基于Curvelet变换联合灰度测算因子的遥感图像融合算法
Remote Sensing Image Fusion Algorithm Based on Curvelet Transform and Grayscale Measurement Factor
杨巧曼1
作者信息
- 1. 陕西交通职业技术学院 西安市 710018
- 折叠
摘要
该文在遥感图像融合研究中,为获取具有良好光谱特征与细节突出的遥感图像,利用Curvelet变换设计灰度测算因子.首先通过HSV变换剥离出多光谱图像的亮度分量V;采用Curvelet变换获取图像系数;接着利用图像的区域方差和均值融合低频系数,利用高频系数的灰度特征构造灰度测算因子融合高频系数;最后通过Curvelet反变换求取V分量的更新值(V),求取融合遥感图像.实验结果显示:该文给出的低频系数融合因子可获得光谱特征较好的融合低频系数;研究的灰度测算因子对求取细节丰富的融合高频系数有较好的效果.
Abstract
In order to obtain remote sensing images with good spectral features and prominent details,this paper designs a grayscale measurement factor based on the Curvelet transform for remote sensing im-age fusion.Firstly,the brightness component V is extracted from the multispectral image through HSV(Hue,Saturation,Value)transformation.Secondly,the image coefficient is obtained by using the Cur-velet transform.Thirdly,based on the area variance and mean features of image,a low-frequency coeffi-cient fusion operator is established,and by using the grayscale features of high-frequency coefficients to construct a grayscale measurement factor,the richness of image details is measured,and a high-frequen-cy coefficient fusion operator is formed.Finally,under the influence of the Curvelet inverse transform,the updated component(V)of the brightness component V is obtained.The experimental results show that the low-frequency coefficient fusion factor provided in this paper can obtain fused low-frequency coeffi-cients with good spectral characteristics.The grayscale calculation factor studied has a good effect on ob-taining fusion high-frequency coefficients with rich details.
关键词
遥感图像融合/灰度测算因子/Curvelet变换/HSV变换/区域方差特征/灰度特征Key words
remote sensing image fusion/grayscale measurement factor/Curvelet transform/HSV trans-formation/regional variance characteristics/grayscale features引用本文复制引用
出版年
2024
NETL
NSTL
万方数据