Results

• Conclusions

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INTRODUCTION

• OBJECTIVE: the goal of the presented research work is to assess the capability

of Sentinel-1 data for land-cover classification • Sentinel-1 coherent dual polarimetric acquisitions present the possibility of

exploiting polarimetric features for classification • Sentinel-1 mission short revisit time (6 days) together with classification capabilities

would foster a myriad of EO applications (Land Use, Emergency management,… ) • Sentinel-1 good trade-off between spatial resolution and swath allows better terrain

coverage than previous spaceborne SAR sensors. • The free, full and open data policy adopted for the Copernicus programme

foresees access available to all users for the Sentinel data products. This will result in bigger impact of EO applications into society.

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Area of interest is the rural zone in south-west Ansbach, Germany. It contains several land cover types including agricultural fields, forest and water bodies (e.g. Altmühlsee lake). The date of data acquisition is 15/11/2014.

SENTINEL-1 DATA CLASSIFICATION POTENTIAL

Google Earth snapshot of the AOI, Ansbach, Germany

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S1 SLC RGB composite suggests the capacity of identifying among different land cover types

S1 SLC RGB composite (R: VV, G: VH, B: VH/VV )

SENTINEL-1 DATA CLASSIFICATION POTENTIAL

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SENTINEL-1 DATA CLASSIFICATION POTENTIAL

SAR data used and location Altmühlsee, Weißenburg-Gunzenhausen, Germany (15/11/2014) S1 IW Single Look Complex data:

PARAMETER VALUE Coordinate System Slant Range

Pixel Value Complex

Bits Per Pixel 16 I and 16 Q

Polarization Dual (VV+VH)

Ground Range Coverage [km] 251.8

Slant Range Resolution [m] 2.7 m (IW1)

Azimuth Resolution [m] 21.7 m

Slant Range Pixel Spacing [m] 2,3 m

Azimuth Pixel Spacing [m] 17,4 m

Incidence angle [°] 32.9

NESZ [dB] < -23.7 dB

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METHODOLOGY

1. Debursting : data handling so that azimuth time is continuous in the SLC to be

processed (w.r.t. original SLC product)

2. Calibration: data has been calibrated with L1 Calibration Annotation Data Set according to Sentinel-1 Product Specification document in order to obtain: • σ0,VV

• σ0,VH

S1 bursted Image example

S1 debursted Image example

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METHODOLOGY

3. De-speckle filtering: Several alternatives were evaluated

• Boxcar • Adaptive: Refined Lee PolSAR Speckle Filter • NL-means (Deledalle et al.)

4. Classification: Several approaches have been tested:

• Supervised Classifiers: Maximum Likelihood, Minimum Distance • Supervised Wishart • Model Based Classifier

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LAND COVER CLASSIFIER

Supervised approach Five main land cover classes are targeted to be classified: • Water (inland) • Bare Soil • Forest • Urban/Man made • Crops

ROIs are chosen for training purposes in supervised classifiers as well as for a polarimetric features evaluation to build up the model based classifier

Span with the chosen training classes

Google Earth snapshot with the identified classes

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LAND COVER CLASSIFIER

Supervised Classifiers: Minimum Distance & Maximum likelihood They show similar performance, though Minimum Distance seems to be slightly better

Minimum Distance classification result RGB composite

99,97 0,00 0,00 0,00 0,03

0,00 99,61 0,29 0,03 0,07

0,00 3,89 84,72 11,39 0,00

0,00 19,06 19,56 61,15 0,22

0,31 0,18 0,00 0,00 99,51

It seems that crops are underestimated. Accuracy and confusion matrix (diagonal order is: water, forest, crops, urban, bare soil)

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LAND COVER CLASSIFIER

Supervised Classifiers: Wishart

Wishart classification result RGB composite

99,87 0,00 0,00 0,00 0,08

0,00 97,66 0,31 4,93 0,07

0,00 2,34 97,44 33,49 0,00

0,00 0,00 2,25 61,09 0,00

0,13 0,00 0,00 0,49 99,86

Clearly better performance than minimum distance. It seems some agricultural fields are misclassified as urban Accuracy and confusion matrix (diagonal order is: water, forest, crops, urban, bare soil)

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LAND COVER CLASSIFIER

Model based classifier: Based on the different polarimetric features of the chosen classes. Polarimetric features evaluated:

• σ0,VV

• σ0,VV

• Ratio • Span

• Alpha1 • Entropy • Normalized

correlation

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LAND COVER CLASSIFIER

Water Both VV and VH are expected to be very low due to water low backscattering. Bare Soil Both VV and VH are expected to be low due to general low backscattering, just above water backscattering power. By using these two information channels seems feasible to identify between this class and the rest Forest Volumetric backscattering mechanism suggests that VH will have more relevance than for the remaining classes with respect to the total span. Moreover, entropy is expected to be high due to the clear presence of more than one backscattering mechanism in the scatterers, and total span has to be moderate. Urban Urban presents in general high reflectivity values in both channels, but with more heterogeneity, and not presenting reflection symmetry. The polarimetric coherence is expected to be high. In addition, Alpha1 has been observed to be higher than in the case of crops. Crops This class consists of agricultural fields with the main characteristic of presenting a high VV backscatter and much lower cross-polar channel, which involves a very low Alpha1 value. Crops present low coherence and very low Alpha1 angle with respect to urban.

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σ0,VV < wt_copol σ0,VH < wt_crosspol

SPAN < wt_span

σ0,VV < bs_copol σ0,VH < bs_crosspol

SPAN < bs_span

SPAN < frt_span Ratio < frt_ratio

Entropy > frt_entropy

Coher > urb_coh α1 > urb_alpha

WATER

BARE SOIL

FOREST

URBAN

CROPS

LAND COVER CLASSIFIER

Upon analysis this decision tree is

proposed for class differentiation so that probability of correct detection is maximized.

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RESULTS

Model based classifier results are compared with minimum distance and Wishart for a small test area with presence of all the defined land covers.

RGB composite patch for the classification performance comparison

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RESULTS

Results Comparison: Minimum Distance vs Model based (confusion matrix)

Minimum Distance classification result Model based classification result

99,97 0,00 0,00 0,00 0,03

0,00 99,61 0,29 0,03 0,07

0,00 3,89 84,72 11,39 0,00

0,00 19,06 19,56 61,15 0,22

0,31 0,18 0,00 0,00 99,51

99,83 0,00 0,00 0,00 0,17

0,00 99,03 0,91 0,05 0,00

0,00 3,39 96,50 0,11 0,00

0,00 20,99 55,83 23,04 0,14

0,78 0,61 0,08 0,00 98,53

water

forest

crops

urban

bare soil

water

forest

crops

urban

bare soil

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RESULTS

Results Comparison: Wishart vs Model Based (confusion matrix)

Wishart classification result Model based classification result

99,87 0,00 0,00 0,00 0,08

0,00 97,66 0,31 4,93 0,07

0,00 2,34 97,44 33,49 0,00

0,00 0,00 2,25 61,09 0,00

0,13 0,00 0,00 0,49 99,86

99,83 0,00 0,00 0,00 0,17

0,00 99,03 0,91 0,05 0,00

0,00 3,39 96,50 0,11 0,00

0,00 20,99 55,83 23,04 0,14

0,78 0,61 0,08 0,00 98,53

water

forest

crops

urban

bare soil

water

forest

crops

urban

bare soil

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RESULTS

Overall Result: Model based

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RESULTS

Overall Result: Model based

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CONCLUSIONS AND NEXT STEPS

CONCLUSIONS • Sentinel-1 SLC data present good potential for land cover classification

• Preliminary obtained result present a good classification performance (over ~95% for all the

classes except urban, which is the real challenge of the classifier)

• Different evaluated classifiers present similar results

• Model based classifier has the potential to become unsupervised w.r.t. the other classifiers evaluated

NEXT STEPS • Evaluate over an area with a good land-cover map (ground truth for performance evaluation)

• Evaluate the influence of the incidence angle in the classification performance

• Evaluate the impact of meteorological conditions during the data acquisition in the

classification results

• Potential for crop type mapping evaluation.

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Thanks for your attention!