24 March 2023 Remote sensing image destriping based on variable weight and group sparse regularization
Wenshuai Jiang, Zhenghua Huang, Kun Bai, Qiong Song
Author Affiliations +
Abstract

Striping effects are common phenomena in remote sensing images, and they significantly limit subsequent applications. Although many destriping approaches have been developed, there aren’t many that can completely eliminate complex stripes with varying levels of strength. To address this issue, we propose a stripe removal model based on a variable weight coefficient and group sparse regularization. Specifically, rather than a single scalar for the stripes in most approaches, different weights are set for different stripe rows to estimate the stripes with varying intensities. An adaptive method to estimate the weight matrix is proposed. On the other hand, group sparsity regularization is employed to constrain the entire stripe. In addition, region weights are designed for regions with different stripe characteristics. The alternating direction multiplier method is employed to solve the proposed model by alternating minimization. Experimental results based on simulation and real data demonstrate that the proposed model outperforms other advanced methods in terms of stripe noise removal and image detail preservation.

© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE)
Wenshuai Jiang, Zhenghua Huang, Kun Bai, and Qiong Song "Remote sensing image destriping based on variable weight and group sparse regularization," Journal of Applied Remote Sensing 17(1), 016516 (24 March 2023). https://doi.org/10.1117/1.JRS.17.016516
Received: 19 September 2022; Accepted: 6 March 2023; Published: 24 March 2023
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Cited by 1 scholarly publication.
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KEYWORDS
Remote sensing

MODIS

Matrices

Data modeling

Mathematical optimization

Tunable filters

Ultraviolet radiation

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