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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Veröffentlicht in:	2013 21st Signal Processing and Communications Applications Conference (SIU) S. 1 - 4
Hauptverfasser:	Firat, O., Vural, F. T. Y.
Format:	Tagungsbericht
Sprache:	Englisch Türkisch
Veröffentlicht:	IEEE 01.04.2013
Schlagworte:	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
Online-Zugang:	Volltext
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