Texture Based Classification of High Resolution Remotely Sensed Imagery using Weber Local Descriptor
This work addresses the challenge of spectral heterogeneity in high-resolution remote sensing data for applications like land cover classification, though it is incremental as it builds on existing texture descriptors.
The paper tackled the problem of classifying high-resolution remote sensing imagery by evaluating the Weber Local Descriptor (WLD) texture metric, finding that WLD generally produces more accurate and robust classification results than state-of-the-art texture descriptors like LBP and LBPRIU.
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.