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Encoding Spectral and Spatial Context Information for Hyperspectral Image Classification
Sun, X.; F. Zhou; J. Y. Dong; F. Gao; Q. Q. Mu and X. H. Wang
2017
Source PublicationIeee Geoscience and Remote Sensing Letters
Volume14Issue:12
AbstractHyperspectral image (HSI) classification is a popular yet challenging research topic in the remote sensing community. This letter attempts to encode both spectral and spatial information into deep features for HSI classification. We first propose a semisupervised method for training the stacked autoencoder to obtain discriminative deep features. A batch training scheme is introduced to constrain the label consistency on a neighborhood region. Second, a mean pooling procedure is suggested to further fuse the spectral and local spatial information for deep feature generation. The experimental results on two hyperspectral scenes show that the proposed method achieves promising classification performance.
Indexed Bysci ; ei
Language英语
Document Type期刊论文
Identifierhttp://ir.ciomp.ac.cn/handle/181722/59207
Collection中科院长春光机所知识产出
Recommended Citation
GB/T 7714
Sun, X.,F. Zhou,J. Y. Dong,et al. Encoding Spectral and Spatial Context Information for Hyperspectral Image Classification[J]. Ieee Geoscience and Remote Sensing Letters,2017,14(12).
APA Sun, X.,F. Zhou,J. Y. Dong,F. Gao,&Q. Q. Mu and X. H. Wang.(2017).Encoding Spectral and Spatial Context Information for Hyperspectral Image Classification.Ieee Geoscience and Remote Sensing Letters,14(12).
MLA Sun, X.,et al."Encoding Spectral and Spatial Context Information for Hyperspectral Image Classification".Ieee Geoscience and Remote Sensing Letters 14.12(2017).
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