This presentation discusses using WorldView-2 satellite imagery to classify land cover in Atlanta, Georgia. It combined multi-spectral data with multi-angle observations from 13 images. Four experiments classified imagery using a nadir multi-spectral image only, full multi-angle data, and dimensionality reduction techniques. The multi-angle data improved classification accuracy by 14% over using a single nadir image alone. Specific classes like cars and highways benefited more from the multi-angle information.

1. Spectral classificationof 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 13 Panchromatic Images Digital Surface Model Atmospheric Correction Nadir Multispectral Multi-angle Multispectral 13 Multispectral True-Ortho Images Polynomial Multispectral Principal Component Analysis

4. Methodology (2/2) 4 Polynomial Multispectral Nadir Multispectral Multi-angle Multispectral PCA y = ax2 + bx + c Poly fit standard error 104 bands 32bands 10 bands 8 bands

5. Atmospheric Correction (1/2) 5

6. Atmospheric Correction (2/2) 6

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

9. Pitched surfaces 9

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

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

14. Results (1/2) 14

15. Results (2/2) 15

17. Empty Parking Spots

18. Pitched Roofs

19. Deciduous Trees

20. Stressed/Dormant Grass

22. 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

25. WHERE: East Ballroom A