Hyperspectral subspace estimation preserving anomalies via a test of multivariate sample skewness

Siyuan Zheng; Shilin Zhou; Liangliang Wang; Zhiyong Li

doi:10.1117/12.2033568

30 August 2013 Hyperspectral subspace estimation preserving anomalies via a test of multivariate sample skewness

Siyuan Zheng, Shilin Zhou, Liangliang Wang, Zhiyong Li

Proceedings Volume 8910, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications; 891013 (2013) https://doi.org/10.1117/12.2033568
Event: ISPDI 2013 - Fifth International Symposium on Photoelectronic Detection and Imaging, 2013, Beijing, China

Abstract

Dimensionality Reduction (DR) for hyperspectral image data can be regarded as a problem of signal subspace estimation (SSE) in terms of the Linear Mixing Model (LMM). Most SSE methods for hyperspectral data are based on the analysis of second-order statistics (SOS) without considering preservation of anomalies. This paper addresses the problem of SSE for preserving both abundant and rare signal components in hyperspectral images. The multivariate sample skewness for testing normality is brought in our new algorithm as a discrimination index for rank determination of rare vectors subspace, combining with analysis of the maximum of data-residual ℓ₂-norm denoted as ℓ_2,∞-norm which is strongly influenced by the anomaly signal components. And the SOS based method, labeled as hyperspectral signal subspace identification by minimum error (HySime), is employed for identification of abundant vectors space. The results of experiments on real AVIRIS data prove that multivariate sample skewness statistics is suitable for measuring the distribution about hyperspectral data globally, and our algorithm can obtain the anomaly components from data that are discarded by HySime, which implies less information loss in the our method.

Citation Download Citation

Siyuan Zheng, Shilin Zhou, Liangliang Wang, and Zhiyong Li "Hyperspectral subspace estimation preserving anomalies via a test of multivariate sample skewness", Proc. SPIE 8910, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications, 891013 (30 August 2013); https://doi.org/10.1117/12.2033568

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KEYWORDS

Hyperspectral imaging

Statistical analysis

Data modeling

Interference (communication)

Error analysis

Principal component analysis

Data analysis

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