High-Resolution SAR Image Classification via Deep Convolutional Autoencoders

Synthetic aperture radar (SAR) image classification is a hot topic in the interpretation of SAR images. However, the absence of effective feature representation and the presence of speckle noise in SAR images make classification difficult to handle. In order to overcome these problems, a deep convol...

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Veröffentlicht in:	IEEE geoscience and remote sensing letters Jg. 12; H. 11; S. 2351 - 2355
Hauptverfasser:	Geng, Jie, Fan, Jianchao, Wang, Hongyu, Ma, Xiaorui, Li, Baoming, Chen, Fuliang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.11.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Classification Deep convolutional autoencoders (DCAEs) Feature extraction Image classification Networks Noise Representations Speckle supervised classification Synthetic aperture radar synthetic aperture radar (SAR) image Texture Training Transformations Transforms supervised classification synthetic aperture radar (SAR) image Deep convolutional autoencoders (DCAEs)
ISSN:	1545-598X, 1558-0571
Online-Zugang:	Volltext
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