Paper Info
Title
Hyperspectral Image Classification with a Multiscale Fusion-Evolution Graph Convolutional Network Based on a Feature-Spatial Attention Mechanism
Abstract
Convolutional neural network (CNN) has achieved excellent performance in the classification of hyperspectral images (HSI) due to its ability to extract spectral and spatial feature information. However, the conventional CNN model does not perform well in regions with irregular geometric appearances. The recently proposed graph convolutional network (GCN) has been successfully applied to the analysis of non-Euclidean data and is suitable for irregular image regions. However, conventional GCN has problems such as very high computational cost on HSI data and cannot make full use of information in the image spatial domain. To this end, this paper proposes a multi-scale fusion-evolution graph convolutional network based on the feature-spatial attention mechanism (MFEGCN-FSAM). Our model enables the graph to be automatically evolved during the graph convolution process to produce more accurate embedding features. We have established multiple local and global input graphs to utilize the multiscale spectral and spatial information of the image. In addition, this paper designs a feature-spatial attention module to extract important features and structural information from the graph. The experimental results on four typical datasets show that the MFEGCN-FSAM proposed in this paper has better performance than most existing HSI classification methods.
Year
DOI
Venue
2022
10.3390/rs14112653
REMOTE SENSING
Keywords
DocType
Volume
hyperspectral image classification, convolutional graph network, fusion evolution, multiscale, feature-spatial attention mechanism
Journal
14
Issue
ISSN
Citations 
11
2072-4292
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Haoyu Jing100.34
Yuanyuan Wang249882.58
Zhenhong Du33116.98
Feng Zhang434.76