Unsupervised Spatial-Spectral Feature Learning by 3D Convolutional Autoencoder for Hyperspectral Classification

Feature learning technologies using convolutional neural networks (CNNs) have shown superior performance over traditional hand-crafted feature extraction algorithms. However, a large number of labeled samples are generally required for CNN to learn effective features under classification task, which...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 57; H. 9; S. 6808 - 6820
Hauptverfasser:	Mei, Shaohui, Ji, Jingyu, Geng, Yunhao, Zhang, Zhi, Li, Xu, Du, Qian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.09.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial neural networks Classification Convolution Convolutional neural network (CNN) Feature extraction feature learning Handicrafts hyperspectral Hyperspectral imaging Learning Neural networks Principal component analysis Remote sensing Spatial discrimination learning spatial–spectral Spectra Task analysis Three-dimensional displays Training
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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