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

2

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

3

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

4

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

5

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

6

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

7

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

8

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

9

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.

10

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

11

Proposed line structure detector Detector overview

12

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

13

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

14

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

15

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.

16

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.

1MB Sizes 3 Downloads 303 Views

Recommend Documents

Fast Road Network Extraction from Remotely Sensed ...
proach that employs a fixed-grid, localized Radon transform to extract ..... comparison can be drawn from Table 1 (the results of the benchmark techniques.

The use of remotely sensed data and innovative ...
The development of advanced next-generation models in combination new .... As an illustration of the potential of AIRS to improve tropical cyclone prediction, figure 4 .... and better models and data assimilation techniques is being performed.

The use of remotely sensed data and innovative ...
aNOAA Atlantic Oceanographic and Meteorological Laboratory, Miami, FL b Goddard Earth Science and Technology Center, University of Maryland, Baltimore ...

Remotely sensed dune celerity and sand flux ...
Dec 17, 2008 - et al., 1999; Bristow and Lancaster, 2004], (b) short term sand trap and anemometer studies [e.g., Greeley et al.,. 1996] or more recently (c) the use of saltation flux impact responders [Baas, 2004]. We here propose an approach for me

Integration of Remotely Sensed Optical and Acoustic ...
DELINEATION OF REEF-LINES IN BROWARD COUNTY, FLORIDA (USA)* ... National Coral Reef Institute, Nova Southeastern University Oceanographic ...

Investigation on image fusion of remotely sensed ...
In this paper the investigation on image fusion applied to data with significantly different spectral properties is presented. The necessity of placing high emphasis on the underscored problem is demonstrated with the use of simulated and real data.

Remotely sensed dune celerity and sand flux ...
Dec 17, 2008 - mine sand flux. To detect dune migration we use the .... massive diatomite crust that can be easily identified as intricately shaped white patterns ...

Texture based segmentation of remotely sensed ...
The exact purpose of image segmentation is either to extract the outlines of different regions in the ...... Simplicity of these metrics may in some cases cause mis-.

Constrained Principal Component Extraction Network
Adopting a network with architecture ... samples which makes it unsuitable for online processing. .... atively will then reasonably measure the similarity degree.

Constrained Principal Component Extraction Network
e-mail: {tchen, syue06}@cqu.edu.cn. Shi Jian ... addresses one of the major issues of traditional batch PCA that requires to .... a batch of N input data x ∈ p×N.

Fast Network Synchronization
algorithm through an improved PC-PDA synchronizer. PalmPilot PDAs (Personal Digital Assistants) synchro- nize their data with that on a host PC either locally ...

TEXTLINE INFORMATION EXTRACTION FROM ... - Semantic Scholar
because of the assumption that more characters lie on baseline than on x-line. After each deformation iter- ation, the distances between each pair of snakes are adjusted and made equal to average distance. Based on the above defined features of snake

Unsupervised Features Extraction from Asynchronous ...
Now for many applications, especially those involving motion processing, successive ... 128x128 AER retina data in near real-time on a standard desktop CPU.

3. MK8 Extraction From Reservoir.pdf
Try one of the apps below to open or edit this item. 3. MK8 Extraction From Reservoir.pdf. 3. MK8 Extraction From Reservoir.pdf. Open. Extract. Open with.

TEXTLINE INFORMATION EXTRACTION FROM ... - Semantic Scholar
Camera-Captured Document Image Segmentation. 1. INTRODUCTION. Digital cameras are low priced, portable, long-ranged and non-contact imaging devices as compared to scanners. These features make cameras suitable for versatile OCR related ap- plications

Textline Information Extraction from Grayscale Camera ... - CiteSeerX
INTRODUCTION ... our method starts by enhancing the grayscale curled textline structure using ... cant features of grayscale images [12] and speech-energy.

Scalable Attribute-Value Extraction from Semi ... - PDFKUL.COM
huge number of candidate attribute-value pairs, but only a .... feature vector x is then mapped to either +1 or −1: +1 ..... Phone support availability: 631.495.xxxx.

Saliency extraction with a distributed spiking neural network
Abstract. We present a distributed spiking neuron network (SNN) for ... The bio-inspired paradigm aims at adapting for computer systems what we un- derstand ...

A Fast Vision-based Road Following Strategy Applied ...
trol system development environment was used for design and validation ... based control application which is both useful and feasible using off-the-shelf ...

Extraction of temporally correlated features from ...
many applications, especially those involving motion processing, successive frames contain ... types of spiking silicon retinas have already been successfully built, generally with resolution of ...... In Electron devices meeting. IEDM. 2011 IEEE.

Information Extraction from Calls for Papers with ... - CiteSeerX
These events are typically announced in call for papers (CFP) that are distributed via mailing lists. ..... INST University, Center, Institute, School. ORG Society ...

paraphrase extraction from parallel news corpora
[Ibrahim et al., 2003], instead of using parallel news corpora as input source, used mul- ..... we score each sentence pair with each technique and pick the best k sentence pairs and ...... Elaboration: the NASDAQ — the tech-heavy NASDAQ.