Title :
Texture-Based Remote-Sensing Image Segmentation
Author :
Guo, Dihua ; Atluri, Vijayalakshmi ; Adam, Nico
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
Keywords :
feature extraction; geophysical signal processing; image segmentation; image texture; remote sensing; unsupervised learning; wavelet transforms; HRRS; USGS orthoimagery; binary pattern feature; high-resolution remote sensing; image segmentation; lossless wavelet transform; unsupervised texture-based segmentation algorithm; Image segmentation; Lab-on-a-chip; Remote sensing; Tellurium; Tin;
Conference_Titel :
Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on
Conference_Location :
Amsterdam
Print_ISBN :
0-7803-9331-7
DOI :
10.1109/ICME.2005.1521710
