# Spectral Pyramid Graph Attention Network for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2001.07108 · HTML: https://mlsanity.com/paper/2001.07108/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2020.

## Built on by (most cited first, 7 of 7)

- [Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications](https://mlsanity.com/paper/2202.08916/) (2022)
- [Robust Tumor Segmentation with Hyperspectral Imaging and Graph Neural Networks](https://mlsanity.com/paper/2311.11782/) (2023)
- [Holistically-Nested Structure-Aware Graph Neural Network for Road Extraction](https://mlsanity.com/paper/2407.02639/) (2024)
- [Spectral Graph Reasoning Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2407.02647/) (2024)
- [Non-parametric Contextual Relationship Learning for Semantic Video Object Segmentation](https://mlsanity.com/paper/2407.05916/) (2024)
- [Graph-Boosted Attentive Network for Semantic Body Parsing](https://mlsanity.com/paper/2407.05924/) (2024)
- [Context Propagation from Proposals for Semantic Video Object Segmentation](https://mlsanity.com/paper/2407.06247/) (2024)

## Abstract (arXiv)

Convolutional neural networks (CNN) have made significant advances in hyperspectral image (HSI) classification. However, standard convolutional kernel neglects the intrinsic connections between data points, resulting in poor region delineation and small spurious predictions. Furthermore, HSIs have a unique continuous data distribution along the high dimensional spectrum domain - much remains to be addressed in characterizing the spectral contexts considering the prohibitively high dimensionality and improving reasoning capability in light of the limited amount of labelled data. This paper presents a novel architecture which explicitly addresses these two issues. Specifically, we design an architecture to encode the multiple spectral contextual information in the form of spectral pyramid of multiple embedding spaces. In each spectral embedding space, we propose graph attention mechanism to explicitly perform interpretable reasoning in the spatial domain based on the connection in spectral feature space. Experiments on three HSI datasets demonstrate that the proposed architecture can significantly improve the classification accuracy compared with the existing methods.