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Tensor-patch-based discriminative marginalized least squares regression for membranous nephropathy hyperspectral data classification
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Paper Abstract

Least squares regression (LSR)-based classifiers are effective in multi-classification tasks. For hyperspectral image (HSI) classification, the spatial structure information usually helps to improve the performance, however, most existing LSRbased methods use the spectral-vector as input which ignore the important correlations in the spatial domain. To solve the drawback, a tensor-patch-based discriminative marginalized least squares regression (TPDMLSR) is proposed to modify discriminative marginalized least squares regression (DMLSR) with consideration of inter-class separability by employing the region covariance matrix (RCM). RCM is adopted to exploit a region of interest around each hyperspectral pixel to characterize the intrinsic spatial geometric structure of HSI. Specifically, TPDMLSR not only maintains the ascendancy of DMLSR, but also preserves the spatial-spectral structure and enhances the ability of class discrimination for regression by learning the tensor-patch manifold term with a new region covariance descriptor and measuring the inter-class similarity more accurately. The experimental results on membranous nephropathy (MN) dataset validate that TPDMLSR significantly outperforms LSR-based methods reflected in sensitivity, overall accuracy (OA), average accuracy (AA) and Kappa coefficient (Kappa).

Paper Details

Date Published: 6 August 2021
PDF: 8 pages
Proc. SPIE 11913, Sixth International Workshop on Pattern Recognition, 119130A (6 August 2021);
Show Author Affiliations
Tianhong Chen, Beijing Institute of Technology (China)
Meng Lv, Beijing Institute of Technology (China)
Yue Yang, China-Japan Friendship Hospital (China)
Tianqi Tu, China-Japan Friendship Hospital (China)
Wei Li, Beijing Institute of Technology (China)
Wenge Li, China-Japan Friendship Hospital (China)


Published in SPIE Proceedings Vol. 11913:
Sixth International Workshop on Pattern Recognition
Xudong Jiang; Li Tan; Tieling Chen; Guojian Chen, Editor(s)

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