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Abstract
En 中文
This paper introduces a novel and efficient Graph Convolutional Network (GCN) and Spatial Supporting Modification (SSM) method for classifying Hyperspectral Images (HSI), called Spatial First (SPA-F). The proposed method can utilize spatial information fully based on the assumption that neighboring pixels are more likely to belong to the same category. The method uses spatial information from two levels and consists of the following steps. Firstly, a graph is constructed of all non-background pixels. Secondly, a GCN is trained and tested to perform node classification tasks based on the constructed graph. Thirdly, the spatial supporting pixels for all non-background pixels are located according to their spatial position in the HSI. Finally, the proposed SSM strategy is used to fine-tune the initial classification results obtained in the second step. The superiorities of the proposed method are verified on the Indian Pines and Salinas-A scene datasets even when using a small number of training samples.
Keywords:
Classification algorithms
Licenses
Machine learning algorithms
Hyperspectral imaging
Training
Task analysis
Surveillance
Spatial first
spatial supporting
hyperspectral images (HSI)
graph convolutional network (GCN)