Extended multi-structure local binary pattern for high-resolution image scene classification

This paper presents a novel extended multi-structure local binary pattern (EMSLBP) approach for high-resolution image classification, generalizing the well-known local binary pattern (LBP) approach. In the proposed EMSLBP approach, three-coupled descriptors with multi-structure sampling are proposed to extract complementary features (pixel value and radial difference) from local image patches. The anisotropic features derived from elliptical sampling are also rotation invariant by averaging the histograms over rotational angles and combined with the isotropic features extracted from circular sampling. Experimental results show that the proposed method can effectively capture local spatial pattern and local contrast, consistently outperforming several state-of-the-art classification algorithms.

[1]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[2]  Dengxin Dai,et al.  Satellite Image Classification via Two-Layer Sparse Coding With Biased Image Representation , 2011, IEEE Geoscience and Remote Sensing Letters.

[3]  Li Ma,et al.  Adaptive classification of hyperspectral images using local consistency , 2014, J. Electronic Imaging.

[4]  Jon Atli Benediktsson,et al.  Extended Random Walker-Based Classification of Hyperspectral Images , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Lorenzo Bruzzone,et al.  A Novel Approach to the Selection of Spatially Invariant Features for the Classification of Hyperspectral Images With Improved Generalization Capability , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[7]  Goo Jun,et al.  Spatially Adaptive Classification of Land Cover With Remote Sensing Data , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Zhenhua Guo,et al.  Rotation invariant texture classification using LBP variance (LBPV) with global matching , 2010, Pattern Recognit..

[9]  Jon Atli Benediktsson,et al.  SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images , 2010, IEEE Geoscience and Remote Sensing Letters.

[10]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Lu Wang,et al.  Land-use scene classification using multi-scale completed local binary patterns , 2015, Signal, Image and Video Processing.

[12]  Shawn D. Newsam,et al.  Bag-of-visual-words and spatial extensions for land-use classification , 2010, GIS '10.

[13]  Paul W. Fieguth,et al.  Extended local binary patterns for texture classification , 2012, Image Vis. Comput..

[14]  Jon Atli Benediktsson,et al.  Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods , 2013, IEEE Signal Processing Magazine.