LatLBP: Spatial-spectral latent local binary pattern for hyperspectral image classification

Abstract

The rich spatial and spectral information brings great potential for pixel-wise classification of hyperspectral image (HSI). Recently, Local Binary Pattern (LBP) as a prominent texture operator has been introduced for discriminative feature extraction of complex volumetric HSI. However, the popular two-dimensional LBP series can only capture spatial structure features and lack pattern descriptions of the spectral domain. The extended three-dimensional LBP series are capable of local multidimensional descriptions, while the information mining for narrow and continuous bands in the spectral domain is still limited. To tackle these issues, we develop a novel local binary pattern named LatLBP for investigating spatial-spectral latent information, which provides a low-dimensional, fine-grained, high-level representation of dual-domain latent semantic information by exploring the group structure of data. LatLBP consists of representative spatial latent (RSLat) LBP encoding and grouped spectral latent (GSLat) LBP encoding, where RSLat describes the local spatial potential features of each grouped representative band, and GSLat characterizes information about each group of latent spectral factors. Besides, a supporting neighborhood band grouping (NBG) algorithm is also designed to provide a finer group structure. Extensive experiments conducted on four HSI datasets indicate that the optimal classification results, i.e., 86.94% for Pavia University, 88.66% for Indian Pines, 77.75% for HanChuan, and 85.54% for HongHu, are achieved by either the proposed LatLBP or GSLat compared to competitive operators.