Local-Manifold-Learning-Based Graph Construction for Semisupervised Hyperspectral Image Classification

Abstract

Graph construction, which is at the heart of graph-based semisupervised learning (SSL), is investigated by using manifold learning (ML) approaches. Since each ML method can be demonstrated to correspond to a specific graph, we build the relation between ML and SSL via the graph, where ML methods are employed for graph construction. Moreover, sparsity is important for the efficiency of SSL algorithms, and therefore, local ML (LML)-method-based sparse graphs are utilized. The LML-based graphs are able to capture the local geometric properties of hyperspectral data and, thus, are beneficial for classification of data with complex geometry and multiple submanifolds. In experiments with Hyperion and AVIRIS hyperspectral data, graphs constructed by two LML methods, namely, locally linear embedding and local tangent space alignment (LTSA), performed better than several popular graph construction methods, and the highest accuracies were obtained by using graphs provided by LTSA.