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2024 | OriginalPaper | Buchkapitel

6. Unifying Label Propagation and Graph Sparsification 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 image (HSI) is a branch of optical remote sensing data, which has advantages of high spectral resolution and detailed spatial structure (Zhang et al. in IEEE Trans. Cybern. 48:16–28, 2016). Among the applications in HSI processing, hyperspectral image classification (HSIC) is a fundamental yet challenging problem (Hong et al. in IEEE Trans. Geosci. Remote Sens. 58:3791–3808, 2020; Jiang et al. in IEEE Trans. Geosci. Remote Sens. 57:851–865, 2019), which aims to assign a specific label to each pixel in the image. HSIC has been widely applied to many scenarios, such as military target detection, vegetation monitoring, and disaster prevention and control.

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Vorheriges Kapitel Graph Neural Network via Edge Convolution for Hyperspectral Image Classification
Nächstes Kapitel S2GFormer: Exploring Relationship Between Transformer and Graph Convolution for Hyperspectral Image Classification
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Metadaten
Titel
Unifying Label Propagation and Graph Sparsification for Hyperspectral Image Classification
verfasst von
Yao Ding
Zhili Zhang
Haojie Hu
Fang He
Shuli Cheng
Yijun Zhang
Copyright-Jahr
2024
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_6

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