A new pan-sharpening method using multiobjective particle swarm optimization and the shiftable contourlet transform

Saeedi, Jamal; Faez, Karim

doi:10.1016/j.isprsjprs.2011.01.006

Cited by 45 publications

(15 citation statements)

References 36 publications

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“…The fusion quality is assessed in two aspects, i.e., preservation of spectral characteristics and enhancement of spatial details (Möller et al, 2012;Saeedi and Faez, 2011;Witharana et al, 2013;Zhou et al, 2014). The used metrics for preservation of spectral characteristics are correlation coefficients (CC), root mean squared error (RMSE) (Möller et al, 2012;Saeedi and Faez, 2011;Witharana et al, 2013) and spectral angle mapper (SAM) (Alparone et al, 2007;Möller et al, 2012;Witharana et al, 2013;Zhou et al, 2014) between multispectral and fused images.…”

Section: Quality Metrics For Assessmentmentioning

confidence: 99%

“…The used metrics for preservation of spectral characteristics are correlation coefficients (CC), root mean squared error (RMSE) (Möller et al, 2012;Saeedi and Faez, 2011;Witharana et al, 2013) and spectral angle mapper (SAM) (Alparone et al, 2007;Möller et al, 2012;Witharana et al, 2013;Zhou et al, 2014) between multispectral and fused images. The formula for calculating RMSE is as follows:…”

Section: Quality Metrics For Assessmentmentioning

confidence: 99%

“…The spatial details of sharpened images are assessed through a correlation coefficient between the high-pass filtered panchromatic and the high-pass filtered sharpened images, named Laplacian correlation coefficient (LCC) (Möller et al, 2012;Saeedi and Faez, 2011;Zhou et al, 1998). The Laplacian filter is illustrated here:…”

Section: Quality Metrics For Assessmentmentioning

confidence: 99%

See 2 more Smart Citations

The Optimized Block-Regression-Based Fusion Algorithm for Pansharpening of Very High Resolution Satellite Imagery

Zhang

Yang

Reinartz

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Pan-sharpening of very high resolution remotely sensed imagery need enhancing spatial details while preserving spectral characteristics, and adjusting the sharpened results to realize the different emphases between the two abilities. In order to meet the requirements, this paper is aimed at providing an innovative solution. The block-regression-based algorithm (BR), which was previously presented for fusion of SAR and optical imagery, is firstly applied to sharpen the very high resolution satellite imagery, and the important parameter for adjustment of fusion result, i.e., block size, is optimized according to the two experiments for Worldview-2 and QuickBird datasets in which the optimal block size is selected through the quantitative comparison of the fusion results of different block sizes. Compared to five fusion algorithms (i.e., PC, CN, AWT, Ehlers, BDF) in fusion effects by means of quantitative analysis, BR is reliable for different data sources and can maximize enhancement of spatial details at the expense of a minimum spectral distortion.

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Section: Quality Metrics For Assessmentmentioning

confidence: 99%

Section: Quality Metrics For Assessmentmentioning

confidence: 99%

Section: Quality Metrics For Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

The Optimized Block-Regression-Based Fusion Algorithm for Pansharpening of Very High Resolution Satellite Imagery

Zhang

Yang

Reinartz

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…Table 9 indicates that the results of IHS and CN in the framework is better than in commercial software, while the UIQI value of PCA in the framework is lower than in commercial software. Correlation coefficient between the high-pass filtered panchromatic and the high-pass filtered sharpened images as an index of the spatial quality, namely Laplacian correlation coefficient (LCC) [35,88,89], is given in Table 10. LCC values have almost no difference among all versions.…”

Section: Comparing With Commercial Softwarementioning

confidence: 99%

A Parallel Computing Paradigm for Pan-Sharpening Algorithms of Remotely Sensed Images on a Multi-Core Computer

2014

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Pan-sharpening algorithms are data-and computation-intensive, and the processing performance can be poor if common serial processing techniques are adopted. This paper presents a parallel computing paradigm for pan-sharpening algorithms based on a generalized fusion model and parallel computing techniques. The developed modules, including eight typical pan-sharpening algorithms, show that the framework can be applied to implement most algorithms. The experiments demonstrate that if parallel strategies are adopted, in the best cases the fastest times required to finish the entire fusion operation (including disk input/output (I/O) and computation) are close to the time required to directly read and write the images without any computation. The parallel processing implemented on a workstation with two CPUs is able to perform these operations up to 13.9 times faster than serial execution. An algorithm in the framework is 32.6 times faster than the corresponding version in the ERDAS IMAGINE software. Additionally, no obvious differences in the fusion effects are observed between the fusion results of different implemented versions.

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“…Saeedi [17] utilized the population-based optimization to select the weights of the low-frequency wavelet coefficients to improve the infrared and visible image fusion. Saeedi [18] found the optimal pan-sharpened image by applying the multi-objective particle swarm optimization algorithm and using the two initial pan-sharpened results generated by two different fusion rules. Finally, Lacewell [19] used genetic algorithms to get a more optimized combined image for visual and thermal satellite image fusion.…”

Section: Introductionmentioning

confidence: 99%

Image Fusion Based on the \({\Delta ^{ - 1}} - T{V_0}\) Energy Function

Xie

Guo

et al. 2014

Entropy

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This article proposes a ∆ −1 − T V 0 energy function to fuse a multi-spectral image with a panchromatic image. The proposed energy function consists of two components, a T V 0 component and a ∆ −1 component. The T V 0 term uses the sparse priority to increase the detailed spatial information; while the ∆ −1 term removes the block effect of the multi-spectral image. Furthermore, as the proposed energy function is non-convex, we also adopt an alternative minimization algorithm and the L 0 gradient minimization to solve it. Experimental results demonstrate the improved performance of the proposed method over existing methods.

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A new pan-sharpening method using multiobjective particle swarm optimization and the shiftable contourlet transform

Cited by 45 publications

References 36 publications

The Optimized Block-Regression-Based Fusion Algorithm for Pansharpening of Very High Resolution Satellite Imagery

The Optimized Block-Regression-Based Fusion Algorithm for Pansharpening of Very High Resolution Satellite Imagery

A Parallel Computing Paradigm for Pan-Sharpening Algorithms of Remotely Sensed Images on a Multi-Core Computer

Image Fusion Based on the \({\Delta ^{ - 1}} - T{V_0}\) Energy Function

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