Joint Spatial and Graph Convolutional Neural Networks - A Hybrid Model for Spatial-Spectral Geospatial Image Analysis

摘要

How to efficiently exploit useful information from hyperspectral data by joint analysis of spectral and spatial information is an important problem. In this work, we demonstrate a fusion network that can leverage recent developments in graph convolutional neural networks (GCNs) to effectively analyze reflectance spectra of hyperspectral image pixels and two-dimensional convolutional neural networks (CNNs) which can extract object-specific spatial characteristics from hyperspectral image. To design this fusion model, we study both decision fusion and deep feature fusion approaches and show that the fusion approach outperforms other traditional deep learning methods such as variants of CNNs.