What is Hyperspectral Remote Sensing?

1. Mapping Roads and other Urban Materials using Hyperspectral DataDar Roberts, Meg Gardner, Becky Powell, Phil Dennison, Val Noronha

2. 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

4. 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

5. 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

6. 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)

7. AVIRIS - Santa Barbara, California Oct 11, 1999 low-altitude data - 4 meter pixels Red 1684 nm Green 1106 nm Blue 675 nm

8. Each pixel is a spectrum Potential for library development is large Red = 1684 nm Green = 1106 nm Blue = 675 nm AVIRIS 991011

9. 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

10. Field Spectra Collection ASD Full-Range Spectrometer

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

12. 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

13. Roads and Parking Lots

14. Concretes

15. Street Paints

16. Tennis Courts & Other Surfaces

17. Plant Materials

18. Composite Shingles Represents less than half the spectra measured, all distinct

19. Other Roof Materials

20. 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?

21. Road Surface Modification

22. Road Aging

23. Road Quality Good roads and bad roads can be spectrally similar Cracking & patching occur at the wrong scale

24. Roads and Roofs All but the darkest are spectrally distinct in some portion of the spectrum Illumination differences and mixed pixels will reduce separability

25. Approaches for Spectral Feature Extraction • Matched filters and derivatives • Cluster matched filter • Spectral Angle Mapper • Tetracorder • Multiple Endmember Spectral Mixture Analysis (MESMA)

26. Multiple Endmember Spectral Mixture Analysis • 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

27. MESMA: Initial Results Road and Roof Confusion

28. AVIRIS Color Composite Road Mapping Using Adobe Photoshop™ Paintbucket Tool

29. Masked MESMA

30. 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

31. 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