Fusing satellite (remote sensing) images is an interesting topic in processing satellite images. The result image is achieved through fusing information from spectral and panchromatic images for sharpening. In this paper, a new algorithm based on based the Artificial bee colony (ABC) algorithm with peak signal-to-noise ratio (PSNR) index optimization is proposed to fusing remote sensing images in this paper. Firstly, Wavelet transform is used to split the input images into components over the high and low frequency domains. Then, two fusing rules are used for obtaining the fused images. The first rule is “the high frequency components are fused by using the average values”. The second rule is “the low frequency components are fused by using the combining rule with parameter”. The parameter for fusing the low frequency components is defined by using ABC algorithm, an algorithm based on PSNR index optimization. The experimental results on different input images show that the proposed algorithm is better than some recent methods.

Several earth observation satellites have dual-resolution sensors. The satellites provide multi-spectral images of low spatial resolution, panchromatic images of high spatial resolution [

Pan sharpening fuses a panchromatic image with higher-resolution and a multiband raster image with lower-resolution [

Some image fusion techniques that based on BT (Brovey transform), IHS (intensity hue saturation) or PCA (principal component analysis) provide multispectral images with visual high-resolution but they ignore the high-quality fusing requirement of spectral information [

There are two main approaches in fusing image, including spatial domain approach and transform domain approach [

In [

The fusing methods used wavelet transform, usually apply the average selection rule on low frequency components and max selection rule on high frequency components. This causes the resulting image to be greatly grayed out comparing to the input image because the grayscale values of the frequency components of the input images differ greatly. In addition, using a selection rule of Max or similar maximum for high frequency components can cause the horizontal and vertical streaks to appear, even forming grid cells that distort the image. To overcome these limitations, a novel algorithm for fusing satellite images is proposed in this paper. This algorithm is based on parameter optimization to choose the most suitable parameters for image merging.

The main

Propose parameter optimization method for combining high frequency components using the ABC algorithm.

Propose a novel algorithm that is used for fusing panchromatic and multispectral satellite images based on Wavelet transform and association rules with parameters found by the optimization method.

The remaining components of the article is structured as follows. Some related works are presented in Section 2. The proposed algorithm for image fusion is presented in Section 3. Section 4 presents some experiments of our algorithm and other related algorithms on selected images. The future researches and conclusions are given in Section 5.

Wavelet Transformation (WT) is a mathematical tool [

In our research, DWT is mainly used to decompose the input images into separate parts based on the frequency. Input image of DWT is the one-band image, for example the grey image. In the case of multi-band image, DWT is applied to each band of the input image.

When DWT performed, the size of the image LL at the previous stage is four times bigger than the size of LL image at the current stage. Therefore, if the input image is disaggregated into 3 levels, size of the input image is 64 times bigger than the final approximate. Wavelet transformation of image is illustrated as in

RGB color space is very suitable for display images on electronic screens. However, this color space is elusive to the human eye. Therefore, to better match the human visual system, the IHS color system (

I is denoted for intensity. This is the characteristic property of luminous intensity.

H is Hue. This is the principal wavelength-related property in a mixture of light wavelengths. The characteristic tint for the dominant color is perceived.

S is Saturation, characteristic of relative purity. Saturation depends on the width of the light spectrum and represents the amount of white mixed with chroma.

Convert from RGB to IHS and vice versa are presented in [

The ABC algorithm is an optimization algorithm, proposed in 2005 by Karaboga. This algorithm simulates the foraging behavior of honey bees [

A swarm of honey bees can achieve tasks successfully by social cooperation. So, ABC algorithm has three types of bees: scout bees, employed bees and onlooker bees. The employed bees find food sources according to their memory, then shares this information to the onlooker bees. Next, good food sources are selected by the onlooker bees from the sources that the employed bees found. The food sources with higher fitness (quality) will have a large chance to be selected by onlooker bees. The scout bees are derived from a few employed bees, with the task of finding new sources of foods.

The employed bees belong to the first half of the bee swarm, and the onlooker bees constitute the second half of this swarm. Total of solutions in the swarm is equal to total of the employed or onlooker bees. The initial population of food sources (solutions) are generated randomly.

Let

A new nomination solution

where

where _{i}. Next, the scout bee finds a new source, the updated equation is presented as in

where

A new algorithm for fusing remote sensing images named as the ABC optimization (shorten as FRSIAO) is proposed in this section. The general framework of the FRSIAO algorithm is given by a flowchart in

As shown in above diagram, this algorithm includes five main steps:

_{1} from RGB color space to IHS color space to get _{spec}, _{spec}, _{spec}. In which, channel I is used for processing in the next steps is calculated by the formula:

_{spec} and _{pan} (the image PAN is a gray image) to get LH_{1}, LL_{1}, HH_{1}, HL_{1} and LH_{2}, LL_{2}, HH_{2}, HL_{2}.

_{1}, HH_{1}, HL_{1}, LH_{1} and LL_{2}, HH_{2}, HL_{2}, LH_{2}) to get LL, HH, HL, LH as follows:

- Fusing the components with high frequency HH_{1}, HL_{1}, LH_{1}, and HH_{2}, HL_{2}, LH_{2}, to get HH, HL and LH using the following rule:

Fusing the low frequency components LL_{1} and LL_{2} to get LL using the following rule:

where,

_{new} using IDWT transformation.

_{new}, _{spec}, _{spec} from HIS color space to RGB colour space to obtain the ouput fused image.

The proposed algorithm has some advantages as follows:

Combining the low frequency components with adaptive fusing parameter obtained by using the algorithm ABC.

Proposing an algorithm for fusing panchromatic satellite and multi-spectral images named as FRSIAO.

Input data is downloaded from links:

PCAIF is a method of fusing images using the PCA. The diagram of PCAIF is introduced in

The eigen values and eigen vector are caculated. The normalized components P_{1} and P_{2} are caculated from the eigen vector. The fused image is defined by:

The measures include PSNR, FMI (Feature Mutual Information) [

From the output images of four methods in

The fusing image by the Wavelet method is greatly modified because using the average rule for components LL of the Spectral and Pan images.

All three methods, including WaveletIF, PCAIF and CSST, create small grids on the resulting images due to the use of the max rule.

The fusing image obtained by applying our proposed method is not distorted as well as creating grid cells comparing with other selected methods.

For the quality evaluation, the values of measures of the output images, generated by the fusion methods, are calculated and given in

Index | Wavelet | PCA | CSST | FRSIAO | |
---|---|---|---|---|---|

A | PSNR | 17.0828 | 21.0618 | 16.3469 | |

SSIM | 0.798 | 0.8721 | 0.7829 | ||

FMI | 0.7853 | 0.7907 | 0.7926 | ||

B | PSNR | 18.7687 | 19.9444 | 20.9225 | |

SSIM | 0.675 | 0.7346 | 0.7655 | ||

FMI | 0.7892 | 0.7876 | 0.7965 | ||

C | PSNR | 20.6758 | 22.3444 | 22.6804 | |

SSIM | 0.6043 | 0.6737 | 0.6877 | ||

FMI | 0.8344 | 0.8392 | 0.8419 |

The results in the above table shows that:

In experiment A, the CSST method gave the smallest PSNR and PSNR index, but the FMI index was larger than the WaveletIF and PCAIF methods. Meanwhile, the proposed method gives the largest results in all 3 indices.

In experiments B and C, the WaveletIF method gives the smallest psnr and ssim indices. For the FMI index, the PCAIF method gives the smallest value. The CSST method gives the value of 3 indices, which are all larger than the WaveletIF and PCAIF methods. Meanwhile, the proposed method gives the largest results in all 3 indices.

These mean that FRSIAO for fusing images has better quality than some recent methods.

This paper introduces a new algorithm for fusing remote sensing images based on optimization algorithm ABC denoted as FRSIAO). The proposed method has some advantages such as the adaptability of combining high frequency components; the high performance in combining the low frequency components based on the weighted parameter obtained by using ABC algorithm. Apart from that, our proposed method overcomes the limitations of wavelet transform based approaches. The experimental results show the higher performance of proposed method comparing with other related methods. For further works, we intend to integrate the parameter optimization in image processing and to apply the improvement method in other problems. Further, the comparison of FRSIAO with other optimization techniques will be done.

Our work is partially supported by

The authors declare that they have no conflicts of interest to report regarding the present study.