Better multi-sensor image fusion method in remote sensing

Xiaoxiang Liu; Chongyang Hao; Guiqing He; Wei Feng; Yangyu Fan

Better multi-sensor image fusion method in remote sensing

Xiaoxiang Liu, Chongyang Hao, Guiqing He, Wei Feng, Yangyu Fan

School of Electronics and Information

Northwestern Polytechnical University Xian

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

We analyzed SFIM (smoothing filter-based intensity modulation) fusion technique proposed by Liu in 2000^[4] and came to forming the opinion that SFIM can be improved in two respects: (l) combining SFIM with IHS(intensity, hue, saturation) fusion technique; (2) smoothing filter different from that used by Liu^[4]. In this paper we explain in much detail how to combine the two multi-sensor image fusion techniques and also discuss in some detail the design of the smoothing filter - called by us the adaptively weighted mean filter - we propose. We performed simulation tests that fused multispectral images and panchromatic images with IHS, SFIM and our improved SFIM fusion techniques respectively. Simulation results show preliminarily that our improved SFIM method is better than IHS or SFIM method and can get excellent spatial resolution while preserving the spectral information of multispectral images.

Original language	English
Pages (from-to)	761-764
Number of pages	4
Journal	Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University
Volume	22
Issue number	6
State	Published - Dec 2004

Keywords

Adaptively weighted mean filter
IHS (intensity, hue, saturation) method
Image fusion
Smoothing filter-based intensity modulation method

Cite this

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title = "Better multi-sensor image fusion method in remote sensing",

abstract = "We analyzed SFIM (smoothing filter-based intensity modulation) fusion technique proposed by Liu in 2000[4] and came to forming the opinion that SFIM can be improved in two respects: (l) combining SFIM with IHS(intensity, hue, saturation) fusion technique; (2) smoothing filter different from that used by Liu[4]. In this paper we explain in much detail how to combine the two multi-sensor image fusion techniques and also discuss in some detail the design of the smoothing filter - called by us the adaptively weighted mean filter - we propose. We performed simulation tests that fused multispectral images and panchromatic images with IHS, SFIM and our improved SFIM fusion techniques respectively. Simulation results show preliminarily that our improved SFIM method is better than IHS or SFIM method and can get excellent spatial resolution while preserving the spectral information of multispectral images.",

keywords = "Adaptively weighted mean filter, IHS (intensity, hue, saturation) method, Image fusion, Smoothing filter-based intensity modulation method",

author = "Xiaoxiang Liu and Chongyang Hao and Guiqing He and Wei Feng and Yangyu Fan",

year = "2004",

month = dec,

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TY - JOUR

T1 - Better multi-sensor image fusion method in remote sensing

AU - Liu, Xiaoxiang

AU - Hao, Chongyang

AU - He, Guiqing

AU - Feng, Wei

AU - Fan, Yangyu

PY - 2004/12

Y1 - 2004/12

N2 - We analyzed SFIM (smoothing filter-based intensity modulation) fusion technique proposed by Liu in 2000[4] and came to forming the opinion that SFIM can be improved in two respects: (l) combining SFIM with IHS(intensity, hue, saturation) fusion technique; (2) smoothing filter different from that used by Liu[4]. In this paper we explain in much detail how to combine the two multi-sensor image fusion techniques and also discuss in some detail the design of the smoothing filter - called by us the adaptively weighted mean filter - we propose. We performed simulation tests that fused multispectral images and panchromatic images with IHS, SFIM and our improved SFIM fusion techniques respectively. Simulation results show preliminarily that our improved SFIM method is better than IHS or SFIM method and can get excellent spatial resolution while preserving the spectral information of multispectral images.

AB - We analyzed SFIM (smoothing filter-based intensity modulation) fusion technique proposed by Liu in 2000[4] and came to forming the opinion that SFIM can be improved in two respects: (l) combining SFIM with IHS(intensity, hue, saturation) fusion technique; (2) smoothing filter different from that used by Liu[4]. In this paper we explain in much detail how to combine the two multi-sensor image fusion techniques and also discuss in some detail the design of the smoothing filter - called by us the adaptively weighted mean filter - we propose. We performed simulation tests that fused multispectral images and panchromatic images with IHS, SFIM and our improved SFIM fusion techniques respectively. Simulation results show preliminarily that our improved SFIM method is better than IHS or SFIM method and can get excellent spatial resolution while preserving the spectral information of multispectral images.

KW - Adaptively weighted mean filter

KW - IHS (intensity, hue, saturation) method

KW - Image fusion

KW - Smoothing filter-based intensity modulation method

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JF - Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University

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ER -

Better multi-sensor image fusion method in remote sensing

Abstract

Keywords

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