Semi-supervised Superpixel-based Multi-Feature Graph Learning for Hyperspectral Image Data

TL;DR

This paper introduces a novel semi-supervised graph learning framework for hyperspectral image classification that leverages superpixel features and pseudo-labels to improve accuracy with limited labeled data.

Contribution

The work presents a multi-stage, edge-efficient graph construction method and an extension that integrates multiple superpixel features, reducing reliance on parameter tuning.

Findings

Outperforms state-of-the-art methods in HSI classification

Effective with limited labeled data

Demonstrates robustness across various datasets

Abstract

Graphs naturally lend themselves to model the complexities of Hyperspectral Image (HSI) data as well as to serve as semi-supervised classifiers by propagating given labels among nearest neighbours. In this work, we present a novel framework for the classification of HSI data in light of a very limited amount of labelled data, inspired by multi-view graph learning and graph signal processing. Given an a priori superpixel-segmented hyperspectral image, we seek a robust and efficient graph construction and label propagation method to conduct semi-supervised learning (SSL). Since the graph is paramount to the success of the subsequent classification task, particularly in light of the intrinsic complexity of HSI data, we consider the problem of finding the optimal graph to model such data. Our contribution is two-fold: firstly, we propose a multi-stage edge-efficient semi-supervised graph learning framework for HSI data which exploits given label information through pseudo-label features embedded in the graph construction. Secondly, we examine and enhance the contribution of multiple superpixel features embedded in the graph on the basis of pseudo-labels in an extension of the previous framework, which is less reliant on excessive parameter tuning. Ultimately, we demonstrate the superiority of our approaches in comparison with state-of-the-art methods through extensive numerical experiments.