ACIVS 2013, October 29, 2013 Poznan, Poland

Fast road network extraction from remotely sensed images Vladimir A. Krylov, James D. B. Nelson Dept. Statistical Science, University College London, UK

Talk outline • Line detection: – – – –

Challenges and state of the art; Application of MCMC; Mammographic image analysis; Road network extraction.

• Detecting and counting objects: – –

Marked point processes; Flamingoes, buildings, etc.

• Conclusions

Fast road network extraction from remotely sensed images by V. Krylov, J. Nelsen. 29 October 2013

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Road extraction: challenges Most urban network images contain – Noise (acquisition); – Geometrical noise (buildings, …) – Occlusions (shadows, angle view); – Curvature; – Varying scales.

In this work we address road extraction as a line detection problem, relying on the elongatedness of the roads. Fast road network extraction from remotely sensed images by V. Krylov, J. Nelsen. 29 October 2013

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Line detection state of the art •

First guess: matched filtering –



Line template matching + accuracy, robustness, - scale choice.

Edge detector, e.g., Canny filter based on the first derivative of a Gaussian + good performance for simple lines, - Missed detection in complex scenes.

Fast road network extraction from remotely sensed images by V. Krylov, J. Nelsen. 29 October 2013

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Line detection state of the art •

Radon transform –

Gives an integral of the function along a straight line

+ fast implementation, - poor curvature-tolerance, - preferential treatment for long lines. •

Hough transform Problem with shorter lines.

Fast road network extraction from remotely sensed images by V. Krylov, J. Nelsen. 29 October 2013

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Line detection state of the art • Local wavelet-like techniques: –

Beamlets Hierarchical dyadic decomposition Adaptive scale Stopping condition



Contourlets / Curvelets, etc. Fast road network extraction from remotely sensed images by V. Krylov, J. Nelsen. 29 October 2013

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Proposed line structure detector •

Preselected scale: + Approximation of curves with lines; + Highly sensitive detection via matching; - Manual scale selection; - Possible losses due to occlusions.



Assumptions on lines of interest: – – –

Local contrast; Low curvature; Elongatedness.

Fast road network extraction from remotely sensed images by V. Krylov, J. Nelsen. 29 October 2013

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Proposed line structure detector • Assumptions on lines of interest: – – –

Local contrast; Low curvature; Elongatedness.

• Two stage curvilinear structure detection: I. Short line extraction • Matching via Radon maxima extraction • Probability assignment based on contrast

II. Structure refinement • Markovianity assumption via MRF • Interaction terms • Optimization via MCMC

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Proposed line structure detector I. Short line extraction via localized Radon transform maxima •

Overlapping fixed grid – –





fixed generation of line candidates overlap allows to address shift-variance

Maxima extraction –

to allow short lines’ detection



extract S-many maxima per image region

Probability assignment

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Proposed line structure detector II. Structure refinement via local interactions •

Markov Random Field –

3-by-3 neighborhood with predefined cliques



The distribution of the configuration is given by

where the energy Ej is the sum of all (unitary and binary) energy terms of the segment at location j.

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Proposed line structure detector II. Structure refinement via local interactions •

Interaction energy terms –

Orientation penalty



Distance penalty



The n-th grid element energy is



Optimization is performed via MCMC – –

random initialization simulated annealing

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Proposed line structure detector Detector overview

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Unitary data term for roads extraction II. Structure refinement via local interactions •

Since roads are geometrically better defined we verify the contrast of the line candidates against the background



We consider the Bhattacharyya distance (two Gaussians case)

and define a unitary energy term

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Road network extraction results Image

Ground truth Segments

Result

Fast road network extraction from remotely sensed images by V. Krylov, J. Nelsen. 29 October 2013

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Road network extraction results Image

Ground truth Segments

Result

Fast road network extraction from remotely sensed images by V. Krylov, J. Nelsen. 29 October 2013

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Conclusions + MCMC methods allow optimization of complicated labeling problems (unlike graph cuts); + RJMCMC allows to optimize energies with random numbers of parameters; + No initialization needed.

- Computational complexity (albeit partially parallelizable); - A commonly large number of parameters to specify.

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Fast road network extraction from remotely sensed ...

Oct 29, 2013 - In this work we address road extraction as a line detection problem, relying on the ... preferential treatment for long lines. ... Distance penalty.

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