S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 1

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Urban area detection and segmentation using OTB

Stéphane MAY

stephane.may@cnes.frJordi INGLADA

jordi.inglada@cnes.fr

S. MAY – J. INGLADA – Urban area detection – IGARSS 2009 2

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Context

Floodings due to Hurricane Ike over Gonaives (Haïti) –

September 2009

Urban area detection

Detect urban area to evaluate the impact zone

Urban area detection applied to:

Rapid mapping

Risk management

Urbanism planning, ...

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Contents of the presentation

Introduction

Algorithms description

Parameter estimation and validation process

Analysis of the results

Conclusion

Urban area detection and segmentation

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Key requirements of the proposed algorithms

Low computation time

Over detection preferred to misdetection

Refined algorithms may be applied in further steps

Algorithm independent of the image resolution

Mixing radiometric techniques (NDVI, NDWI mask) and texture algorithms

3 algorithms based on texture :

Edge density

Pantex detector

Gabor Texture Index filtering algorithm

Introduction

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Computation of a local density of edges around a pixel

Sobel filter (edge detection)

Application of a threshold

Computation of density of detected edges

One parameter : the filter radius

Edge density algorithm

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Computation of the Haralick contrast texture descriptor

Considering 8 directions, computation of the co-occurence matrixes

Optimal histogram bin defined by the Scott formula

Computation of the Haralick contrast descriptors

The output value of the filter is the min value of contrasts

Pantex detector

1 parameter : radius of the filter

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A Gabor filter is a linear filter whose impulse response is defined by a harmonic function multiplied by a Gaussian function

Interesting properties to detect textures and alignments

Gabor Texture Index filtering algorithm (1)

A=0.10 B=0.20 =0° f=0.25

A=0.12 B=0.12 =45° f=0.12

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Proposed algorithm

Gabor filtering with N directions (Gabor filters bank)

Computation of the local standard deviation with a sliding window

Selection of the median value for the N directions (x M channels)

Hard thresholding to build the binary urban mask

Gabor Texture Index filtering algorithm (2)

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Method applicable using each channel of the image or intensity image

Many parameters...

Gabor filter parameters

Number of directions

Radius of the standard deviation filter

Evaluation of the best parameters set

Gabor Texture Index filtering algorithm (3)

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Contents of the presentation

Introduction

Algorithms description

Parameter estimation and validation process

Analysis of the results

Conclusion

Urban area detection and segmentation

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Use of a Quickbird multispectral image

Creation of a reference mask : urban / non urban

NDVI, and thresholding of clouds, bare soils

Reference mask built for several resolutions : 30m, 20m, 10m,

5m, 2.5m, 1.25m, 0.62m

Monte-Carlo simulation used to evaluate several parameters sets for the Gabor Texture Index (GTI).

The generated mask is compared to the reference mask :

Computation of True Positive Ratio (TPR), False Positive

Ration (FPR)

Computation of TPR and FPR after application of the NDVI mask

Parameter estimation and validation process (1)

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ROC simulations : representation of TPR versus FPR

Indication on processing time (depending of the filters radius)

Parameter estimation and validation process (2)

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Selection of interesting parameters for the GTI detector

Robustness to the parameters choice

Parameter estimation and validation process (3)

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Contents of the presentation

Introduction

Algorithms description

Parameter estimation and validation process

Analysis of the results

Conclusion

Urban area detection and segmentation

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Comparison of the 3 algorithms

ED and GTI are compliant with preliminary specifications

Pantex filter is not applicable at high resolution

Analysis of processing time

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Results – Edge Density

Great improvement thanks to the NDVI

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Results – Gabor Texture Index

Slight performance increase for the GTI (flat curves for lower false alarm rates) : Gabor sensitive to alignments

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Results – Pantex filter

Pantex seems to give the relative worse results in this experiment but differences are small

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3 interesting algorithms to detect urban areas

Edge density filter : one parameter method, robust and fast

Gabor : slight enhancement of performance among the 3

algorithms (sensitive to alignments)

Pantex : able to detect urban areas, but penalized by the high

computation complexity

Application of the NDVI mask and NDWI enhances the results

Mix of radiometry method and texture method is an excellent

approach

Next step : use algorithms based on geometric features to detect buildings...

Conclusion

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Edge detector and pantex available in a packaged application

Gabor Texture Index filter soon available in the OTB

OTB contributions

otbUrbanAreaExtractionApplication

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Thank you for your attention

Urban area detection