Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification
Philip Sellars, Angelica I. Aviles-Rivero, Carola-Bibiane Schonlieb
Abstract
A central problem in hyperspectral image (HSI) classification is obtaining high classification accuracy when using a limited amount of labeled data. In this article we present a novel graph-based semi-supervised framework to tackle this problem. Our framework uses a superpixel approach, allowing it to define meaningful local regions in HSIs, which with high probability share the same classification label. We then extract spectral and spatial features from these regions and use them to produce a contracted weighted graph-representation, where each node represents a region rather than a pixel. The graph is then fed into a graph-based semi-supervised classifier which gives the final classification. We show that using superpixels in a graph representation is an effective tool for speeding up graphical classifiers applied to HSIs. We demonstrate through exhaustive quantitative and qualitative results that our proposed method produces accurate classifications when an incredibly small amount of labeled data is used. We show that our approach mitigates the major drawbacks of existing approaches, resulting in our approach outperforming several comparative state-of-the-art techniques.