  Traditional image classification techniques often produce unsatisfactory
results when applied to high spatial resolution data because classes in high
resolution images are not spectrally homogeneous. Texture offers an alternative
source of information for classifying these images. This paper evaluates a
recently developed, computationally simple texture metric called Weber Local
Descriptor (WLD) for use in classifying high resolution QuickBird panchromatic
data. We compared WLD with state-of-the art texture descriptors (TD) including
Local Binary Pattern (LBP) and its rotation-invariant version LBPRIU. We also
investigated whether incorporating VAR, a TD that captures brightness
variation, would improve the accuracy of LBPRIU and WLD. We found that WLD
generally produces more accurate classification results than the other TD we
examined, and is also more robust to varying parameters. We have implemented an
optimised algorithm for calculating WLD which makes the technique practical in
terms of computation time. Overall, our results indicate that WLD is a
promising approach for classifying high resolution remote sensing data.
