A comparative analysis of image fusion methods

Wang, Zhijun; Ziou, Djemel; Armenakis, Costas; Li, D.; Li, Qingquan

doi:10.1109/tgrs.2005.846874

Cited by 678 publications

(80 citation statements)

References 24 publications

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“…Figure 4 Taking the original multi-spectral image as a reference image, an objective quantitative analysis of the fused images was performed in the scale of degradation. Deviation index, correlation coefficient, entropy and relative dimensionless global error (ERGAS) ( Zhijun et al, 2005) were calculated, shown in Table 1. Comparing the results of Table1, the deviation index of the improved method is the smallest, and its correlation coefficient is the biggest, even it has the minimum ERGAS values.…”

Section: Experimental Experimental Experimental Experimental Resultsmentioning

confidence: 99%

An Improved High Frequency Modulating Fusion Method Based on Modulation Transfer Function Filters

Jia

Zhang

2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. GeoEye-1 is the most advanced and highest resolution commercial earth imaging satellite in the world today. It provides multispectral images (MS) and Panchromatic image (PAN) with spatial resolutions of 2.0 m and 0.5 m respectively. Image fusion is very important for mapping and image interpretation because it can take advantage of the complementary spatial/spectral resolution characteristics of remote sensing imagery. So an improved high frequency modulation fusion method based on MTF is proposed. Modulation transfer functions (MTF) are firstly measured from GeoEye-1 images, and then the degraded images based on MTF filters are obtained. Secondly, modulating parameter is obtained based on Minimum Mean Square Error, and image fusion is performed and measured in the degraded version. Finally, fused images with the high spatial resolution are produced by using the proposed method. Compared with fusion methods of weighted high passing filtering(w-HPF) in ERDAS IMAGINE and general image fusion based on MTF( MTF-GIF), The results of fused GeoEye-1 images show that the proposed method is an efficient way for GeoEye-1 image fusion, which can keep spectral information with the high spatial resolution.

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Section: Experimental Experimental Experimental Experimental Resultsmentioning

confidence: 99%

An Improved High Frequency Modulating Fusion Method Based on Modulation Transfer Function Filters

Jia

Zhang

2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Figure 12 shows that barrel correction significantly improves the image segmentation accuracy when the observation radius continues to expand [55]. For small observation radius, pincushion correction leads to slight improvement of the image segmentation accuracy [56], but a significant improvement in accuracy occurs for a larger observation radius. We thus conclude that the main factor leading to the decrease in accuracy is the edge barrel distortion when the segmentation area increases [57].…”

Section: Accuracy Analysis Of Manual Statisticsmentioning

confidence: 99%

Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms

et al. 2018

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Abstract:To obtain an accurate count of wheat spikes, which is crucial for estimating yield, this paper proposes a new algorithm that uses computer vision to achieve this goal from an image. First, a home-built semi-autonomous multi-sensor field-based phenotype platform (FPP) is used to obtain orthographic images of wheat plots at the filling stage. The data acquisition system of the FPP provides high-definition RGB images and multispectral images of the corresponding quadrats. Then, the high-definition panchromatic images are obtained by fusion of three channels of RGB. The Gram-Schmidt fusion algorithm is then used to fuse these multispectral and panchromatic images, thereby improving the color identification degree of the targets. Next, the maximum entropy segmentation method is used to do the coarse-segmentation. The threshold of this method is determined by a firefly algorithm based on chaos theory (FACT), and then a morphological filter is used to de-noise the coarse-segmentation results. Finally, morphological reconstruction theory is applied to segment the adhesive part of the de-noised image and realize the fine-segmentation of the image. The computer-generated counting results for the wheat plots, using independent regional statistical function in Matlab R2017b software, are then compared with field measurements which indicate that the proposed method provides a more accurate count of wheat spikes when compared with other traditional fusion and segmentation methods mentioned in this paper.

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“…Several image fusion methods have been devised and applied to various applications. The common methods used for fusion are principal component analysis (PCA) [Lal and Margret, 2015a], intensity hue saturation (IHS), wavelet transform (WT) [Lal and Margret, 2015a], Laplacian pyramid (LP), Bayesian, Brovey transform (BT), and maximum entropy [Wang et al, 2005]. Sparse representation was first introduced in image fusion by Yang and Shutao [2010], and since then, it has become a renowned image fusion technique amongst all other techniques.…”

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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A comparative analysis of image fusion methods

Cited by 678 publications

References 24 publications

An Improved High Frequency Modulating Fusion Method Based on Modulation Transfer Function Filters

An Improved High Frequency Modulating Fusion Method Based on Modulation Transfer Function Filters

Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms

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

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