Volumetric Directional Pattern for Spatial Feature Extraction in Hyperspectral Imagery

In this letter, we propose to use an enhanced version of volumetric directional pattern to efficiently extract rich spatial context information in the hyperspectral imagery (HSI). The proposed technique fuses the texture information from three consecutive bands in the input HSI. The extracted local image texture features for each pixel of interest are then fed into an extreme learning machine classifier to assign object category. The experimental results on three standard hyperspectral data sets demonstrate the effectiveness of the proposed method for HSI classification compared with that of a set of state-of-the-art spatial extraction methods.