Dense connection and depthwise separable convolution based CNN for polarimetric SAR image classification

Convolution neural networks (CNN) have achieved great success in natural image processing where large amounts of training data are available. However, for the polarimetric synthetic aperture radar (PolSAR) image classification problem, the number of labeled training samples is typically limited. To...

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Veröffentlicht in:	Knowledge-based systems 2020-04, Vol.194, p.105542, Article 105542
Hauptverfasser:	Shang, Ronghua, He, Jianghai, Wang, Jiaming, Xu, Kaiming, Jiao, Licheng, Stolkin, Rustam
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial neural networks Convolution Convolutional neural networks Dense connection Depthwise separable convolution DSNet Feature extraction Feature maps Image classification Image processing Neural networks Parameters Performance enhancement Polarimetric SAR image classification Radar imaging Radar polarimetry Synthetic aperture radar Training
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