Unsupervised Spectral-Spatial Feature Learning With Stacked Sparse Autoencoder for Hyperspectral Imagery Classification

In this letter, different from traditional methods using original spectral features or handcraft spectral-spatial features, we propose to adaptively learn a suitable feature representation from unlabeled data. This is achieved by learning a feature mapping function based on stacked sparse autoencode...

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Vydáno v:	IEEE geoscience and remote sensing letters Ročník 12; číslo 12; s. 2438 - 2442
Hlavní autoři:	Tao, Chao, Pan, Hongbo, Li, Yansheng, Zou, Zhengrou
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.12.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Classification Feature extraction Hyperspectral imagery (HSI) classification Hyperspectral imaging Image acquisition Image classification Learning linear support vector machine Remote sensing Representations Spectra spectral-spatial feature learning stacked sparse autoencoder (SSAE) Support vector machines Training Training data spectral–spatial feature learning stacked sparse autoencoder (SSAE) linear support vector machine Hyperspectral imagery (HSI) classification
ISSN:	1545-598X, 1558-0571
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Shrnutí:	In this letter, different from traditional methods using original spectral features or handcraft spectral-spatial features, we propose to adaptively learn a suitable feature representation from unlabeled data. This is achieved by learning a feature mapping function based on stacked sparse autoencoder. Considering that hyperspectral imagery (HSI) is intrinsically defined in both the spectral and spatial domains, we further establish two variants of feature learning procedures for sparse spectral feature learning and multiscale spatial feature learning. Finally, we embed the learned spectral-spatial feature into a linear support vector machine for classification. Experiments on two hyperspectral images indicate the following: 1) the learned spectral-spatial feature representation is more discriminative for HSI classification compared to previously hand-engineered spectral-spatial features, especially when the training data are limited and 2) the learned features appear not to be specific to a particular image but general in that they are applicable to multiple related images (e.g., images acquired by the same sensor but varying with location or time).
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1545-598X 1558-0571
DOI:	10.1109/LGRS.2015.2482520