Two-Stream Deep Architecture for Hyperspectral Image Classification

Most traditional approaches classify hyperspectral image (HSI) pixels relying only on the spectral values of the input channels. However, the spatial context around a pixel is also very important and can enhance the classification performance. In order to effectively exploit and fuse both the spatia...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 56; H. 4; S. 2349 - 2361
Hauptverfasser:	Hao, Siyuan, Wang, Wei, Ye, Yuanxin, Nie, Tingyuan, Bruzzone, Lorenzo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Architecture Artificial neural networks Class-specific fusion Classification convolutional neural networks (CNNs) deep learning Feature extraction hyperspectral image (HSI) classification Hyperspectral imaging Image classification Machine learning Neural networks Noise reduction Pixels remote sensing Rivers Spectra stacked denoising autoencoder (SdAE) State of the art Training two-stream architecture Weight
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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