Optimized Input for CNN-Based Hyperspectral Image Classification Using Spatial Transformer Network

Deep learning-based methods, especially deep convolutional neural networks (CNNs), have shown their effectiveness for hyperspectral image (HSI) classification. In previous deep CNN-based HSI classification methods, a cuboid is empirically determined as the input. The dimensionalities of the cuboid,...

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Veröffentlicht in:	IEEE geoscience and remote sensing letters 2019-12, Vol.16 (12), p.1884-1888
Hauptverfasser:	He, Xin, Chen, Yushi
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Classification Convolution Convolutional neural network (CNN) Deep learning Feature extraction Handicrafts hyperspectral image (HSI) classification Hyperspectral imaging Image classification Machine learning Methods Neural networks Regularization Spatial discrimination learning spatial transformer network (STN) Task analysis Training
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