Encoding Spectral and Spatial Context Information for Hyperspectral Image Classification

	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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Encoding Spectral.pd（2197KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	浏览下载