Multi-level graph learning network for hyperspectral image classification

Sheng Wan

doi:10.1016/j.patcog.2022.108705

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Multi-level graph learning network for hyperspectral image classification

2022-09-01

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Sheng Wan

Shirui Pan (Shirui Pan)

Shengwei Zhong

杨

杨洁 (Jie Yang)

Jian Yang (Jian Yang)

Yibing Zhan

龚晨 (Chen Gong) *

DOI：10.1016/j.patcog.2022.108705

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Abstract

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Graph Convolutional Network (GCN) has emerged as a new technique for hyperspectral image (HSI) classification. However, in current GCN-based methods, the graphs are usually constructed with manual effort and thus is separate from the classification task, which could limit the representation power of GCN. Moreover, the employed graphs often fail to encode the global contextual information in HSI. Hence, we propose a Multi-level Graph Learning Network (MGLN) for HSI classification, where the graph structural information at both local and global levels can be learned in an end-to-end fashion. First, MGLN employs attention mechanism to adaptively characterize the spatial relevance among image regions. Then localized feature representations can be produced and further used to encode the global contextual information. Finally, prediction can be acquired with the help of both local and global contextual information. Experiments on three real-world hyperspectral datasets reveal the superiority of our MGLN when compared with the state-of-the-art methods. (c) 2022 Elsevier Ltd. All rights reserved.

Keywords:

Graph convolutional network

Graph-based machine learning

Hyperspectral image classification

Remote sensing

Graph structural learning

Graph convolutional network

Graph-based machine learning

Hyperspectral image classification

Remote sensing

Graph structural learning