Non-subsampled shearlet transform remote sensing image fusion combined with parameter-adaptive PCNN

In order to solve the problem that the parameters of pulse-coupled neural network can't be adjusted adaptively in pan-sharpening image fusion, a non-subsampled shearlet transform remote sensing image fusion method based on the combination of parametric-adaptive pulse coupled neural network mode...

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Autores principales:	CHENG Feifei, FU Zhitao, HUANG Liang, CHEN Pengdi, HUANG Kun
Formato:	article
Lenguaje:	ZH
Publicado:	Surveying and Mapping Press 2021
Materias:	image fusion non-subsampled shearlet transform parameter-adaptive pulse-coupled neural network panchromatic image multispectral image Mathematical geography. Cartography GA1-1776
Acceso en línea:	https://doaj.org/article/b3b5ceb1c43341599dc84f3c4fbfe817
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id	oai:doaj.org-article:b3b5ceb1c43341599dc84f3c4fbfe817
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spelling	oai:doaj.org-article:b3b5ceb1c43341599dc84f3c4fbfe8172021-11-12T02:25:59ZNon-subsampled shearlet transform remote sensing image fusion combined with parameter-adaptive PCNN1001-159510.11947/j.AGCS.2021.20200589https://doaj.org/article/b3b5ceb1c43341599dc84f3c4fbfe8172021-10-01T00:00:00Zhttp://xb.sinomaps.com/article/2021/1001-1595/2021-10-1380.htmhttps://doaj.org/toc/1001-1595In order to solve the problem that the parameters of pulse-coupled neural network can't be adjusted adaptively in pan-sharpening image fusion, a non-subsampled shearlet transform remote sensing image fusion method based on the combination of parametric-adaptive pulse coupled neural network model and energy-attributing fusion strategy is proposed. First, the high and low frequency coefficients are obtained by extracting the Y luminance component of the multispectral image YUV color space transform and transforming it with the panchromatic image. Then, aiming at the low-frequency sub-band coefficients are fused by the EA method, the high-frequency sub-band coefficients are obtained by the PA-PCNN model to determine the optimal PCNN model, and then the high-frequency sub-band coefficients are fused; finally, the fusion image is obtained by inverse transformation of NSST and YUV. In this paper, six objective quality indexes, such as spatial frequency, relative dimensionless global error, ERGAS, correlation coefficient, visual information fidelity for fusion, gradient-based fusion performance and structural similarity index, are selected to evaluate the spectral and spatial detail information of the fused images, compared with SE, DGIF, COF and PA-PCNN fusion methods, the proposed method is validated by using multiple sets of high-and low-resolution panchromatic and multispectral remote sensing images, the results show that this method is generally superior to the traditional fusion method of panchromatic and multispectral remote sensing images in objective evaluation and visual perception.CHENG FeifeiFU ZhitaoHUANG LiangCHEN PengdiHUANG KunSurveying and Mapping Pressarticleimage fusionnon-subsampled shearlet transformparameter-adaptive pulse-coupled neural networkpanchromatic imagemultispectral imageMathematical geography. CartographyGA1-1776ZHActa Geodaetica et Cartographica Sinica, Vol 50, Iss 10, Pp 1380-1389 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	image fusion non-subsampled shearlet transform parameter-adaptive pulse-coupled neural network panchromatic image multispectral image Mathematical geography. Cartography GA1-1776
spellingShingle	image fusion non-subsampled shearlet transform parameter-adaptive pulse-coupled neural network panchromatic image multispectral image Mathematical geography. Cartography GA1-1776 CHENG Feifei FU Zhitao HUANG Liang CHEN Pengdi HUANG Kun Non-subsampled shearlet transform remote sensing image fusion combined with parameter-adaptive PCNN
description	In order to solve the problem that the parameters of pulse-coupled neural network can't be adjusted adaptively in pan-sharpening image fusion, a non-subsampled shearlet transform remote sensing image fusion method based on the combination of parametric-adaptive pulse coupled neural network model and energy-attributing fusion strategy is proposed. First, the high and low frequency coefficients are obtained by extracting the Y luminance component of the multispectral image YUV color space transform and transforming it with the panchromatic image. Then, aiming at the low-frequency sub-band coefficients are fused by the EA method, the high-frequency sub-band coefficients are obtained by the PA-PCNN model to determine the optimal PCNN model, and then the high-frequency sub-band coefficients are fused; finally, the fusion image is obtained by inverse transformation of NSST and YUV. In this paper, six objective quality indexes, such as spatial frequency, relative dimensionless global error, ERGAS, correlation coefficient, visual information fidelity for fusion, gradient-based fusion performance and structural similarity index, are selected to evaluate the spectral and spatial detail information of the fused images, compared with SE, DGIF, COF and PA-PCNN fusion methods, the proposed method is validated by using multiple sets of high-and low-resolution panchromatic and multispectral remote sensing images, the results show that this method is generally superior to the traditional fusion method of panchromatic and multispectral remote sensing images in objective evaluation and visual perception.
format	article
author	CHENG Feifei FU Zhitao HUANG Liang CHEN Pengdi HUANG Kun
author_facet	CHENG Feifei FU Zhitao HUANG Liang CHEN Pengdi HUANG Kun
author_sort	CHENG Feifei
title	Non-subsampled shearlet transform remote sensing image fusion combined with parameter-adaptive PCNN
title_short	Non-subsampled shearlet transform remote sensing image fusion combined with parameter-adaptive PCNN
title_full	Non-subsampled shearlet transform remote sensing image fusion combined with parameter-adaptive PCNN
title_fullStr	Non-subsampled shearlet transform remote sensing image fusion combined with parameter-adaptive PCNN
title_full_unstemmed	Non-subsampled shearlet transform remote sensing image fusion combined with parameter-adaptive PCNN
title_sort	non-subsampled shearlet transform remote sensing image fusion combined with parameter-adaptive pcnn
publisher	Surveying and Mapping Press
publishDate	2021
url	https://doaj.org/article/b3b5ceb1c43341599dc84f3c4fbfe817
work_keys_str_mv	AT chengfeifei nonsubsampledshearlettransformremotesensingimagefusioncombinedwithparameteradaptivepcnn AT fuzhitao nonsubsampledshearlettransformremotesensingimagefusioncombinedwithparameteradaptivepcnn AT huangliang nonsubsampledshearlettransformremotesensingimagefusioncombinedwithparameteradaptivepcnn AT chenpengdi nonsubsampledshearlettransformremotesensingimagefusioncombinedwithparameteradaptivepcnn AT huangkun nonsubsampledshearlettransformremotesensingimagefusioncombinedwithparameteradaptivepcnn
_version_	1718431310670725120

Non-subsampled shearlet transform remote sensing image fusion combined with parameter-adaptive PCNN

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