Knowledge Commons of Institute of Automation,CAS
A label propagation method using spatial-spectral consistency for hyperspectral image classification | |
Haichang Li; Ying Wang; Shiming Xiang; Jiangyong Duan; Feiyun Zhu; Chunhong Pan; haichang516@gmail.com | |
发表期刊 | International Journal of Remote Sensing |
2016-01-04 | |
卷号 | 37期号:1页码:191-211 |
摘要 |
In this article, a label propagation approach with automatic seed selection is developed for hyperspectral image classification. The core idea is to combine pixel-wise classification results with spatial information described by a data graph. Using only the support vector machine (SVM) classifier on spectral features to tackle the hyperspectral classification task will produce results with a salt and pepper appearance. To overcome this limitation, the spatial information is incorporated by label propagation. The performance of label propagation is dependent on two points: the seeds and the connection graph. Generally, a limited number of labelled samples are available, which are considered as seeds in label propagation. However, the limited seeds will result in bad label propagation. Therefore, pseudo-seeds are automatically selected in local windows. Specifically, the pixels whose initial labels according to SVM are consistent with their most spatial neighbours are selected as seeds. Through seed selection, the number of seeds is greatly increased. Then, the label information of the selected seeds is propagated to their spatial neighbours using a data graph which is constructed according to the local structures in the image. Through seed selection and label propagation
on the graph, the problem of salt-and-pepper appearance is solved elegantly – the noisy labels are highly suppressed and most of the structures are preserved. Competitive experimental results on a variety of hyperspectral data sets demonstrate the effectiveness of the proposed method. |
关键词 | Hyperspectral Image Classification Label Propagation Spatial-spectral. |
WOS研究方向 | Remote Sensing ; Image Science & Photographic Technology |
WOS类目 | Remote Sensing ; Image Science & Photographic Technology |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/11123 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
通讯作者 | haichang516@gmail.com |
作者单位 | National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Haichang Li,Ying Wang,Shiming Xiang,et al. A label propagation method using spatial-spectral consistency for hyperspectral image classification[J]. International Journal of Remote Sensing,2016,37(1):191-211. |
APA | Haichang Li.,Ying Wang.,Shiming Xiang.,Jiangyong Duan.,Feiyun Zhu.,...&haichang516@gmail.com.(2016).A label propagation method using spatial-spectral consistency for hyperspectral image classification.International Journal of Remote Sensing,37(1),191-211. |
MLA | Haichang Li,et al."A label propagation method using spatial-spectral consistency for hyperspectral image classification".International Journal of Remote Sensing 37.1(2016):191-211. |
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