high spectral and spatial resolution sensor images for...
TRANSCRIPT
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High Spectral And Spatial Resolution Sensor Images for Mapping Urban
Areas
• Dar A. Roberts: UCSB Geography
• Martin Herold: University of Jena
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Outline
• Introduction– Why urban, why imaging spectrometry?
• Urban spectroscopy • Example Analysis
– Classification• Spectral separability• Spectral and spatial tradeoffs
– Matched filters– Pavement Quality– Multiple Endmember Spectral Mixture Analysis
• Summary
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Why is Urban remote sensing important?
• Urban areas are where a majority of humans live– > 50% urban population and rising
• Urban areas are centers of human activity– Major sinks for raw and fabricated materials– Major consumers of energy, sources of airborne and waterborne
pollutants
• Urban areas are vulnerable to disaster, require planning– Flood management/water quality – Fire danger – Urban infrastructure, transportation
• Reduced energy consumption, reduced emissions
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Remote Sensing of Urban Environments
• Remote Sensing is a Crucial Technology– Urban areas are growing rapidly– Many urban areas are poorly mapped globally– Rapid response and planning require current maps
• Urban Environments are Challenging– The diversity of materials is high– The scale at which surfaces are homogeneous is typically below
the spatial resolution of spaceborne and airborne sensors• New Remote Sensing Technologies have considerable
promise– Hyperspectral: AVIRIS, Hyperion, HYMAP– Hyperspatial: IKONOS Panchromatic– LIDAR: Fine vertical resolution– SAR: Interferometry
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Study Site: Santa Barbara, CaliforniaOct 11, 1999 low-altitude data - 4 meter pixels
Considerable dataImage sourcesField spectra
Complex urban environment
Red 1684 nmGreen 1106 nmBlue 675 nm
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Urban Spectroscopy
• What are the spectral properties of typical urban materials?
• How many unique spectra are present?• Which spectra are likely to be confused?• Which wavelengths are important for
distinguishing materials?• How can spectral and spatial information be
used to map roads and roof types and road quality?
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Image SourcesEach pixel is a spectrumPotential for library development is large
Red = 1684 nmGreen = 1106 nmBlue = 675 nm
0
50
100
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350 850 1350 1850 2350Wavelength (nm)
Re
fle
cta
nce
(50
0=50
%)
Parking Lot
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400
500
350 850 1350 1850 2350Wavelength (nm)
Re
fle
cta
nc
e
(50
0=
50
%)
Roof5 (Vons1)
Roof6 (Vons2)
0
50
100
150
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350 850 1350 1850 2350Wavelength (nm)
Ref
lect
ance
(500
=50%
)Road2 (CalleReal)
Road7 (Fairview)
AVIRIS 991011
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Sample Concrete Spectra
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350 850 1350 1850 2350
Wavelength (nm)
Ref
lect
ance
(0.5
=50%
)
ppcsmm.001-ppcsmm.002-ppcsmm.003-ppcsmm.004-ppcsmm.005-ppcsmm.006-
Field Spectra CollectionASD Full-Range Spectrometer
Roberts and Herold, 2004
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Field photos were taken & metadata recorded at each field site...
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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
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Transportation Surfaces
Typical Roads
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Wavelength(um)
Ref
lect
ance
FairviewCathedral OaksButtePembrokeBrandon
Parking Lots
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Wavelength (um)
Refle
ctan
ce
Dry oilParking lot1Parking lot 2Parking Lot 3Sealcoat
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350 850 1350 1850 2350
Wavelength (um)
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ctan
ce
Old ConcreteNew ConcreteConcrete BridgeRed TintedConstance
Asphalt Roads
Parking Lots
ConcreteAge
Age
Hydrocarbon absorption
Material compositionand age are critical
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Road Surface Modification
0
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Wavelength (um)
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lect
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Dry OilWet OilTar PatchSealcoat
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0.25
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0.35
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350 850 1350 1850 2350
Wavelength (um)
Refle
ctan
ce
Calle RealCathedral OaksEvergreenBrandonButteDelnorte-avg
Old
NewAge
Transportation surfaces changeAsphalt roads generally become lighter as they ageCracking, patching and oil generally darken road surfaces
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Street Paints
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350 850 1350 1850 2350Wavelength (um)
Ref
lect
ance
Old WhiteFresh WhiteBlueFresh RedYellowFresh Yellow
HydrocarbonVibrational bands
Pigments
Age
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Composite Shingles
Dark Composite Shingle
0
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350 850 1350 1850 2350Wavelength (um)
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Dark GreyTanVery BlackDark OrangeGreen
Light Composite Shingle
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0.350.4
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Wavelength (um)
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lect
ance
Red 10 yearsRedLight GreyLight BrownVery Light GreenGrey (4)
Generally comprised of asphalt with minerals imbedded in the surface for colorVary depending upon age, mix of materials that provide colorHighly variable – these show only a selection of those present in the region
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Other Roof Materials
Bright Roofs
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350 850 1350 1850 2350Wavelength(um)
Ref
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Wood 1bRed TiledRed TileCalshakeGreen MetalWood 2
Dark Roofs
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350 850 1350 1850 2350Wavelength (um)
Re
fle
cta
nc
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Uncoated TileTar (1)Gravel 2Red GravelCedarlite
Iron oxide
Ligno-cellulose
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The Challenge of Roads and Roofs
Roads and Parking Lots
0
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350 850 1350 1850 2350
Wavelength (um)
Ref
lect
ance
Tar Patch Parking Lot1
Parking Lot3 Fairview
Butte Pembroke
Roof Materials
0
0.1
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350 850 1350 1850 2350Wavelength(um)
Ref
lect
ance
Uncoated TileTar Roof(1)Cedarlitedg Composite10yr Red Compositevblk Composite
Some roads and roofs are quite distinct (Red tile)Composite shingle and asphalt roofs can be spectrally similarAging, illumination and condition complicate analysis
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Classifying Urban Landscapes
Key Questions1) Which classes are spectrally
distinct?2) What is the optimal spatial
resolution?3) How do hyperspectral and broad
band sensors compare?4) How might LIDAR improve
analysis?
From Herold and Roberts, 2006Int. J. Geoinformatics 2(1) 1-14
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Urban Classification SchemesAnderson Classification:Hierarchical classification scheme
VIS model: Vegetation-Impervious-Soil (Ridd, 1995)
Herold et al., 2003
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Spectral Separability Measures:Bhattacharrya Distance
• Screening of spectral characteristics of urban targets
• Separability measures – Bhattacharyya distance:
(µ - mean value | ∑ - Covariance)
• Maximum Likelihood based image classification
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Most suitable spectral bandsTop 14 selected based on Bhattacharyya -distance
From: Herold M., Roberts D., Gardner M. and P. Dennison 2004. Spectrometry for urban area remote sensing - Development and analysis of a spectral library from 350 to 2400 nm, Remote Sens. Environ, Vol 91 (3-4) 304-319 .
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Spectral Separability Matrix
All values are B-distance scores: Larger values = more separableLower left part of matrix: average separabilityUpper right part of matrix: minimum separabilityLight grey are moderately separable, dark grey are problems
From: Herold M., Roberts D., Gardner M. and P. Dennison 2004. Spectrometry for urban area remote sensing - Development and analysis of a spectral library from 350 to 2400 nm, Remote Sens. Environ, Vol 91 (3-4) 304-319 .
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Land Cover Mapping14 most suitable bands26 land cover classes22 built up classesInter-class confusion confirms sep. analysisSpectral limitations:
# and location of bandsNarrow vs. broadband
From: Herold M., Gardner M. and Roberts D. 2003. Spectral Resolution Requirements for Mapping Urban Areas, IEEE Transactions on Geoscience and Remote Sensing, 41, 9, pp. 1907-1919
Overall Accuracy
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Small-footprint LIDAR
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Spatial-spectral tradeoffsProducer’s accuracy User’s accuracyCorrect/Reference Correct/Mapped
Spatial resolution Spatial resolution
Herold et al., 2006
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Matched Filter Analysis
Confusion is minimal between wood shingleand other materialsConsiderable error occurs between Roads and composite shingle roofs
Roberts and Herold, 2004
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Pavement Quality
• Two aspects are of interest– How old is a road?– What is its condition?
• Cracks, patches
• Data Sources– Field spectra– High spatial resolution imagery
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Surface spectra 1 Surface spectra 2 Surface spectra 3
1)
2)
3)
Asphalt Aging
Hydrocarbon
Iron oxide
Minerals
Herold and Roberts,2005
Age: less than 1 year 3 years more than 10 yearsPCI (Roadware): 99 86 32Structure (Roadware): 100 100 63
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Asphalt Condition
Herold and Roberts,2005
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Band Differences for RS data analysis
VIS2 Difference= (830nm-490nm) SWIR Difference = (2120nm-2340nm)
830nm490nm
VIS2
diff
eren
ce
2340nm2120nm
SWIR
Diff
eren
ce
Herold and Roberts,2005
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HyperSpectir (HSI) dataUltra-fine spatial resolution is needed for
mapping road quality
• Goleta, CA• www.spectir.com• HSI-1 data• spatial res. ++• 0.5 m / 40 m swath• spectral cal. --• Only VIS/VNIR use• Improv. sensor now
4 m AVIRIS
0.5 m HyperSpectir HWY 101
Herold and Roberts,2005
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Spatial distribution of VIS2-Difference
0 8Reflectance [%]
Herold and Roberts,2005
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HSI signal versus Roadware data
Herold and Roberts,2005
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Pavement condition index derived from VIS2
Difference
Herold and Roberts,2005
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Mapping Impervious Surfaces and Vegetation Cover in an Urban area using MESMA
• Objective– Identify optimal spectra for
discriminating impervious and pervious surfaces
– Accurately estimate subpixelvegetation cover with variable backgrounds
• Approach– Multiple Endmember Spectral
Mixture Analysis• Allows number and types of
endmembers to vary per pixel• Addresses challenges of spectral
diversity in urban areas• Data
– Field spectral library of over 900 materials
– AVIRIS high resolution image– 2000+ spectra for accuracy
assessment
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Building a Spectral Library
000606, 1650, 830, 645 nm RGB Wood Shingle Roof
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Selecting Impervious and Pervious SpectraCount Based Endmember Selection
• Objective– Identify spectra that
best discriminate pervious and impervious surfaces
• Spectra sorted by two categories
• Optimum spectra selected from each category using CoB
• 51 spectra selected– 20 pervious
• 4 GV• 4 NPV• 5 soils• 7 water
– 31 impervious• 21 roofs• 10 roads
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Model Selection: Two Endmembers
• Legend– Vegetation: Dark purple – Senesced Grass: light
purple– Woodshingle roofs:
Aquamarine– Parking lots: Dark blue– Roads and Streets; Green
Accuracy Assessment:Unclassified: 156 (100 of water)Overall: 86.3%Pervious: 327/400 (81.8%)
72% Soil, 77% GV, 92% NPVImpervious: 1720/1973 (87.2%)
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MESMA Fraction Images
• 4 Endmember Model• NPV, GV, Soil/Impervious (RGB)• Fractions highly accurate
– Readily accounts for spectral variability in backgrounds
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Summary
• Urban environments are challenging due to fine spatial requirements and large spectral heterogeneity
• Imaging spectrometry is critical for improving our understanding of urban spectroscopy
• Imaging spectrometry provides improved spectral discrimination– Roofs and roads remain difficult to separate– Wood shingle is particularly easy to map
• Adding a vertical dimension vastly improves accuracy• New tools, such as MESMA have considerable promise
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Questions?