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Journal Publication

Global and Local Tensor Sparse Approximation Models for Hyperspectral Image Destriping

This publication appears in: Remote Sensing

Authors: X. Kong, Y. Zhao, J. Xue, J. C-W Chan and S. G. Kong

Volume: 12

Issue: 4

Publication Date: Feb. 2020


Abstract:

This paper presents a global and local tensor sparse approximation (GLTSA) model for removing the stripes in hyperspectral images (HSIs). HSIs can easily be degraded by unwanted stripes. Two intrinsic characteristics of the stripes are (1) global sparse distribution and (2) local smoothness along the stripe direction. Stripe-free hyperspectral images are smooth in spatial domain, with strong spectral correlation. Existing destriping approaches often do not fully investigate such intrinsic characteristics of the stripes in spatial and spectral domains simultaneously. Those methods may generate new artifacts in extreme areas, causing spectral distortion. The proposed GLTSA model applies two l0-norm regularizers to the stripe components and along-stripe gradient to improve the destriping performance. Two l1-norm regularizers are applied to the gradients of clean image in spatial and spectral domains. The double non-convex functions in GLTSA are converted to single non-convex function by mathematical program with equilibrium constraints (MPEC). Experiment results demonstrate that GLTSA is eective and outperforms existing competitive matrix-based and tensor-based destriping methods in visual, as well as quantitative, evaluation measures.

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Prof. Dr. Jonathan C-W Chan

+32 (0)02 629 128

jcheungw@etrovub.be

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