Spectral-Spatial Feature Extraction With Dual Graph Autoencoder for Hyperspectral Image Clustering

Autoencoder (AE) is an unsupervised neural network framework for efficient and effective feature extraction. Most AE-based methods do not consider spatial information and band correlations for hyperspectral image (HSI) analysis. In addition, graph-based AE methods often learn discriminative represen...

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Veröffentlicht in:	IEEE transactions on circuits and systems for video technology Jg. 32; H. 12; S. 8500 - 8511
Hauptverfasser:	Zhang, Yongshan, Wang, Yang, Chen, Xiaohong, Jiang, Xinwei, Zhou, Yicong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	autoencoder Band spectra Banded structure Clustering Coders Convolution Data mining Decoding dimensionality reduction Encoders-Decoders Feature extraction graph convolution Graph neural networks Graphical representations Hyperspectral imagery Hyperspectral imaging Pixels Principal component analysis Similarity Source code Spatial data Spectral bands Task analysis
ISSN:	1051-8215, 1558-2205
Online-Zugang:	Volltext
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