Polarimetry-based land cover classification with Sentinel...
Transcript of Polarimetry-based land cover classification with Sentinel...
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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!