Mapping Roads and other Urban Materials using Hyperspectral Data

Dar Roberts, Meg Gardner, Becky Powell, Phil Dennison, Val Noronha

What is Hyperspectral Remote Sensing?

• Airborne or Spaceborne Sensors– Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)– Hyperion

• Sample large number of contiguous wavelengths– AVIRIS: 224 channels, 10 nm intervals, 20 to 4 m.– Ikonos: 4 channels (4 m), 1 panchromatic (1 m)– Thematic Mapper: 6 channels, 3 vis,3 NIR, 30 m.

• Material identification based on chemistry and physical structure– Radiance --> reflectance– Reflectance --> identification

Challenges of the Urban Environment

• Urban environments are incredibly heterogeneous– The diversity of materials is high

• Urban land-cover is typically homogeneous below the spatial resolution of most sensors– Most sensors sample 20-30 meter resolution, but surfaces are

smaller than that– Exceptions: Ikonos (1 m), aerial photography (< 1m)

• Spectral knowledge of urban environments is lacking• Algorithms for mapping urban environments are mostly

lacking– Mostly pattern matching, some classification

Objectives

• To develop a spectral library of urban materials

• To map urban features using hyperspectral data– automated road feature extraction

– roof type mapping and other urban surfaces

• To explore the potential of hyperspectral data for assessing pavement age and quality

Image Data Availability

• Santa Barbara study region• Fine resolution AVIRIS (4 m)

– 1999, 2000, 2001 (scheduled)

• Coarse resolution AVIRIS (20 m)– 1998 to 2001

• Hyperion (30 m, follows ETM)– June 12, 2001

• ETM (30 m)– June 12, 2001

• IKONOS• Extensive photographic coverage (DOQQs)

AVIRIS - Santa Barbara, CaliforniaOct 11, 1999 low-altitude data - 4 meter pixels

Red 1684 nmGreen 1106 nmBlue 675 nm

Each pixel is a spectrumPotential for library development is large

AVIRIS 991011Red = 1684 nmGreen = 1106 nmBlue = 675 nm

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Re

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Parking Lot

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Wavelength (nm)

Re

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Roof5(Vons1)

Roof6(Vons2)

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Road2(CalleReal)

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Field Spectra

• Spectrometer: Analytical Spectral Devices Full range: On loan from JPL

• Late May, early June 2001 field campaign

• Measured a diversity of roads, bridges, sidewalks, roofs and other materials

• Well documented metadata including some photographs

Sample Concrete Spectra

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Field Spectra CollectionASD Full-Range Spectrometer

Field photos were taken & metadata recorded at each field site...

Field Spectra Summary• Over 6,500 urban field spectra were collected throughout Santa Barbara in May & June

2001

• Field spectra were averaged in sets of 5 and labeled appropriately in building the urban spectral library

• The resulting urban spectral library includes:– 499 roof spectra– 179 road spectra– 66 sidewalk spectra– 56 parking lot spectra– 40 road paint spectra– 37 vegetation spectra– 47 non-photosynthetic vegetation spectra (ie. Landscaping bark, dead wood)– 27 tennis court spectra– 88 bare soil and beach spectra– 50 miscellaneous other urban spectra

Roads and Parking Lots

Typical Roads

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Fairview

Cathedral Oaks

Butte

Pembroke

Brandon

Parking Lots

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Dry oil

Parking lot1

Parking lot 2

Parking Lot 3

Sealcoat

Concretes

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Old ConcreteNew ConcreteConcrete BridgeRed TintedConstance

Street Paints

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e Old White

Fresh White

Blue

Fresh Red

Yellow

Fresh Yellow

Tennis Courts & Other Surfaces

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Tennis Court (dg)

Tennis Court (g)

Tennis Court (t)

Gravel 1

Gravel 2

Bare Soil

Plant Materials

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Old Bark

New Bark

Pine Needles

Grass

English Ivy

Star Jasmine

Composite Shingles

Dark Composite Shingle

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Dark Grey

Tan

Very Black

Dark Orange

Green

Light Composite Shingle

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Wavelength (um)

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Red 10 years

Red

Light Grey

Light Brown

Very Light Green

Grey (4)

Represents less than half the spectra measured, all distinct

Other Roof Materials

Bright Roofs

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Wood 1

bRed Tile

dRed Tile

Calshake

Green Metal

Wood 2

Dark Roofs

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Uncoated Tile

Tar (1)

Gravel 2

Red Gravel

Cedarlite

Mapping road quality and age

• What do roads look like when they are modified?

• What do roads look like as they age?

• Can road quality be mapped?

• What materials are confused with roads?

Road Surface Modification

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Dry Oil

Wet Oil

Tar Patch

Sealcoat

Road Aging

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Calle Real

Cathedral Oaks

Evergreen

Brandon

Butte

Delnorte-avg

Old

New

Road Quality

Good roads and bad roads can be spectrally similarCracking & patching occur at the wrong scale

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Butte

Berkeleyg

Berkeleyb

Roads and Roofs

Roof Materials

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Uncoated Tile

Tar Roof(1)

Cedarlite

dg Composite

10yr Red Composite

vblk Composite

All but the darkest are spectrally distinct in some portion of the spectrumIllumination differences and mixed pixels will reduce separability

Roads and Parking Lots

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Tar Patch Parking Lot1

Parking Lot3 Fairview

Butte Pembroke

Approaches for Spectral Feature Extraction

• Matched filters and derivatives

• Cluster matched filter

• Spectral Angle Mapper

• Tetracorder

• Multiple Endmember Spectral Mixture Analysis (MESMA)

• Simple Spectral Mixture Analysis– One suite of spectra used to decompose full scene

– Typical spectra: Soil, Shade, Green Vegetation, Non-photosynthetic Vegetation

– Inappropriate for many urban areas

• MESMA– Multiple suites of spectra

• number and type vary per pixel– Select by fit and fractions

• ideal for the complex urban environment

• Models can be prioritized

Multiple Endmember Spectral Mixture Analysis

MESMA: Initial Results

Road and Roof Confusion

AVIRIS Color Composite Road MappingUsing Adobe Photoshop™ Paintbucket Tool

Masked MESMA

Summary

• Most urban materials sampled are spectrally distinct– Dark surfaces were the least distinct

• Spectra change with aging– Road surfaces and composite shingle brighten

– Paints and red tile darken

• Road quality did not impact spectra– Cracks and patches occur at a different scale

• Roads and certain roof types are confused– Tar roofs, dark composite shingles

• A combination of spatial pattern matching and spectral matching is promising

Future Directions• Improve upon current techniques:

– Publish results

• Address three critical questions:– How many bands do you need, and which bands are best?

– What is the minimum spatial resolution required?

– Which sensors meet these requirements and if none, what could be designed?

– Santa Barbara test site• Test bed of techniques and sensors• Competing techniques for centerline mapping (ie, GPS) and road quality

• Scale up:– Rural roads and global road databases