Topological Structure and Semantic Information Transfer Network for Cross-Scene Hyperspectral Image Classification

Domain adaptation techniques have been widely applied to the problem of cross-scene hyperspectral image (HSI) classification. Most existing methods use convolutional neural networks (CNNs) to extract statistical features from data and often neglect the potential topological structure information bet...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 34; H. 6; S. 2817 - 2830
Hauptverfasser: Zhang, Yuxiang, Li, Wei, Zhang, Mengmeng, Qu, Ying, Tao, Ran, Qi, Hairong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.06.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptation models
Alignment
Artificial neural networks
Classification
Convolutional neural networks
Cross-scene
Data mining
Data structures
distribution alignment
domain adaption
Domains
Feature extraction
graph alignment
graph convolutional network (GCN)
Graph theory
hyperspectral image (HSI) classification
Hyperspectral imaging
Image classification
Information transfer
Land cover
Land use
Neural networks
Semantics
Task analysis
Topology
Training
ISSN:2162-237X, 2162-2388, 2162-2388
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Zusammenfassung:Domain adaptation techniques have been widely applied to the problem of cross-scene hyperspectral image (HSI) classification. Most existing methods use convolutional neural networks (CNNs) to extract statistical features from data and often neglect the potential topological structure information between different land cover classes. CNN-based approaches generally only model the local spatial relationships of the samples, which largely limits their ability to capture the nonlocal topological relationship that would better represent the underlying data structure of HSI. In order to make up for the above shortcomings, a Topological structure and Semantic information Transfer network (TSTnet) is developed. The method employs the graph structure to characterize topological relationships and the graph convolutional network (GCN) that is good at processing for cross-scene HSI classification. In the proposed TSTnet, graph optimal transmission (GOT) is used to align topological relationships to assist distribution alignment between the source domain and the target domain based on the maximum mean difference (MMD). Furthermore, subgraphs from the source domain and the target domain are dynamically constructed based on CNN features to take advantage of the discriminative capacity of CNN models that, in turn, improve the robustness of classification. In addition, to better characterize the correlation between distribution alignment and topological relationship alignment, a consistency constraint is enforced to integrate the output of CNN and GCN. Experimental results on three cross-scene HSI datasets demonstrate that the proposed TSTnet performs significantly better than some state-of-the-art domain-adaptive approaches. The codes will be available from the website: https://github.com/YuxiangZhang-BIT/IEEE_TNNLS_TSTnet .
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3109872