Multi-source Remote Sensing Image Fusion Method Based on Sparse Representation

Yu, Xianchuan; Zhang, Yinggang; Gao, Guanyin

doi:10.1007/978-3-662-45737-5_26

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…For MRI imaging, the two different priors are incorporated, while for MIMO systems and remote sensing, only the sparse prior is applied in Figure 12. Comparison of image fusion methods for remote sensing applications using Brovey, DWT, PCA, FDCT, and the sparse representation methods [46]. the analysis.…”

Section: Discussionmentioning

confidence: 99%

“…We can observe that the performance of the CS method is almost equal to that of the method without using CS while saving half the number of bits. To improve the quality of the fused image, a remote sensing image fusion method based on sparse representation is proposed in [46]. In these methods, the source images were represented with sparse coefficients first.…”

Section: Remote Sensingmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian Inference and Compressed Sensing

Tesfamicael,

Barzideh

2017

Bayesian Inference

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Section: Discussionmentioning

confidence: 99%

Section: Remote Sensingmentioning

confidence: 99%

Bayesian Inference and Compressed Sensing

Tesfamicael,

Barzideh

2017

Bayesian Inference

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“…Decomposition of sparse coefficients from the matrix of input multispectral and panchromatic images has also been reported by Yu et al [2014]. As the values of higher coefficients of the panchromatic image are set to 0, the combined and balanced coefficients are directly used for fusion of the higher resolution and lower resolution images.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Dictionary based Sparse Representation Fusion for Multi-temporal Remote Sensing Images

Lal

Anouncia

2016

European Journal of Remote Sensing

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In remote sensing, image fusion is the process of blending two images to obtain finer details of the fused image. In this paper, an enhanced dictionary-based sparse representation (EDSR) is proposed for multitemporal image fusion. Multitemporal remote satellite images acquired on the same geographical area at different acquisition dates are merged to obtain a fused image for further analysis. Sparse representation of the image is employed in the approximation and representation of the target image. In order to improve the performance of the fusion process, a locally adaptive dictionary is created such that the dictionary contains patches extracted from both source images. The reconstruction of the image is performed using maximum absolute coefficients through the learned dictionary. The proposed EDSR technique has been compared quantitatively and qualitatively with the existing techniques, such as PCA, DWT, SWT, Ehlers, and sparse representation (SR), to evaluate its performance. The EDSR performs well in mutual information (MI) with 3.4742 and feature mutual information (FMI) with 0.4790 and provides better results in the case of degree of distortion, UIQI, and ERGAS for dataset 1 than the existing fusion methods. Experimental results on LANDSAT images revealed that the proposed technique is more effective in terms of preservation of spectral information, errors, color, and visual quality of the fused product.

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RETRACTED ARTICLE: Multi-model fusion based satellite image classification using versatile unsupervised vector zone (VUVZ) fusion and intensive pragmatic blossoms (IPB) technique

Swathika¹,

Sharmila²

2019

Multimed Tools Appl

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Multi-source Remote Sensing Image Fusion Method Based on Sparse Representation

Cited by 3 publications

References 13 publications

Bayesian Inference and Compressed Sensing

Bayesian Inference and Compressed Sensing

Enhanced Dictionary based Sparse Representation Fusion for Multi-temporal Remote Sensing Images

RETRACTED ARTICLE: Multi-model fusion based satellite image classification using versatile unsupervised vector zone (VUVZ) fusion and intensive pragmatic blossoms (IPB) technique

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