Unsupervised hierarchical convolutional sparse auto-encoder for high spatial resolution imagery scene classification

Recently, efficiently representing the scenes from a large volume of high spatial resolution (HSR) images is a critical problem to be solved. Traditional scene classification problems were solved by utilizing the spatial, spectral and structural features of the HSR images separately or jointly, whic...

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Veröffentlicht in:	Proceedings (International Conference on Natural Computation. Print) S. 42 - 46
Hauptverfasser:	Han, Xiaobing, Zhong, Yanfei, Zhao, Bei, Zhang, Liangpei
Format:	Tagungsbericht Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.08.2015
Schlagworte:	Algorithms Classification Conferences Convolution Data mining Decoding Deep Learning dropout Encoding Feature extraction hierarchical convolutional sparse autoencoder (HCSAE) Hierarchies HSR images Image classification Machine learning Robustness scene classification Spatial resolution Spectra Strategy
ISSN:	2157-9563
Online-Zugang:	Volltext
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