1. Spectral classification of WorldView-2 multi-angle sequence Atlanta city-model derived from a WorldView-2 multi-sequence acquisition N. Longbotham, C. Bleilery, C. Chaapel, C. Padwick, W. J. Emery, and F. Pacifici

2. Outline 2 This presentation illustrates the unique aspects of the WorldView-2 satellite platform by combining multi-spectral information with multi- angle observations The previous presentation dealt with very high spatial resolution imagery with multi-angle observations What can we do with this kind of data set? Four experiments have been carried out to investigate the classification contribution of multi-angle reflectance (MAR) as well as different feature extraction data sets (reducing the large size of the raw data space)

3. Methodology (1/2) 3 13 Multispectral Images Atmospheric Correction 13 Panchromatic Images Digital Surface Model 13 Multispectral True-Ortho Images Polynomial Multispectral Nadir Multispectral Multi-angle Multispectral Principal Component Analysis

4. Methodology (2/2) 4 Polynomial Multispectral Nadir Multispectral Multi-angle Multispectral PCA y = ax 2 + bx + c Poly fit standard error 104 bands32bands10 bands8 bands

5. Atmospheric Correction (1/2) 5 Coastal Blue Green Yellow Red Red Edge NIR1 NIR2

6. Atmospheric Correction (2/2) 6 Coastal Blue Green Yellow Red Red Edge NIR1 NIR2

7. Information Sources 7 The MAR contains a partial bidirectional reflectance distribution function (BRDF) over a single satellite track at a single sun angle Objects with pitched surfaces, such as trees and residential roofs, will present a different observational cross-section at each angle Surfaces with varying reflectance in both time and angle can be described by an error term that encapsulates the variation of a pixel through the multi-angle sequence

8. Partial BRDF - over a single satellite track 8 Coastal Blue Green Yellow Red Red Edge NIR1 NIR2

9. Pitched surfaces 9 Coastal Blue Green Yellow Red Red Edge NIR1 NIR2

10. Varying reflectance in both time and angle (1/2) 10 Differentiates land-use of similar spectral signature –low vs. high volume traffic roads Multi-angle spectral variability –stationary vehicles

11. Varying reflectance in both time and angle (2/2) 11

12. Four Experiments The most-nadir multi-spectral image is used as base-case 12 Exp. 9Exp. 10Exp. 11Exp. 12 Nadir MultispectralX Multi-angle MultispectralX Polynomial MultispectralX Principal Component AnalysisX

13. Classification and Validation 15 classes of interest have been selected representing a wide variety of both natural and man-made land- covers, including different kind of roof, roads, and vegetation Training: 50 samples per class Validation: 90,000 of independent samples Each of the classification experiments are conducted using the Random Forest algorithm 13 Flat Roof Pitched Roof Concrete Pavement Parking Lot Healthy Vegetation Stressed Vegetation Dormant Vegetation Soil Evergreen Trees Deciduous Trees Parked Cars Recreational Shadow Water

14. Results (1/2) 14 Exp. 9Exp. 10Exp. 11Exp. 12 NadirX Multi-angleX PolynomialX PCAX Exp. 9 Exp. 10Exp. 11Exp. 12 Flat Roof55.462.854.657.2 Pitched Roof35.654.863.265.8 Concrete84.794.289.191.0 Pavement54.180.580.284.0 Parking Lot76.482.090.789.3 Healthy Vegetation94.196.796.096.2 Stressed Vegetation94.794.891.490.4 Dormant Vegetation80.8 82.085.4 Soil90.294.892.596.7 Evergreen Trees77.285.692.493.0 Deciduous Trees85.994.895.891.0 Parked Cars28.043.064.749.6 Recreational88.896.296.797.1 Shadow89.394.893.089.9 Water95.195.983.483.2 Exp. 9Exp. 10Exp. 11Exp. 12

15. Results (2/2) 15 Flat Roof Pitched Roof Concrete Pavement Parking Lot Healthy Vegetation Stressed Vegetation Dormant Vegetation Soil Evergreen Trees Deciduous Trees Parked Cars Recreational Shadow Water

16. Detail 16 Parked Cars Empty Parking Spots Pitched Roofs Deciduous Trees Stressed/Dormant Grass Road Flat Roof Pitched Roof Concrete Pavement Parking Lot Healthy Vegetation Stressed Vegetation Dormant Vegetation Soil Evergreen Trees Deciduous Trees Parked Cars Recreational Shadow Water

17. Feature Contribution 17

18. Conclusions 18 This study showed that there is significant improvement in classification accuracy available from the spectral data in a multi-angle WorldView-2 image sequence. Four spectral classification experiments were separately presented using a nadir multi-spectral image, the full multi-angle multispectral data set, and two feature extraction techniques. The multi-angle spectral information provided 14% improvement in kappa coefficient over the base case of a single nadir multispectral image. Specific classes benefited from the unique aspects of the multi-angle information: –The classes car and highway are of particular interest

19. 2011 IEEE GRSS Data Fusion Contest Data Fusion Session: WHEN: Tuesday, July 26, 08:20 - 10:00 AM WHERE: Ballroom C Data Fusion Technical Committee meeting: WHEN: Tuesday, July 26th, 5:30 to 6:30 PM WHERE: East Ballroom A