Incremental Graph Embedding Based on Spatial-Spectral Neighbors for Hyperspectral Image Classification

Abstract

Recently, graph embedding-based methods have been developed in dimensionality reduction (DR) and classification of hyperspectral image (HSI). The key step for graph embedding methods is the construction of graph. The commonly used method is to manually choose nearest neighbors and then, compute edge weights using the spectral feature. However, the adjacency graph is inappropriate due to the negligence of spatial information. What is more, the construction of graph only takes training samples or $k$ nearest neighbors into account, which may lead to unsuitable graph representation. In this paper, we propose a novel incremental graph embedding (IGE) algorithm to construct a spatial-spectral neighbor graph for the HSI classification. The IGE can spread the discrimination information contained in training samples to their neighbors until each testing sample has a pseudo label. The spatial affinity weights between unlabeled data points and their labeled neighbors are calculated according to the construction strategies of the spatial-spectral neighbor graph. The pseudo label of the unlabeled data point is determined based on the maximum spatial affinity weights. Moreover, three weight strategies are designed for those samples nearby the decision boundary to improve the separability of different classes. In addition, the window size of spatial neighbors is able to be adjusted adaptively according to whether labeled data points in spatial neighbors exit. Experimental results on two datasets, Indian Pine and Pavia University have demonstrated that our algorithm is remarkably superior to other conventional DR algorithms on improving classification performance.