Fusion of multispectral and panchromatic satellite images based on contourlet transform and local average gradient

Song, Haohao

doi:10.1117/1.2437125

Cited by 35 publications

(10 citation statements)

References 4 publications

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“…Such methods are presented in a general way in [9]. The MSM may be based on wavelet transforms [18], [24]- [26], Laplacian pyramid decomposition [18], [20], curvelets [27], contourlets, and local average gradient [28]. The MSM should take into account the modulation transfer functions (MTFs) of the real sensors since the MTFs of the MS sensors may be significantly different from one another and, particularly, are different from the Pan one [29].…”

Section: Arsis Concept Implementationsmentioning

confidence: 99%

Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics

Thomas

Ranchin

Wald

et al. 2008

IEEE Trans. Geosci. Remote Sensing

568

309

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Abstract-Our framework is the synthesis of multispectral images (MS) at higher spatial resolution, which should be as close as possible to those that would have been acquired by the corresponding sensors if they had this high resolution. This synthesis is performed with the help of a high spatial but low spectral resolution image: the panchromatic (Pan) image. The fusion of the Pan and MS images is classically referred as pan-sharpening. A fused product reaches good quality only if the characteristics and differences between input images are taken into account. Dissimilarities existing between these two data sets originate from two causes-different times and different spectral bands of acquisition. Remote sensing physics should be carefully considered while designing the fusion process. Because of the complexity of physics and the large number of unknowns, authors are led to make assumptions to drive their development. Weaknesses and strengths of each reported method are raised and confronted to these physical constraints. The conclusion of this critical survey of literature is that the choice in the assumptions for the development of a method is crucial, with the risk to drastically weaken fusion performance. It is also shown that the Amélioration de la Résolution Spatiale par Injection de Structures concept prevents from introducing spectral distortion into fused products and offers a reliable framework for further developments.

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Section: Arsis Concept Implementationsmentioning

confidence: 99%

Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics

Thomas

Ranchin

Wald

et al. 2008

IEEE Trans. Geosci. Remote Sensing

568

309

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“…It is reasonable that the BSEM and other methods [31] of fusion are better than all other including WT and SEM.…”

Section: )mentioning

confidence: 89%

Multi-Source Multi-Sensor Image Fusion Based on Bootstrap Approach and SEM Algorithm

Delleji¹,

Zribi²,

Hamida³

2009

TORMSJ

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Bootstrap approach and Stochastic EM algorithm combination applied for the improvement of the multisource and multi-sensor image fusion process; was presented in this research. Improvement concerned not only image quality and reducing processing execution time as mentioned in our previous Bootstrap EM algorithm (BEM), but also regarding initialization dependence as well as fixed classes' number. Such interesting fusion algorithm for multisource and multisensor image using one stochastic phase, i.e. SEM algorithm, preceded by Bootstrap procedure was successfully implemented and tested for several prototype images. Targeted images were firstly split by an unsupervised Bayesian segmentation approach in order to determine a joint region map for the fused image. The Bootstrap approach was then applied to the targeted multisource image in conjunction with the SEM algorithm, forming hence one Bootstrap SEM algorithm called BSEM. The procedure of such algorithm involved both statistical parameters' estimation from one representative Bootstrap sample of each source or sensor images.Keywords: Multi-source multi-sensor image fusion, unsupervised bayesian segmentation, bootstrap approach, sem algorithm.

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“…Also, a curvelet image fusion was presented by [4] [17]. Furthermore, many other fusion methods like contourlet by [8] [22], and Non-subsampled Contourlet Transform (NSCT) which can be found in [5].…”

Section: Ima Ge Fusion Prelim Ina Riesmentioning

confidence: 99%

Correcting Multi-focus Images via Simple Standard Deviation for Image Fusion

Jassim¹

2013

IJIGSP

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Abstract-Image fusion is one of the recent trends in image reg istration which is an essential field of image processing. The basic principle o f this paper is to fuse mu lti-focus images using simple statistical standard deviation. Firstly, the simp le standard deviation for the k×k window inside each of the mult i-focus images was computed. The contribution in this paper came fro m the idea that the focused part inside an image had high details rather than the unfocused part. Hence, the dispersion between pixels inside the focused part is higher than the dispersion inside the unfocused part. Secondly, a simple co mparison between the standard deviation for each k×k window in the mu lti-focus images could be co mputed. The highest standard deviation between all the co mputed standard deviations for the mult i-focus images could be treated as the optimal that is to be placed in the fused image. The experimental visual results show that the proposed method produces very satisfactory results in spite of its simp licity.

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Fusion of multispectral and panchromatic satellite images based on contourlet transform and local average gradient

Cited by 35 publications

References 4 publications

Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics

Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics

Multi-Source Multi-Sensor Image Fusion Based on Bootstrap Approach and SEM Algorithm

Correcting Multi-focus Images via Simple Standard Deviation for Image Fusion

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