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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
发表期刊Ieee Geoscience and Remote Sensing Letters
卷号14期号:12
摘要Hyperspectral 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.
收录类别sci ; ei
语种英语
文献类型期刊论文
条目标识符http://ir.ciomp.ac.cn/handle/181722/59207
专题中科院长春光机所知识产出
推荐引用方式
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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