Global and Local Tensor Sparse Approximation Models for Hyperspectral Image Destriping

Xiangyang Kong,Jize Xue,Yongqiang Zhao,Seong G. Kong,Jonathan Cheung-Wai Chan

doi:10.3390/rs12040704

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 ℓ 0 -norm regularizers to the stripe components and along-stripe gradient to improve the destriping performance. Two ℓ 1 -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 effective and outperforms existing competitive matrix-based and tensor-based destriping methods in visual, as well as quantitative, evaluation measures.

Highlights

Hyperspectral images (HSIs) offer abundant spatial and spectral information useful in various application fields, including weather prediction [1] and environmental monitoring [2]
The first four methods belong to band-by-band restoration matrix-based methods, including the unidirectional total variation model (UTV) [14], Directional L0 Sparse (DL0S) Model [16], the wavelet Fourier adaptive filter (WFAF) [22], and statistical gain estimation (SGE) [23]
The codes of WFAF, UTV, Anisotropic Spectral–Spatial Total Variation (ASSTV), and Low-Rank Multispectral Image Decomposition (LRMID) can be downloaded from the website

Summary

Introduction

Hyperspectral images (HSIs) offer abundant spatial and spectral information useful in various application fields, including weather prediction [1] and environmental monitoring [2]. Hyperspectral images collected by using whisk-broom sensors and push-broom scanners can be degraded by stripes, often caused by calibration error or inconsistent responses between detectors [3]. Such stripes degrade visual quality and pose negative influence on subsequent processing, such as unmixing [4,5], super-resolution [6], classification [7,8], compressive sensing reconstruction [9,10], and recovery [11,12]. Three types of stripes exist: horizontal (row-by-row) [13,14], vertical (column-by-column) [15,16], and oblique stripes [17,18]. In spite of good results for a single band, matrix-based destriping methods ignore spectral correlation causing spectral distortion in the restored HSI

Methods

Results

Conclusion