Graph-Cut-Based Node Embedding for Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Images

Abstract

Dimensionality reduction (DR) is a common preprocessing technology for hyperspectral images (HSIs). Recently, many neural networks can implement DR to remove the re-dundant information by node embedding. However, numer-ous hidden-layer parameters limit the generalization ability of the node embedding. In this paper, we develop a graph-cut-based node embedding (GCNE) that can be used for DR of HSIs. The embedding can refine correlations by a graph-cut strategy, and it can avoid numerous parameters when using graph models. Moreover, we combine the graph-cut strategy and extreme learning machine (ELM) to achieve HSI classi-fication. The effectiveness of the proposed method is verified by using real HSIs. Compared with other state-of-the-art DR and classification methods, the proposed approach demon-strates very competitive performance.