Abstract
Typically, high-resolution remote sensing (HRRS) images contain a high level noise as well as possess different texture scales. As a result, existing image segmentation approaches are not suitable to HRRS imagery. In this paper, we have presented an unsupervised texture-based segmentation algorithm suitable for HRRS images, by extending the local binary pattern texture features and the lossless wavelet transform. Our experimental results using USGS 1 ft orthoimagery show a significant improvement over the previously proposed LBP approach