DOI: 10.1016/j.jag.2016.06.006
Scopus记录号: 2-s2.0-84997719738
论文题名: Generalization of spectral fidelity with flexible measures for the sparse representation classification of hyperspectral images
作者: Wu B ; , Zhu Y ; , Huang X ; , Li J
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 52 起始页码: 275
结束页码: 283
语种: 英语
英文关键词: Hyperspectral image
; Sparse representation classification
; Spectral similarity measures
; Unified framework
Scopus关键词: image classification
; optimization
; spectral analysis
英文摘要: Sparse representation classification (SRC) is becoming a promising tool for hyperspectral image (HSI) classification, where the Euclidean spectral distance (ESD) is widely used to reflect the fidelity between the original and reconstructed signals. In this paper, a generalized model is proposed to extend SRC by characterizing the spectral fidelity with flexible similarity measures. To validate the flexibility, several typical similarity measures—the spectral angle similarity (SAS), spectral information divergence (SID), the structural similarity index measure (SSIM), and the ESD—are included in the generalized model. Furthermore, a general solution based on a gradient descent technique is used to solve the nonlinear optimization problem formulated by the flexible similarity measures. To test the generalized model, two actual HSIs were used, and the experimental results confirm the ability of the proposed model to accommodate the various spectral similarity measures. Performance comparisons with the ESD, SAS, SID, and SSIM criteria were also conducted, and the results consistently show the advantages of the generalized model for HSI classification in terms of overall accuracy and kappa coefficient. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80013
Appears in Collections: 气候变化事实与影响
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作者单位: Key laboratory of Poyang lake wetland and watershed research, ministry of education, Jiangxi normal university, Nanchang, China; Spatial Information Research Center of Fujian, Fuzhou University, Fuzhou, Fujian, China; School of Remote Sensing and Information Engineering, Wuhan University 129, Luoyu road, Wuhan, China
Recommended Citation:
Wu B,, Zhu Y,, Huang X,et al. Generalization of spectral fidelity with flexible measures for the sparse representation classification of hyperspectral images[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,52