Using convolutional features and a sparse autoencoder for land-use scene classification

In this article, we propose a novel approach based on convolutional features and sparse autoencoder (AE) for scene-level land-use (LU) classification. This approach starts by generating an initial feature representation of the scenes under analysis from a deep convolutional neural network (CNN) pre-...

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Vydané v:	International journal of remote sensing Ročník 37; číslo 10; s. 2149 - 2167
Hlavní autori:	Othman, Esam, Bazi, Yakoub, Alajlan, Naif, Alhichri, Haikel, Melgani, Farid
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Taylor & Francis 18.05.2016
Predmet:	artificial intelligence California Classification data collection Image classification Land use Learning Neural networks Reconstruction remote sensing Representations Training California
ISSN:	0143-1161, 1366-5901, 1366-5901
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