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

arXiv: https://arxiv.org/abs/2103.13710 · HTML: https://mlsanity.com/paper/2103.13710/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 25 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning](https://mlsanity.com/paper/1812.09530/) (2018)
- [Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/1903.06548/) (2019)
- [Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification](https://mlsanity.com/paper/1905.02488/) (2019)
- [A Sampling Theory Perspective of Graph-based Semi-supervised Learning](https://mlsanity.com/paper/1705.09518/) (2017)

## Abstract (arXiv)

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.