Picture of person typing on laptop with programming code visible on the laptop screen

World class computing and information science research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

The Department also includes the iSchool Research Group, which performs leading research into socio-technical phenomena and topics such as information retrieval and information seeking behaviour.


Fast implementation of singular spectrum analysis for effective feature extraction in hyperspectral imaging

Zabalza, Jaime and Ren, Jinchang and Wang, Zheng and Zhao, Huimin and Wang, Jun and Marshall, Stephen (2015) Fast implementation of singular spectrum analysis for effective feature extraction in hyperspectral imaging. IEEE Journal of Selected Topics in Earth Observation and Remote Sensing, 8 (6). pp. 2845-2853. ISSN 1939-1404

Text (Zabalza-etal-JSTEORS2014-effective-feature-extraction-in-hyperspectral-imaging)
Zabalza_etal_JSTEORS2014_effective_feature_extraction_in_hyperspectral_imaging.pdf - Accepted Author Manuscript

Download (1MB) | Preview


As a recent approach for time series analysis, singular spectrum analysis (SSA) has been successfully applied for feature extraction in hyperspectral imaging (HSI), leading to increased accuracy in pixel-based classification tasks. However, one of the main drawbacks of conventional SSA in HSI is the extremely high computational complexity, where each pixel requires individual and complete singular value decomposition (SVD) analyses. To address this issue, a fast implementation of SSA (F-SSA) is proposed for efficient feature extraction in HSI. Rather than applying pixel-based SVD as conventional SSA does, the fast implementation only needs one SVD applied to a representative pixel, i.e., either the median or the mean spectral vector of the HSI hypercube. The result of SVD is employed as a unique transform matrix for all the pixels within the hypercube. As demonstrated in experiments using two well-known publicly available data sets, almost identical results are produced by the fast implementation in terms of accuracy of data classification, using the support vector machine (SVM) classifier. However, the overall computational complexity has been significantly reduced.