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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Vydané v:	IEEE geoscience and remote sensing letters Ročník 13; číslo 9; s. 1359 - 1363
Hlavní autori:	Zhang, Lu, Ma, Wenping, Zhang, Dan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.09.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
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