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

Outline

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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)

Methodology (1/2)

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13 Multispectral Images

Atmospheric Correction

13 Panchromatic Images

Digital Surface Model

13 Multispectral True-Ortho ImagesPolynomial Multispectral

Nadir Multispectral

Multi-angle Multispectral

Principal Component Analysis

Methodology (2/2)

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Polynomial Multispectral

Nadir Multispectral

Multi-angle Multispectral PCA

y = ax2 + bx + cPoly fit standard error

104 bands 32bands 10 bands8 bands

Atmospheric Correction (1/2)

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Coastal

Blue

Green

Yellow

Red

Red Edge

NIR1

NIR2

Atmospheric Correction (2/2)

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Coastal

Blue

Green

Yellow

Red

Red Edge

NIR1

NIR2

Information Sources

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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

Partial BRDF - over a single satellite track

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Coastal

Blue

Green

Yellow

Red

Red Edge

NIR1

NIR2

Pitched surfaces

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Coastal

Blue

Green

Yellow

Red

Red Edge

NIR1

NIR2

Varying reflectance in both time and angle (1/2)

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Differentiates land-use of similar spectral signature– low vs. high volume traffic roads

Multi-angle spectral variability– stationary vehicles

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

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Four Experiments

The most-nadir multi-spectral image is used as base-case

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Exp. 9 Exp. 10 Exp. 11 Exp. 12

Nadir Multispectral X

Multi-angle Multispectral X

Polynomial Multispectral X

Principal Component Analysis X

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

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Flat Roof

Pitched Roof

Concrete

Pavement

Parking Lot

Healthy Vegetation

Stressed Vegetation

Dormant Vegetation

Soil

Evergreen Trees

Deciduous Trees

Parked Cars

Recreational

Shadow

Water

Results (1/2)

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Exp. 9 Exp. 10 Exp. 11 Exp. 12

Nadir X

Multi-angle X

Polynomial X

PCA X

Exp. 9

Exp. 10

Exp. 11

Exp. 12

Flat Roof 55.4 62.8 54.6 57.2

Pitched Roof 35.6 54.8 63.2 65.8

Concrete 84.7 94.2 89.1 91.0

Pavement 54.1 80.5 80.2 84.0

Parking Lot 76.4 82.0 90.7 89.3

Healthy Vegetation 94.1 96.7 96.0 96.2

Stressed Vegetation 94.7 94.8 91.4 90.4

Dormant Vegetation 80.8 80.8 82.0 85.4

Soil 90.2 94.8 92.5 96.7

Evergreen Trees 77.2 85.6 92.4 93.0

Deciduous Trees 85.9 94.8 95.8 91.0

Parked Cars 28.0 43.0 64.7 49.6

Recreational 88.8 96.2 96.7 97.1

Shadow 89.3 94.8 93.0 89.9

Water 95.1 95.9 83.4 83.2

Exp. 9 Exp. 10 Exp. 11 Exp. 12

Results (2/2)

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Flat Roof

Pitched Roof

Concrete

Pavement

Parking Lot

Healthy Vegetation

Stressed Vegetation

Dormant Vegetation

Soil

Evergreen Trees

Deciduous Trees

Parked Cars

Recreational

Shadow

Water

Detail

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• 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

Feature Contribution

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Conclusions

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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

2011 IEEE GRSS Data Fusion Contest

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