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Diversity-Connected Graph Convolutional Network for Hyperspectral Image Classification

高光谱成像 计算机科学 图形 模式识别(心理学) 人工智能 邻接表 邻接矩阵 算法 理论计算机科学
作者
Yun Ding,Yanwen Chong,Shaoming Pan,Chun-Hou Zheng
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:61: 1-18 被引量:12
标识
DOI:10.1109/tgrs.2023.3298848
摘要

Hyperspectral image classification methods based on the graph convolutional network (GCN) have received more attention because they can handle irregular regions by graph encoding techniques. However, GCN-based HSI classification methods are highly sensitive to the quality of the graph structure. Its performance degrades in the case of underdeveloped graphs because it cannot excavate the intrinsic adjacency relationships. Thus, it is necessary to improve the quality of graph structure in GCN-based methods. In this paper, a novel diversity-connected graph convolutional network (DCGCN) method is proposed to improve the quality of the graph structure for HSI classification, and its basic idea can be adopted by other GCN-based methods. First, the potential neighbors are excavated by performing topological extensions based on the given graph. The diversity of surrounding neighbors is maintained by adaptively smoothing operation via a global threshold value from Kullback-Leibler divergence to eliminate weak interclass connections caused by weakly spectral variability. Second, another key connectivity restriction is imposed on the diverse neighbors to further refine the ambiguous connections of hard samples aiming at removing strong interclass connections where the spectral information is heavily confounded. Finally, the DCGCN method is analyzed theoretically to demonstrate its low-pass filter property. The comprehensive experiments demonstrate the effectiveness of the proposed DCGCN method and the basic idea of the diversity-connected graph in terms of overall accuracy (OA), kappa coefficient (KC), average accuracy (AA) indexes.
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