| With remote sensing data of high resolution,high spectral and multi-temporal of the three high trend,remote sensing image processing will face huge amounts of data processing problem.Because of its compressive sample feature that the sampling rate is far lower than the Nyquist,a large number of sampled data are reduced by compressive sensing.For this feature,this paper studies the remote sensing image fusion method and denoising method based on compressive sensing.This paper designs a remote sensing image fusion method with compressed sensing based on wavelet sparse basis This method firstly extract panchromatic image and multispectral image separate R,G,B components,secondly wavelet transform is performed on these components,thirdly,compressed sensing domain data are got by using the gaussian random matrix to sample the compressed data.Fourthly,the data are fused by taking different weights.Fifth,the fusion image is acquired by OMP algorithm.Finally,the simulation experiment results show that the proposed fusion method compared with the method proposed by Fan Wang and traditional fusion methods has a bigger superiority.Traditional remote sensing image denoising algorithm is only for single one to deal with the problem,combined with multiple measurements so far there is few papers discussing it.This paper presents a remote sensing image denoising algorithm of multiple measurements combined with GPSR.This method firstly do the fusion of remote sensing images obtained by multiple measurements,then get rid of the noise by GPSR which based on CS.Then simulation experiment is carried out,the experimental results contrast show that the proposed method has a bigger superiority.Finally improved the algorithm,applied the proposed fusion method in third chapter of this paper to the multiple measuring step.The experimental results show that the improved algorithm greatly reduces the amount of data on the basis of the denoising effect is not change. |
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