A Remote Sensing Image Fusion Algorithm Based on Second Generation Curvelet Transform Coupled Variance Constraints
HAN Weibing
Abstract：In order to make the fused remote sensing image retain more spectral characteristics and improve its sharpness as much as possible,this paper proposes a remote sensing image fusion algorithm based on second generation curvelet transform cou?pled variance feature constraints. The multispectral image is processed by IHS transform to obtain its brightness(I)component. Sec?ond-generation curvelet transform is used to decompose I component and panchromatic image in multi-scale and multi-direction to obtain the corresponding low-frequency coefficients and high-frequency coefficients. The regional energy proportion corresponding to different low-frequency coefficients is taken as the weighting factor,and the low-frequency coefficients are weighted and fused. Through the variance characteristics of different high frequency coefficients,a fusion model of high frequency coefficients is con?structed to complete the fusion of high frequency coefficients. The fused high and low frequency coefficients are updated by the sec?ond generation inverse curve let transform,and the fused remote sensing image is obtained by the updated I component through the inverse IHS transform. The experimental results show that the fused image of the proposed algorithm has better spectral characteris?tics and sharpness than the fused image of the current remote sensing image fusion method,and has excellent fusion performance.
Keywords：remote sensing image fusionIHS transformsecond generation curvelet transformregional energyvariance characteristics
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 57-61,66 )