first trials on sentinel-1 performance for mapping built...

First trials on Sentinel-1 performance for mapping built-up areas

Kaupo Voormansik1,2, Anni Sisas2,3, Jaan Praks1

Aalto University, Tartu Observatory, University of Tartu

Outline

1. Motivation – why urban monitoring? 2. How to extract built-up areas from SAR imagery,

in theory? 3. Sentinel-1 input data and processing 4. Performance figures 5. Copernicus Urban Development Analyser - CUDA

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Urbanisation – a global megatrend

• Increasing number of people live in urban areas. • Cities grow, covering more and more land. • Taking place all around the world, but fastest growth

in Asia and Africa. There are cities, which population grows up to 10% in year!

• From 2011 to 2050 world’s urban population is expected to grow from 3.6 billion to 6.3 billion.

• 83 % of governments are concerned about their population distribution in the country.

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According to United Nations, Department of Economic and Social Affairs, Population Division (2011): World Urbanization Prospects, the 2011 Revision.

Percentage of urban population and agglomerations by size class: 1960, 1980, 2011, 2025

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1960 Source: United Nations, Department of Economic and Social Affairs, Population Division (2011): World Urbanization Prospects, the 2011 Revision.

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How to extract built-up areas from SAR imagery?

• Several methods: intensity thresholding, InSAR coherence, polarimetry…

• One of the most robust method ideas: exploiting local area statistics!

• Due to the speckle effect homogeneous areas distribution function is known.

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Figure source: Lee and Pottier „Polarimetric Radar Imaging“ 2010

1-look intensity data Exponential distribution 1-look amplitude data Rayleigh distribution 4-look amplitude data Chi distribution

How to extract built-up areas from SAR imagery?

• 𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍_𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍𝒍_𝒎𝒎𝒔𝒔𝒍𝒍𝒎𝒎

= 𝒍𝒍𝒍𝒍𝒎𝒎𝒔𝒔𝒔𝒔𝒍𝒍𝒎𝒎𝒔𝒔.

• The relation is broken in urban areas.

• It is possible to measure the deviation from the expected ratio - „speckle divergence“ method suggested by T. Esch et al in 2010.

• But there is more than just the width of the distribution!

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Figure: Lee and Pottier „Polarimetric Radar Imaging“ 2010

1-look intensity data Exponential distribution 1-look amplitude data Rayleigh distribution 4-look amplitude data Chi distribution

Input data

• Sentinel-1 in orbit since April 2014.

• Most common is the Interferometric Wide (IW) swath mode.

• GRD data products available, VV+VH polarisation.

Sentinel-1 IW mode GRDH data

Swath width 250 km

Resolution (rg X az) 20 m X 22 m

Pixel spacing (rg X az) 10 m X 10 m

Equivalent Number of Looks (ENL)

4.9

Incidence angle 29°-45°

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Source: Sentinel-1 User Handbook

Input data

• Sentinel-1 IW VV/VH GRDH

• Distribution of an homogeneous forest area in Estonia.

• 5 looks, Chi distribution, appears close to Gaussian.

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Sentinel-1, Oct. 23, 2014, Tallinn and Viimsi in Estonia

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R: VV, G: VH, B: VV+VH Sentinel-1, European Space agency 2014

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VV-channel, mean-median Sentinel-1, European Space agency 2014

How we processed?

• Compared against Estonian Building Registry data about Tallinn and the surrounding Harjumaa county.

• Mean-median performance.

• 5x5 window size, 50 m by 50 m on ground.

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Sentinel-1, VH amplitude and buildings of Tallinn


VV VH Natural areas 90% range -12.7 .. 15.5 -5.8 .. 6.5

Sparse built-up areas inside natural areas range

70% 74%

- lower 10% 9%

- higher 20% 17%

Dense built-up areas inside natural areas range

33% 30%

- lower 20% 18%

- higher 47% 52%

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Weather in the area: dry, -6° C

Building orientation effects

• Sentinel-1, Oct. 23, 2014, Tallinn and Viimsi in Estonia

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VV VH

Natural areas 90% range -12.7 .. 15.5 -5.8 .. 6.5

Buildings orientation respect to SAR flight path

90° 45°

90° 45°

Built-up areas inside natural areas range

29% 51% 45% 37%

- lower 23% 14% 15% 19%

- higher 48% 35% 40% 44%

Weather effects

Comparison of two datasets: • Wet conditions:

Jan. 8th, 2015, 5° C, 3.6 mm percip. 6 h prior the data take

• Dry conditions: Dec. 27th, 2014, -6° C, dry

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Weather effects

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VV VH

Weather Wet Dry Wet Dry

Natural areas 90% range

-17.9 .. 20.2 -15.1 .. 17.5 -9.15 .. 11.3 -6.88 … 8.01

Built-up areas inside natural areas range

49% 38% 52% 37%

- lower 15% 17% 14% 17%

- higher 36% 45% 34% 46%

Window size considerations

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VV amplitude

VV amplitude, local_mean-local_median

Ortophoto aboout the area from Estonian Land Board

Conclusion

• Built-up areas could be well extracted from Sentinel-1 IW mode imagery.

• Use images from dry conditions. • Dense built-up areas easier to detect than

detached houses. • Having dual pol. gives rather significant

improvement for detecting buildings at different orientation angles.

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What is CUDA? • Copernicus Urban

Development Analyser (CUDA) - a complex information system for monitoring urbanisation, related infrastructure and population changes.

• Using Copernicus Sentinel satellite data and anonymous mobile Location Based Services (LBS) data.

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CUDA

Satellite data for infrastructure

mapping.

Anonymous LBS data for population statistics.

User interface concept

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Thank you! Questions?

Photo: Villem Voormansik

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First trials on Sentinel-1 performance for mapping built-up areasOutlineUrbanisation – a global megatrendPercentage of urban population and agglomerations �by size class: 1960, 1980, 2011, 2025Slide Number 5Percentage of urban population and agglomerations �by size class: 1960, 1980, 2011, 2025Percentage of urban population and agglomerations �by size class: 1960, 1980, 2011, 2025How to extract built-up areas �from SAR imagery?How to extract built-up areas �from SAR imagery?Input dataInput dataSentinel-1, Oct. 23, 2014, �Tallinn and Viimsi in EstoniaSlide Number 13How we processed?Sentinel-1, Oct. 23, 2014, �Tallinn and Viimsi in EstoniaBuilding orientation effectsWeather effectsWeather effectsWindow size considerationsConclusionWhat is CUDA?User interface conceptThank you! Questions?

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