A hybrid pan-sharpening approach using nonnegative matrix factorization for worldview imageries

Guiqing He; Jiaqi Ji; Qiqi Zhang; Zhaoqiang Xia

doi:10.1007/978-3-030-31723-2_60

A hybrid pan-sharpening approach using nonnegative matrix factorization for worldview imageries

Guiqing He, Jiaqi Ji, Qiqi Zhang, Zhaoqiang Xia

School of Electronics and Information

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

With the advent of WorldView series imageries (WorldView-2/3/4), it is necessary to develop new fusion approaches for remote sensing images with higher spatial and spectral resolutions. Since most existing fusion approaches are not well capable of merging multi-spectral images with eight bands, a new hybrid pan-sharpening approach is proposed in this paper. The hybrid framework integrates the multiplicative model and additive model to improve the quality of multi-spectral images. In the additive procedure, the nonnegative matrix factorization (NMF) algorithm is utilized to synthesize the intensity component for obtaining the mutual information from multi-spectral images. Then the difference information between the panchromatic image and synthetic component is injected into multi-spectral images by the spectral-adjustable weights. In the multiplicative procedure, the smoothing filter-based intensity modulation (SFIM) is used to modulate the preliminary fusion. The nonlinear fitting method is utilized to calculate the optimal parameters of the hybrid model. Visual and quantitative assessments of fused images show that the proposed approach clearly improves the fusion quality compared to the state-of-the-art algorithms.

Original language	English
Title of host publication	Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II
Editors	Zhouchen Lin, Liang Wang, Tieniu Tan, Jian Yang, Guangming Shi, Nanning Zheng, Xilin Chen, Yanning Zhang
Publisher	Springer
Pages	703-713
Number of pages	11
ISBN (Print)	9783030317225
DOIs	https://doi.org/10.1007/978-3-030-31723-2_60
State	Published - 2019
Event	2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019 - Xi'an, China Duration: 8 Nov 2019 → 11 Nov 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11858 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019
Country/Territory	China
City	Xi'an
Period	8/11/19 → 11/11/19

Keywords

Image fusion
Nonlinear fitting
Nonnegative matrix factorization
Smoothing filter-based intensity modulation
Spectral-adjustable weight

Access to Document

10.1007/978-3-030-31723-2_60

Cite this

He, G., Ji, J., Zhang, Q., & Xia, Z. (2019). A hybrid pan-sharpening approach using nonnegative matrix factorization for worldview imageries. In Z. Lin, L. Wang, T. Tan, J. Yang, G. Shi, N. Zheng, X. Chen, & Y. Zhang (Eds.), Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II (pp. 703-713). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11858 LNCS). Springer. https://doi.org/10.1007/978-3-030-31723-2_60

He, Guiqing ; Ji, Jiaqi ; Zhang, Qiqi et al. / A hybrid pan-sharpening approach using nonnegative matrix factorization for worldview imageries. Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II. editor / Zhouchen Lin ; Liang Wang ; Tieniu Tan ; Jian Yang ; Guangming Shi ; Nanning Zheng ; Xilin Chen ; Yanning Zhang. Springer, 2019. pp. 703-713 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{1ed0e0340468450bb7c5719ca2c41c6e,

title = "A hybrid pan-sharpening approach using nonnegative matrix factorization for worldview imageries",

abstract = "With the advent of WorldView series imageries (WorldView-2/3/4), it is necessary to develop new fusion approaches for remote sensing images with higher spatial and spectral resolutions. Since most existing fusion approaches are not well capable of merging multi-spectral images with eight bands, a new hybrid pan-sharpening approach is proposed in this paper. The hybrid framework integrates the multiplicative model and additive model to improve the quality of multi-spectral images. In the additive procedure, the nonnegative matrix factorization (NMF) algorithm is utilized to synthesize the intensity component for obtaining the mutual information from multi-spectral images. Then the difference information between the panchromatic image and synthetic component is injected into multi-spectral images by the spectral-adjustable weights. In the multiplicative procedure, the smoothing filter-based intensity modulation (SFIM) is used to modulate the preliminary fusion. The nonlinear fitting method is utilized to calculate the optimal parameters of the hybrid model. Visual and quantitative assessments of fused images show that the proposed approach clearly improves the fusion quality compared to the state-of-the-art algorithms.",

keywords = "Image fusion, Nonlinear fitting, Nonnegative matrix factorization, Smoothing filter-based intensity modulation, Spectral-adjustable weight",

author = "Guiqing He and Jiaqi Ji and Qiqi Zhang and Zhaoqiang Xia",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019 ; Conference date: 08-11-2019 Through 11-11-2019",

year = "2019",

doi = "10.1007/978-3-030-31723-2_60",

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isbn = "9783030317225",

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pages = "703--713",

editor = "Zhouchen Lin and Liang Wang and Tieniu Tan and Jian Yang and Guangming Shi and Nanning Zheng and Xilin Chen and Yanning Zhang",

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}

He, G, Ji, J, Zhang, Q & Xia, Z 2019, A hybrid pan-sharpening approach using nonnegative matrix factorization for worldview imageries. in Z Lin, L Wang, T Tan, J Yang, G Shi, N Zheng, X Chen & Y Zhang (eds), Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11858 LNCS, Springer, pp. 703-713, 2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019, Xi'an, China, 8/11/19. https://doi.org/10.1007/978-3-030-31723-2_60

A hybrid pan-sharpening approach using nonnegative matrix factorization for worldview imageries. / He, Guiqing; Ji, Jiaqi; Zhang, Qiqi et al.
Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II. ed. / Zhouchen Lin; Liang Wang; Tieniu Tan; Jian Yang; Guangming Shi; Nanning Zheng; Xilin Chen; Yanning Zhang. Springer, 2019. p. 703-713 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11858 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A hybrid pan-sharpening approach using nonnegative matrix factorization for worldview imageries

AU - He, Guiqing

AU - Ji, Jiaqi

AU - Zhang, Qiqi

AU - Xia, Zhaoqiang

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - With the advent of WorldView series imageries (WorldView-2/3/4), it is necessary to develop new fusion approaches for remote sensing images with higher spatial and spectral resolutions. Since most existing fusion approaches are not well capable of merging multi-spectral images with eight bands, a new hybrid pan-sharpening approach is proposed in this paper. The hybrid framework integrates the multiplicative model and additive model to improve the quality of multi-spectral images. In the additive procedure, the nonnegative matrix factorization (NMF) algorithm is utilized to synthesize the intensity component for obtaining the mutual information from multi-spectral images. Then the difference information between the panchromatic image and synthetic component is injected into multi-spectral images by the spectral-adjustable weights. In the multiplicative procedure, the smoothing filter-based intensity modulation (SFIM) is used to modulate the preliminary fusion. The nonlinear fitting method is utilized to calculate the optimal parameters of the hybrid model. Visual and quantitative assessments of fused images show that the proposed approach clearly improves the fusion quality compared to the state-of-the-art algorithms.

AB - With the advent of WorldView series imageries (WorldView-2/3/4), it is necessary to develop new fusion approaches for remote sensing images with higher spatial and spectral resolutions. Since most existing fusion approaches are not well capable of merging multi-spectral images with eight bands, a new hybrid pan-sharpening approach is proposed in this paper. The hybrid framework integrates the multiplicative model and additive model to improve the quality of multi-spectral images. In the additive procedure, the nonnegative matrix factorization (NMF) algorithm is utilized to synthesize the intensity component for obtaining the mutual information from multi-spectral images. Then the difference information between the panchromatic image and synthetic component is injected into multi-spectral images by the spectral-adjustable weights. In the multiplicative procedure, the smoothing filter-based intensity modulation (SFIM) is used to modulate the preliminary fusion. The nonlinear fitting method is utilized to calculate the optimal parameters of the hybrid model. Visual and quantitative assessments of fused images show that the proposed approach clearly improves the fusion quality compared to the state-of-the-art algorithms.

KW - Image fusion

KW - Nonlinear fitting

KW - Nonnegative matrix factorization

KW - Smoothing filter-based intensity modulation

KW - Spectral-adjustable weight

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DO - 10.1007/978-3-030-31723-2_60

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SN - 9783030317225

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II

A2 - Lin, Zhouchen

A2 - Wang, Liang

A2 - Tan, Tieniu

A2 - Yang, Jian

A2 - Shi, Guangming

A2 - Zheng, Nanning

A2 - Chen, Xilin

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PB - Springer

T2 - 2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019

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ER -

He G, Ji J, Zhang Q, Xia Z. A hybrid pan-sharpening approach using nonnegative matrix factorization for worldview imageries. In Lin Z, Wang L, Tan T, Yang J, Shi G, Zheng N, Chen X, Zhang Y, editors, Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II. Springer. 2019. p. 703-713. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-31723-2_60

A hybrid pan-sharpening approach using nonnegative matrix factorization for worldview imageries

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