Assessment of Binary Coding Techniques for Texture Characterization in Remote Sensing Imagery

Abstract

This letter investigates the use of rotation invariant descriptors based on Local Binary Patterns (LBP) and Local Phase Quantization (LPQ) for texture characterization in the context of land-cover and land-use classification of Remote Sensing (RS) optical image data. Very high resolution images from the IKONOS-2 and Quickbird-2 orbital sensor systems covering different urban study areas were subjected to classification through an object-based approach. The experiments showed that the discrimination capacity of LBP and LPQ descriptors substantially increased when combined with contrast information. This work also proposes a novel texture descriptors assembled through the concatenation of the histograms of either LBP or LPQ descriptors and of the local variance estimates. Experimental analysis demonstrated that the proposed descriptors, though more compact, preserved the discrimination capacity of bi-dimensional histograms representing the joint distribution of textural descriptors and contrast information. Finally, the paper compares the discrimination capacity of the LBP- and LPQ-based textural descriptors with that of features derived from the Gray Level Co-occurrence Matrices (GLCM). The related experiments revealed a noteworthy superiority of LBP and LPQ descriptors over the GLCM features in the context of RS image data classification.