Stacked Sparse Autoencoder in PolSAR Data Classification Using Local Spatial Information

Terrain classification is an important topic in polarimetric synthetic aperture radar (PolSAR) image processing. Among various classification techniques, the stacked sparse autoencoder (SSAE) is a kind of deep learning method that can automatically learn useful features layer by layer in an unsuperv...

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Bibliographic Details
Published in:	IEEE geoscience and remote sensing letters Vol. 13; no. 9; pp. 1359 - 1363
Main Authors:	Zhang, Lu, Ma, Wenping, Zhang, Dan
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.09.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Classification Cost function Data mining Deep learning Feature extraction Image classification Image processing Learning local spatial information Machine learning Pixels polarimetric synthetic aperture radar (PolSAR) Remote sensing Scattering sparse Speckle stacked sparse autoencoder (SSAE) Synthetic aperture radar Training
ISSN:	1545-598X, 1558-0571
Online Access:	Get full text
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