Feature extraction method based on spectral dimensional edge preservation filtering for hyperspectral image classification. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Li, Zhijian; Zhu, Qing; Wang, Yaonan; Zhang, Zhenjun; Zhou, Xianen; Lin, Anping; Fan, Jingmin
• Is Part Of: International journal of remote sensing. Volume 41:Issue 1(2020)
• Page Start: 90
• Page End: 113
• Publication Date: 2020-01-02
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2019.1635723
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: A large amount of spectral and spatial information contained in hyperspectral imagery has provided a great opportunity to effectively characterize and identify the surface materials of interest. Feature extraction plays a very important role for hyperspectral data classification, which can reduce noise from the original data and improve the separability of land classes. A novel feature extraction technique based on spectral dimensional edge preserving filter is proposed in this paper. A series of Gaussian filters are applied in the spatial domain of the hyperspectral image to produce the guidance image, then, the edge preserving filter which is guided by the guidance image is adopted and applied in the spectral domain of the hyperspectral data to get the feature. For the feature is produced by filtering in the spectral domain, the spectral curves of the feature are more continues, which avoids the spectral discontinuity problems result from the traditional two-dimensional spatial filter. The guidance image is obtained by filtering the original image in the spatial domain, so, the spatial and the spectral information are integrated together in the following spectral edge preserving filtering process. We carefully adjusted the parameters of the filter and applied it to different real hyperspectral remote sensing images, with the support vector machine, multinomial logistic regression, and random forest serving as the classifier, by comparing with other feature extraction methods presented in recent literature, the results indicate that the proposed methodology always has a great performance in different kinds of cases.