Kernel Extreme Learning Machine Optimized by the Sparrow Search Algorithm for Hyperspectral Image Classification

TL;DR

This paper introduces MLS-KELM, a hyperspectral image classification method that combines multi-scale feature extraction with SSA-optimized kernel parameters, achieving superior accuracy and robustness.

Contribution

It proposes a novel multiscale fusion feature classification method (MLS-KELM) optimized by the Sparrow Search Algorithm for improved hyperspectral image analysis.

Findings

MLS-KELM outperforms existing methods in accuracy

The method demonstrates strong robustness with small sample sizes

Experimental validation on multiple datasets confirms effectiveness

Abstract

To improve the classification performance and generalization ability of the hyperspectral image classification algorithm, this paper uses Multi-Scale Total Variation (MSTV) to extract the spectral features, local binary pattern (LBP) to extract spatial features, and feature superposition to obtain the fused features of hyperspectral images. A new swarm intelligence optimization method with high convergence and strong global search capability, the Sparrow Search Algorithm (SSA), is used to optimize the kernel parameters and regularization coefficients of the Kernel Extreme Learning Machine (KELM). In summary, a multiscale fusion feature hyperspectral image classification method (MLS-KELM) is proposed in this paper. The Indian Pines, Pavia University and Houston 2013 datasets were selected to validate the classification performance of MLS-KELM, and the method was applied to ZY1-02D hyperspectral data. The experimental results show that MLS-KELM has better classification performance and generalization ability compared with other popular classification methods, and MLS-KELM shows its strong robustness in the small sample case.