Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification

TLDR

Exploiting texture information is a key interest for classifying high‑resolution hyperspectral imagery. This study proposes a classification paradigm that leverages rich texture information in HSI. The approach extracts local texture features with local binary patterns, fuses them with Gabor and spectral features at both feature‑ and decision‑levels, and classifies the fused representation using an efficient extreme learning machine. Experiments on several HSI datasets demonstrate that the proposed framework outperforms traditional alternatives.

Abstract

It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of each individual classification pipeline and soft-decision fusion rule is adopted to merge results from the classifier ensemble. Moreover, the efficient extreme learning machine with a very simple structure is employed as the classifier. Experimental results on several HSI data sets demonstrate that the proposed framework is superior to some traditional alternatives.