Simultaneous Fusion and Denoising of Panchromatic and Multispectral Satellite Images

Ragheb, Amr; Osman, Hussein Al; Abbas, Alaa M.; Mesbah, Saleh; Eltobely, Tarek E.; Khamis, S.; Elhalawany, Mohamed E.; Nasr, Mohamed E.; Dessouky, Moawad I.; Al-Nuaimy, Waleed; El‐Samie, Fathi E. Abd

doi:10.1007/s11220-013-0075-3

Cited by 5 publications

(1 citation statement)

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“…This fusion method can obtain more detailed information and achieve better visual quality than discrete wavelet transform and normal NSCT. Ragheb et al (2012) constructed a hybrid fusion method for satellite images comprising both intensity-hue-saturation (IHS) transform and discrete wavelet frame transform (DWFT). This method tries to achieve the highest possible spectral and spatial resolution with minimal distortion in the fused images.…”

Section: Introductionmentioning

confidence: 99%

Multi-channel satellite cloud image fusion in the tetrolet transform domain

Zhang

Chen

Duanmu

et al. 2014

International Journal of Remote Sensing

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A novel multi-channel satellite cloud image fusion algorithm constructed in the tetrolet transform domain is proposed. Tetrolet is successfully applied in image denoising, image sparse representation, and image restoration. In this paper, tetrolet transform was introduced into the field of satellite cloud image fusion since its sparse degree is high. Tetrolet can describe the geometric structure feature of the satellite cloud image very well. First, tetrolet transform must be implemented into the multi-channel satellite cloud images to obtain low-and high-frequency coefficients and corresponding covering distribution values. Then, a Laplacian pyramid algorithm must be used to decompose the low-frequency portion in the tetrolet domain by averaging the values of its top layer and taking the maximum absolute values of the other layers. While reconstruction is implemented in this stage, the algorithm takes the maximum standard deviation of the high-frequency parts for each block in the tetrolet domain. Last, an inverse tetrolet transform must be used to obtain the final fused image. This paper compares the proposed image fusion algorithm to three similar image fusion algorithms: the curvelet image fusion algorithm, the non-subsampled contourlet transform (NSCT) image fusion algorithm, and the tetrolet image fusion algorithm. Mutual information, joint entropy, mean structural similarity (MSSIM), standard deviation, and average relative deviation are used as objective criteria to evaluate the quality of the fused images. In order to verify the efficiency of the proposed algorithm, the fusion cloud image is used to determine the centre location of eye and non-eye typhoons. Experimental results show that the proposed algorithm performs well when fusing the information in multi-channel satellite cloud images and improves the precision of locating the typhoon's centre. The proposed algorithm's comprehensive performance is superior to similar image fusion algorithms.

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Section: Introductionmentioning

confidence: 99%