A label propagation method using spatial-spectral consistency for hyperspectral image classification. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: Li, Haichang; Wang, Ying; Xiang, Shiming; Duan, Jiangyong; Zhu, Feiyun; Pan, Chunhong
• Is Part Of: International journal of remote sensing. Volume 37:Issue 1(2016)
• Page Start: 191
• Page End: 211
• Publication Date: 2016-01-02
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2015.1125547
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: 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.