A Novel Spectral-Spatial Singular Spectrum Analysis Technique for Near Real-Time In Situ Feature Extraction in Hyperspectral Imaging

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Hang Fu¹  Genyun Sun¹  Aizhu Zhang¹  Jinchang Ren²  Jaime Zabalza²  Xiuping Jia³ 

[1] College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, China;Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, U.K.;School of Engineering and Information Technology, University of New South Wales at Canberra, Canberra, ACT, Australia;

关键词: Feature extraction;    hyperspectral image (HSI);    near real-time;    singular spectrum analysis (SSA);    spectral-spatial;   

DOI  :  10.1109/JSTARS.2020.2992230

来源: DOAJ

【 摘 要 】

As a cutting-edge technique for denoising and feature extraction, singular spectrum analysis (SSA) has been applied successfully for feature mining in hyperspectral images (HSI). However, when applying SSA for in situ feature extraction in HSI, conventional pixel-based 1-D SSA fails to produce satisfactory results, while the band-image-based 2D-SSA is also infeasible especially for the popularly used line-scan mode. To tackle these challenges, in this article, a novel 1.5D-SSA approach is proposed for in situ spectral-spatial feature extraction in HSI, where pixels from a small window are used as spatial information. For each sequentially acquired pixel, similar pixels are located from a window centered at the pixel to form an extended trajectory matrix for feature extraction. Classification results on two well-known benchmark HSI datasets and an actual urban scene dataset have demonstrated that the proposed 1.5D-SSA achieves the superior performance compared with several state-of-the-art spectral and spatial methods. In addition, the near real-time implementation in aligning to the HSI acquisition process can meet the requirement of online image analysis for more efficient feature extraction than the conventional offline workflow.

【 授权许可】

Unknown