An Improved Feature Extraction Method for Texture Classification with Increased Noise Robustness

This paper presents an improved feature extraction method based on the use of state-of-the art filtering techniques and Local Binary Patterns-derived feature descriptors with applications in texture classification. The method is adaptive, being capable to determine the type of noise present in the input image and to apply the appropriate operator for the filtering step of the feature extraction technique. The improved approaches, labelled BM3DELBP (Block Matching and 3D filtering Extended Local Binary Pattern) and SARBM3DELBP (Synthetic Aperture Radar Block Matching and 3D filtering Extended Local Binary Pattern) bring significant improvements both in terms of robustness to Gaussian and speckle noise and in terms of classification accuracy, being invariant to different image transformations. We tested our approach both on synthetic textures from two standard Outex databases and on real polarimetric Synthetic Aperture Radar (SAR) images of pine forests. On all considered databases, the proposed approach proved to he above state-of-the-art LBP variants in terms of classification accuracy, even in the presence of high Gaussian and speckle noise levels.