摘要
遥感技术的飞速发展带来了多样化的遥感数据,高光谱图像作为其中光谱分辨率最高的类型,一直是对地观测各项应用的重要数据源。而在计算机视觉领域,以深度学习为代表的模式识别算法也不断发展和突破,这为高光谱遥感相关应用提供了更有效的技术手段。其中,图神经网络是近年来在高光谱遥感图像解译任务中被广泛利用的有效方法,可以在少量标记样本下利用样本间潜在关系挖掘局部和全局的上下文信息,生成高精度的分类结果。本文从现有研究中总结了最常用的几种图神经网络框架,通过分解每种框架的结构来分析文献中方法的特点,并对这些方法整理归类。本文从图连接、图节点、网络模型3个角度分析遥感领域中的图神经网络方法,分别依据连接的空间范围、节点的信息层次、模型的不确定性将已有研究成果归类。另外,本文介绍了图神经网络在不同模态数量、不同标记数量下的高光谱遥感图像分类应用。最后,本文分别从深度图网络、结合其他深度学习技术、基于图神经网络的大模型3个方面总结和展望了图神经网络的前沿技术,为今后图神经网络在遥感领域的研究提供方向和思路。
The rapid development of remote sensing technology has brought a variety of remote sensing data.Hyperspectral images,with the highest spectral resolution among these data,are always a crucial source for various Earth observation applications.In the field of computer vision,pattern recognition algorithms represented by deep learning also constantly developing and breaking through the limitations,providing more effective technologies for hyperspectral remote sensing applications.In recent years,Graph Neural Networks(GNNs)have been widely utilized in hyperspectral remote sensing image interpretation tasks,which can leverage the underlying relationships between samples to extract both local and global contextual information,producing high-precision classification results even with a limited number of labeled samples.This paper summarizes the most commonly used GNN frameworks from existing studies,analyzing the characteristics of these methods by decomposing their structures and categorizing them.We first extract the commonalities of existing GNN architectures and propose a basic module of GNN,which consists of an information aggregation function and a feature updating function.Building upon this module,we reinterpret various popular GNN architectures,including spectral-based Graph Convolutional Networks(GCNs),spatial-based GCNs,and Graph Autoencoders(GAEs).In the context of GAEs,current approaches are analyzed from three perspectives:loss functions,decoders,and graph reconstruction methods.These methods formulate loss functions to incorporate various graph-based constraints,thereby embodying the implicit assumptions and specific characteristics inherent to each method.Then,the analyses of GNN methods in the remote sensing field are conducted from three perspectives:graph connections,graph nodes,and network models.The existing research outcomes are classified based on the spatial range of connections,the information hierarchy of nodes,and the uncertainty of models.These GNN-based algorithms extract either local information or non-local information(e.g.,global or local-global interactions)by using graph connections across different spatial ranges.The concept of non-local modeling has been extensively explored in GNNs over the past four years.Among GNNs with different node information hierarchies,approaches of using superpixels as graph node representations are the most prevalent.This is because superpixels can serve as a generalized form of node representation for other hierarchies,and their construction is relatively straightforward.Additionally,this paper introduces the application of GNNs in hyperspectral remote sensing image classification under varying modal and label quantities.For single-modal applications,we summarize the characteristics of several representative algorithms and provide their corresponding code implementations.For a limited number of multi-modal applications,we categorize and introduce methods based on the role of GNNs in multi-modal feature fusion.We conduct detailed analyses of the performance of GNN-based classification models in relevant literature,evaluating the applicability of these methods by considering the number of labeled samples and their corresponding classification accuracies.Furthermore,we elaborate on the theoretical foundations and integrated techniques of these models in the fields of supervised,semi-supervised,and unsupervised classification.Finally,the paper summarizes and looks forward to the frontier technologies of GNNs from three aspects:deep graph networks,GNN integrated with other deep learning techniques,and GNN-based foundation models,providing directions and insights for future research in the remote sensing field.
作者
李军
余龙
段依琳
卓莉
LI Jun;YU Long;DUAN Yilin;ZHUO Li(Hubei Key Laboratory of Intelligent Geo-Information Processing,School of Computer Science,China University of Geosciences(Wuhan),Wuhan 430078,China;School of Geography and Planning,Sun Yat-sen University,Guangzhou 510006,China)
出处
《遥感学报》
北大核心
2025年第6期1681-1704,共24页
NATIONAL REMOTE SENSING BULLETIN
基金
国家杰出青年科学基金(编号:T2225019)。
关键词
高光谱遥感
分类
图神经网络
图卷积网络
深度学习
hyperspectral remote sensing
classification
graph neural network
graph convolutional networks
deep learning
