Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning

TL;DR

This paper introduces RAGE, a novel ensemble learning method for hyperspectral image classification that effectively handles high dimensionality and preserves local spatial features, outperforming existing approaches.

Contribution

The paper proposes a new spatial-spectral classification method using multiple random anchor graphs and local binary patterns to improve accuracy and computational efficiency.

Findings

RAGE achieves superior classification accuracy compared to state-of-the-art methods.

The use of local binary patterns enhances feature descriptiveness.

Ensemble of multiple anchor graphs improves model robustness.

Abstract

Graph-based semi-supervised learning methods, which deal well with the situation of limited labeled data, have shown dominant performance in practical applications. However, the high dimensionality of hyperspectral images (HSI) makes it hard to construct the pairwise adjacent graph. Besides, the fine spatial features that help improve the discriminability of the model are often overlooked. To handle the problems, this paper proposes a novel spatial-spectral HSI classification method via multiple random anchor graphs ensemble learning (RAGE). Firstly, the local binary pattern is adopted to extract the more descriptive features on each selected band, which preserves local structures and subtle changes of a region. Secondly, the adaptive neighbors assignment is introduced in the construction of anchor graph, to reduce the computational complexity. Finally, an ensemble model is built by utilizing multiple anchor graphs, such that the diversity of HSI is learned. Extensive experiments show that RAGE is competitive against the state-of-the-art approaches.