Encoding Spectral and Spatial Context Information for Hyperspectral Image Classification

CORC > 长春光学精密机械与物理研究所 > 中国科学院长春光学精密机械与物理研究所 > 中科院长春光机所知识产出

	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
刊名	Ieee Geoscience and Remote Sensing Letters
	2017
卷号	14 期号:12
英文摘要	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.
语种	英语
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/59207]
专题	长春光学精密机械与物理研究所_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).