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2024 | OriginalPaper | Buchkapitel

5. Graph Neural Network via Edge Convolution for Hyperspectral Image Classification

verfasst von : Yao Ding, Zhili Zhang, Haojie Hu, Fang He, Shuli Cheng, Yijun Zhang

Erschienen in: Graph Neural Network for Feature Extraction and Classification of Hyperspectral Remote Sensing Images

Verlag: Springer Nature Singapore

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Abstract

Hyperspectral images have been widely used in many remote sensing applications due to their rich spectral and spatial information, and up to now, a wide range of applications have been benefited from the development of this direction, including urban development, land monitoring, scene interpretation, and resource exploration 0. Among these applications, HSI classification is a common technique that facilitates the study of the chemical properties of scene materials remotely.

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Vorheriges Kapitel Multi-receptive Field: An Adaptive Path Aggregation Graph Neural Framework for Hyperspectral Image Classification

Nächstes Kapitel Unifying Label Propagation and Graph Sparsification for Hyperspectral Image Classification

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Titel: Graph Neural Network via Edge Convolution for Hyperspectral Image Classification
verfasst von: Yao Ding
Zhili Zhang
Haojie Hu
Fang He
Shuli Cheng
Yijun Zhang
Verlag: Springer Nature Singapore
Buch: Graph Neural Network for Feature Extraction and Classification of Hyperspectral Remote Sensing Images
Print ISBN: 978-981-9780-08-2

Electronic ISBN: 978-981-9780-09-9

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-97-8009-9_5

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