Representation learning with convolutional sparse autoencoders for remote sensing

The performance of object recognition and classification on remote sensing imagery is highly dependent on the quality of extracted features and the amount of labeled data in the dataset. In this study, we concentrated on representation learning using unlabeled remote sensing data and using these rep...

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Vydané v:	2013 21st Signal Processing and Communications Applications Conference (SIU) s. 1 - 4
Hlavní autori:	Firat, O., Vural, F. T. Y.
Médium:	Konferenčný príspevok..
Jazyk:	English Turkish
Vydavateľské údaje:	IEEE 01.04.2013
Predmet:	Artificial intelligence Feature extraction Filtering algorithms Gabor filters Image color analysis Image edge detection Remote sensing representation learning self-taught learning sparse auto-encoders unsupervised feature learning
ISBN:	9781467355629, 1467355623
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