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ROAD EXTRACTION IN URBAN AND RURAL ENVIRONMENTS  EXPLOITING A DUAL-BAND SAR SYSTEM   P. Gamba (1) , G. Lisini (2) , D. Luebeck (3) (1)  Università degli Studi di Pavia (2)  IUSS, Pavia (3)  Orbisat, Sao Jose dos Campos
[object Object],[object Object],[object Object],[object Object],[object Object],Outline
Common problems of road extraction Road networks automatically extracted from remotely sensed data are often incomplete ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Does radar frequency matter? Green arrows point to roads in P-band data that are not visible in X-band data.   P-band X-band
Dual-band SAR may  help  ... Dual-band SAR systems are well suited for vegetation analysis   X-band  is more prone to be scattered by trees and foliage P-band , due to its longer wavelength, is able to pass through vegetation and to better detect underlying roads and other man-made or natural  objects
[object Object],Orbisat system
The general idea …  A fusion methodologies able to exploit road extraction from an airborne dual-band SAR acquisition  … Multi-frequency SAR data Road network fusion … N th  band road extraction   1 st  band road extraction 2 nd  band road extraction
Dual-band SAR data analysis
SAR road extraction methodology Road detection Road extraction Road network MRF optimization Network regularization HR SAR Final road network Multi-scale feature fusion Junction-aware  MRF model Perceptual grouping M. Negri, P. Gamba, G. Lisini, F. Tupin, “ Junction-Aware Extraction and Regularization of Urban Road Networks in High Resolution SAR Images ” , IEEE Trans. on Geoscience and Remote Sensing,   vol. 44, n. 10, pp. 2962-2971, Oct. 2006.
Road detection ,[object Object],[object Object],[object Object],Road detection Road extraction HR SAR Multi-scale feature fusion
Multiple feature extraction ,[object Object],[object Object],[object Object]
Road candidate area extraction  Remote Sensing Group “ min radiance” thresholded “ min radiance” output
Binarization? ,[object Object]
Road candidate extraction ,[object Object],Road detection Road extraction HR SAR Multi-scale feature fusion
Tracking process
Road candidate extraction Final segments The approach is efficient, with the only drawback of reducing curvilinear roads to chains of linear segments.
Perceptual grouping step The procedure is based on  Perceptual Grouping Concepts  and allows connecting segments where reasonable, based on their mutual positions   1° 2° 3° 4° 5° 6°
Network optimization   Some extracted segments are not connected along the entire path Markovian approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],3 4 5 2 1 7 6 segment extreme found segment added segment
Experimental Results Remote Sensing Group The area around the town of Paragominas (state of Parà, Brazil).   ,[object Object],[object Object],[object Object],[object Object],First test area Second  test area
First test site results   Remote Sensing Group Original P-band image   P-band extraction   Original X-band image   X-band extraction
First test site results Remote Sensing Group final results after the fusion step (option 1b)   final results after the fusion step (option 1a)   final results after the fusion step (option 1c)
Quantitative evaluation Main outcomes: + due to the presence of vegetation, P-band results are better  than X-band ones ; + the fusion of both extractions increase in the completeness    and correctness index values; + the real improvement in the road extraction results is for the    roads outside the human settlement . X-band extraction results   P-band extraction results   Fusion results (1a) Fusion results (1b) Fusion results (1c) Completeness 0.16 0.67 0.73 0.73 0.64 Correctness 0.78 0.79 0.74 0.78 0.93 Quality 0.15 0.56 0.60 0.61 0.54 Redundancy -0.07 -0.03 0.06 0.06 0.03
Second test site results: Paragominas P-band image   X-band image
Best result for Paragominas Final results after the fusion step  (option 1b)   Quantitative indexes for the road extraction results in the Paragominas urban area test site   P-band extraction results   Fusion  (option 1a) Fusion  (option 1b) Fusion  (option 1c) Completeness 0.64 0.92 0.92 0.84 Correctness 0.55 0.41 0.45 0.95 Quality 0.42 0.41 0.43 0.63 Redundancy -0.02 0.05 0.05 0.03
Conclusions ,[object Object],[object Object],[object Object]

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4_Paragominas.ppt

  • 1. ROAD EXTRACTION IN URBAN AND RURAL ENVIRONMENTS EXPLOITING A DUAL-BAND SAR SYSTEM P. Gamba (1) , G. Lisini (2) , D. Luebeck (3) (1) Università degli Studi di Pavia (2) IUSS, Pavia (3) Orbisat, Sao Jose dos Campos
  • 2.
  • 3.
  • 4. Does radar frequency matter? Green arrows point to roads in P-band data that are not visible in X-band data. P-band X-band
  • 5. Dual-band SAR may help ... Dual-band SAR systems are well suited for vegetation analysis X-band is more prone to be scattered by trees and foliage P-band , due to its longer wavelength, is able to pass through vegetation and to better detect underlying roads and other man-made or natural objects
  • 6.
  • 7. The general idea … A fusion methodologies able to exploit road extraction from an airborne dual-band SAR acquisition … Multi-frequency SAR data Road network fusion … N th band road extraction 1 st band road extraction 2 nd band road extraction
  • 9. SAR road extraction methodology Road detection Road extraction Road network MRF optimization Network regularization HR SAR Final road network Multi-scale feature fusion Junction-aware MRF model Perceptual grouping M. Negri, P. Gamba, G. Lisini, F. Tupin, “ Junction-Aware Extraction and Regularization of Urban Road Networks in High Resolution SAR Images ” , IEEE Trans. on Geoscience and Remote Sensing, vol. 44, n. 10, pp. 2962-2971, Oct. 2006.
  • 10.
  • 11.
  • 12. Road candidate area extraction Remote Sensing Group “ min radiance” thresholded “ min radiance” output
  • 13.
  • 14.
  • 16. Road candidate extraction Final segments The approach is efficient, with the only drawback of reducing curvilinear roads to chains of linear segments.
  • 17. Perceptual grouping step The procedure is based on Perceptual Grouping Concepts and allows connecting segments where reasonable, based on their mutual positions 1° 2° 3° 4° 5° 6°
  • 18.
  • 19.
  • 20. First test site results Remote Sensing Group Original P-band image P-band extraction Original X-band image X-band extraction
  • 21. First test site results Remote Sensing Group final results after the fusion step (option 1b) final results after the fusion step (option 1a) final results after the fusion step (option 1c)
  • 22. Quantitative evaluation Main outcomes: + due to the presence of vegetation, P-band results are better than X-band ones ; + the fusion of both extractions increase in the completeness and correctness index values; + the real improvement in the road extraction results is for the roads outside the human settlement . X-band extraction results P-band extraction results Fusion results (1a) Fusion results (1b) Fusion results (1c) Completeness 0.16 0.67 0.73 0.73 0.64 Correctness 0.78 0.79 0.74 0.78 0.93 Quality 0.15 0.56 0.60 0.61 0.54 Redundancy -0.07 -0.03 0.06 0.06 0.03
  • 23. Second test site results: Paragominas P-band image X-band image
  • 24. Best result for Paragominas Final results after the fusion step (option 1b) Quantitative indexes for the road extraction results in the Paragominas urban area test site P-band extraction results Fusion (option 1a) Fusion (option 1b) Fusion (option 1c) Completeness 0.64 0.92 0.92 0.84 Correctness 0.55 0.41 0.45 0.95 Quality 0.42 0.41 0.43 0.63 Redundancy -0.02 0.05 0.05 0.03
  • 25.