Patch-Free Bilateral Network for Hyperspectral Image Classification Using Limited Samples

Recently, data-driven methods represented by deep learning have been widely used in hyperspectral image (HSI) classification and achieved the promising success. However, using less labeled samples to obtain higher classification accuracy is still a challenging task. In this study, we propose a patch...

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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing 2021, Vol.14, p.10794-10807
Hauptverfasser:	Liu, Bing, Yu, Xuchu
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Bilateral network Classification Computer applications Convolution deep learning (DL) Feature extraction hyperspectral image (HSI) classification Hyperspectral imaging Hyperspectral sensors Image classification Machine learning Methods Receptive field Semantics Support vector machines Task analysis Training data
Online-Zugang:	Volltext
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