Journal Article

Hyperspectral Image Classification with Localized Graph Convolutional Filtering

Sun, Xu, Pu, Shengliang, Wu, Yuanfeng, Sun, Xiaotong

2021

Overview

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

• Publisher: MDPI AG
• Subject: Algorithms / Artificial neural networks / Classification / Convolution / Cubes / data collection / Deep learning / Discriminant analysis / Filtration / graph convolutional network / graph representation learning / Graph representations / Graph theory / Graphical representations / hyperspectral image classification / hyperspectral imagery / Hyperspectral imaging / image analysis / Image classification / localized graph convolutional filtering / Machine learning / mathematical theory / Neighborhoods / Neural networks / principal component analysis / Principal components analysis / Remote sensing