ahierarchicalmarkovrandomfieldforroadnetworkextractionanditsapplicationwithopticalandsardata.pdf
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Introduction Methodology Results Conclusions
A hierarchical Markov random fieldfor road network extraction and its
application with optical and sar data
Talita Perciano1,2 Roberto Hirata Jr.1 Roberto M. C. Jr.1
Florence Tupin2
1Departamento de Computacao
Instituto de Matematica e Estatıstica
Universidade de Sao Paulo
2Departement Traitement du Signal et des Images
Telecom ParisTech
IGARSS 2011
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 1 / 25
Introduction Methodology Results Conclusions
Contents
1 Introduction
2 Methodology
3 Results
4 Conclusions
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 2 / 25
Introduction Methodology Results Conclusions
Motivations and objective
• Advent of new optical (QuickBird, Pleiades) and radar(TerraSAR-X, Cosmo-Skymed) high-resolution satellite sensors
• New perspectives for pattern recognition problems as roadnetwork extraction
• The number of works in the literature exploring high-resolutionimages and multi-sensor image processing is increasing
Objective
Propose a flexible hierarchical Markovian random field based onfeature extraction and road network structure, exploringmulti-sensor data fusion
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 3 / 25
Introduction Methodology Results Conclusions
Problem of road network extraction
• Problem studied since many years as it is an importantstructure for many applications:
• Urban planning• Making and updating maps• Traffic management• Cartography
• Difficult task due to the spatial and spectral features of theroad
• Different automatic and semiautomatic approaches in theliterature
• A two-step approach is explored in this work:
1 Low level: features extraction2 High level: road network reconstruction using contextual
information
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 4 / 25
Introduction Methodology Results Conclusions
Contents
1 Introduction
2 Methodology
3 Results
4 Conclusions
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 5 / 25
Introduction Methodology Results Conclusions
Overview of the method (Tupin et al, 1998 )
1 Extract linear features• Ratio-based detector (D1(x , y)) and a cross-correlation-based
detector (D2(x , y))
D(x , y) =D1(x , y)D2(x , y)
1− D1(x , y)− D2(x , y) + 2D1(x , y)D2(x , y). (1)
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Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 6 / 25
Introduction Methodology Results Conclusions
Overview of the method
2 Road network reconstruction
• Graph modeling: map structures and its relations into a graphwhere each segment is a node and two nodes are connected iftheir corresponding segments share a extremity
• Markovian model: search for the optimal binary labeling byminimizing an energy function defined for the MRF that has adata attachment term (likelihood) and a prior term:
U(l) = Ulikelihood(l , d) + Uprior (l) (2)
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 7 / 25
Introduction Methodology Results Conclusions
Overview of the method
2 Road network reconstruction
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Line detection Polygonal approximationand connections
Graph
Final road networkExample of labeling
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Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 8 / 25
Introduction Methodology Results Conclusions
Features extractionProposed radar and optical fusion (Novelty)
• The ratio and cross-correlation measures are calculatedsimultaneously in the radar and optical images
• The maximum response for each measure is retained• The symmetrical sum is used as before:
D(x , y) =D1(x , y)D2(x , y)
1−D1(x , y)− D2(x , y) + 2D1(x , y)D2(x , y). (3)
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(a) Radar
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(b) Optical
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 9 / 25
Introduction Methodology Results Conclusions
Connected component level (Novelty)Proposed method
• Road network reconstruction: the use of connectedcomponents instead of segments
Detect componentsand make connections
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Line detection Graph
Example of labelingFinal road network
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Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 10 / 25
Introduction Methodology Results Conclusions
Connected component level (Novelty)Proposed method
• Advantages of using connected components! Simplification of the graph by decreasing considerably itsnumber of nodes! Deal with more complex structures! Take more advantage of the complete structures detected inthe low level
• Process applied in a multi-scale way• A pyramid is created by degrading the resolution (average of
the amplitudes of n × n pixels blocks)• Extraction of the roads in the three scales• Results of each scale are merged together• “Cleaning step” to remove possible redundancies
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 11 / 25
Introduction Methodology Results Conclusions
Road section level (Novelty)Additional high-level step
Built a new graph from the result of the previous road extraction:
• Image is preprocessed to obtain only crossroads and roadsections
• Each road section is a node of the graph and two nodes areconnected is their corresponding sections share a crossroad
• MRF model with the same kind of energy function, but thebest likelihood value is obtained analyzing all three scales ofthe multi-scale pyramid and from both radar and opticalimages
• Simpler and computationally faster step
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 12 / 25
Introduction Methodology Results Conclusions
Road section level (Novelty)Additional high-level step
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Result from previousMRF
Prolongations
Graph
Example of labeling
Final road network
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Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 13 / 25
Introduction Methodology Results Conclusions
Contents
1 Introduction
2 Methodology
3 Results
4 Conclusions
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 14 / 25
Introduction Methodology Results Conclusions
Results - QuickBird and TerraSAR-X images
(Toulouse)
(a) Optical image (b) Radar image (c) Ground-truth
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 15 / 25
Introduction Methodology Results Conclusions
Results - QuickBird image
(a) Ground-truth (b) Optical result (c) Optical result
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 16 / 25
Introduction Methodology Results Conclusions
Results - TerraSAR-X image
(a) Ground-truth (b) Radar result (c) Radar result
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 17 / 25
Introduction Methodology Results Conclusions
Results - Fusion
(a) Ground-truth (b) Fusion result (c) Fusion result
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 18 / 25
Introduction Methodology Results Conclusions
Results - QuickBird and TerraSAR-X images
(Toulouse)
(a) Optical image result (b) Radar image result (c) Fusion result
Figure: Correct detection in red, incorrect detection in black andabsent roads in blue.
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 19 / 25
Introduction Methodology Results Conclusions
Results - QuickBird and TerraSAR-X images
(Toulouse)
(a) Optical image (b) Radar image (c) Ground-truth
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 20 / 25
Introduction Methodology Results Conclusions
Results - QuickBird and TerraSAR-X images
(Toulouse)
(a) Ground-truth (b) Fusion result (c) Fusion result
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 21 / 25
Introduction Methodology Results Conclusions
Results
Table: Quantitative evaluation of the results.
Data Completeness Correctness Quality
R1 Optical image 41.44% 38.63% 24.99%
Radar image 44.42% 50.82% 31.06%
Fusion 67.39% 56.55% 44.40%
R2 Optical image 45.21% 45.72% 31.06%
Radar image 53.16% 43.77% 31.59%
Fusion 62.69% 57.4% 42.78%
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 22 / 25
Introduction Methodology Results Conclusions
Contents
1 Introduction
2 Methodology
3 Results
4 Conclusions
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 23 / 25
Introduction Methodology Results Conclusions
Discussion and conclusions
• We propose a new framework for road detection composed bythree steps:
• Low-level step (line detection with fusion of optical and radardata)
• First high-level step (connected components)• Second high-level step (road sections and crossroads)
• A hierarchical multi-scale framework that uses informationfrom different sources (radar and optical images)
• The quantitative results show the considerable improvementof detection using the fusion approach
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 24 / 25
Introduction Methodology Results Conclusions
Acknowledgements
Thanks to FAPESP, CAPES (scholarship process number0310-10-7) and CNPq Brazilian agencies for funding.
Contact: talitaperciano@gmail.com
Questions?
Talita Perciano Road extraction and data fusion IGARSS 2011 26/07/2011 25 / 25
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