Abstract. A spatial method of multistructure sampling based rotation-invariant uniform local binary pattern(named MsLBP riu2 ) for classification of hyperspectral images is proposed. This method exploits the local prop-erty (micro-/macrostructure) of local image patches encoded in the classifier by considering a local neighboringstructure around each central pixel and can well suppress the difference of rotational textures for each multi-cluster class. The proposed method is simpleyet efficient for extracting isotropic andanisotropic spatial featuresfromlocalimagepatchesviadifferentextendedsamplingoncircularregionsandellipticaloneswithfourdifferentrotational angles. Furthermore, the rotation-invariant characteristic of extracted isotropic features is achieved bytheinclusionofarotation-invariantuniformLBPoperator.Moreover,theproposedmethodbecomesmorerobustwith respect to the within-class variation. Finally, different classifiers, support vector machine, K-nearest neigh-bor, and linear discriminant analysis, are compared to evaluate MsLBP
['Xiaoyong Bian', 'Xiaolong Zhang', 'Renfeng Liu', 'Li Ma', 'Xiaowei Fu']
Adaptive classification of hyperspectral images using local consistency