The Statistical methods of Pixel-Based Image Fusion Techniques

Transferring spatial details of a high-resolution image into a low-resolution one is called image fusion. There are some different fusion methods introduced. Due to the nature of fusion process, these methods may damage the spectral quality of the low-resolution multispectral image to a certain extent. In the literature, there are some metrics that are used to evaluate the quality of the fused images. Depending on their mathematical algorithms, these quality metrics may result in misleading results in terms of spectral quality in the fused images. If fusion process is successful, the classification result of the fused image should not be worse than the result acquired from raw multispectral image. In this study, Worldview-2, Landsat ETM+ and Ikonos multispectral images are fused with their own panchromatic bands and another Ikonos image is fused with Quickbird pan-sharpened image using IHS, CN, HPF, PCA, Multiplicative, Ehlers, Brovey, Wavelet, Gram-Schmidt and Criteria Based fusion...

2011

Image fusion is important algorithm in many remote sensing applications like visualization, identification of the boundary of the object, object segmentation, object analysis. Most widely used IHS method preserves the spatial information but distorts the spectral information during fusion process. IHS method is also limited to the three bands. In this paper, the MS based difference with respect to its mean image is used to reconstruct the fused image. In this the mean of the MS band is replaced by the mean of the PAN image. In this way both spatial and spectral information are taken into account in the process of image fusion. We have considered actual dataset of IKONOS four band image for our experiment. It has been observed from the simulation results that the proposed algorithm preserves both spatial and spectral information better than compared standard algorithm and it also significantly improves the universal quality index which measures the visual quality of fused image.

2012

In literature, several methods are available to combine both low spatial multispectral and low spectral panchromatic resolution images to obtain a high resolution multispectral image. One of the most common problems encountered in these methods is spectral distortions introduced during the merging process. At the same time, the spectral quality of the image is the most important factor affecting the accuracy of the results in many applications such as object recognition, object extraction, image analysis. In this study, the most commonly used methods including GIHS, GIHSF, PCA and Wavelet are analyzed using image quality metrics such as SSIM, ERGAS and SAM. At the same time, Wavelet is the best method for obtaining the fused image having the least spectral distortions according to obtained results. At the same time, image quality of GIHS, GIHSF and PCA methods are close to each other, but spatial qualities of the fused image using the wavelet method are less than others.

This paper attempts to undertake the study of image fusion ,by using pixel –based image fusion techniques i.e. arithmetic combination ,frequency filtering methods of pixel –based image fusion techniques and different statistical techniques of image fusion. The first type includes Brovey Transform (BT), Color Normalize Transformation (CNT) and Multiplicative Method (MLT). The second type includes High-Pass Filter Additive Method (HPFA and HFA). The third type includes Local Mean Matching (LMM), Regression Variable Substitution (RVS). This paper also devotes to concentrate on the analytical techniques for evaluating the quality of image fusion (F) , in this study will concentrate on determination image details quality specially tiny detail and edges by uses two criterion edge detection then quality measurements determine and estimation homogenous to determine homogenous in different regions image using Mean (µ) and Standard Deviation (SD), Signal –to Noise Ratio (SNR) ,and compute Absolute Mean Square Error (AMSE),Mean Square Error (MSE), Peak-Signal-To –Noise Ratio(PSNR) , Mutual Information(MI) and Spatial Frequency(SF) ,therefore will be evaluation active and good because to take into consideration homogenous and edge quality measurements .

International Journal of Engineering Sciences & Research Technology, 2014

Image fusion techniques have interest within the remote sensing community. The reason of this is that in most cases the new generation of remote sensors with very high spatial resolution acquires image datasets in two separate modes: the highest spatial resolution is obtained for panchromatic images (PAN) whereas multispectral information (MS) is associated with lower spatial resolution. Usually, the term ‘fusion’ gets several words to appear, such as merging, combination, synergy, integration and several others that express more or less the same meaning the concept have since it appeared in literature. Image fusion techniques can be classified into three categories depending on the stage at which fusion takes place; it is often divided into three levels, namely: pixel level, feature level and decision level of representation. This paper describes the concept of image fusion and its relevant methods.

Many algorithms and software tools have been developed for fusing panchromatic and multispectral datasets in remote sensing. Also, a number of methods has been proposed and developed for the comparative evaluation of fusion results. To this date, however, no papers have been published that analyze effectiveness and quality of the evaluation techniques. In our study, methods that evaluate fusion quality are tested for different images and test sites. This analysis shows that in most cases the tested methods perform well, but are sometimes inconsistent with visual analysis results.

International Journal of …, 2012

Index Terms-image fusion, data fusion, remote sensing, image processing, signal processing. Visual Sensor, DCT.

In remote sensing community, different image sensors provide different data with different spectral and spatial resolution. Multispectral imaging sensors collect poor spatial resolution multispetral data, while panchromatic imaging ones provide adequate spatial resolution panchromatic data. The fusion of images is the process of combining two or more images into a single image retaining important features from each. Ideally, the method used to merge data sets with high-spatial and high-spectral resolution should not distort the spectral characteristics of the high-spectral resolution data. In many applications, the quality of the fused images is of fundamental importance and is usually assessed by visual analysis subjective to the interpreter. This paper proposes to assess the effects of the spatial resolution ratio of the input images on the quality of the fused image. In this paper, the IHS transform is used to combine a high resolution panchromatic image with a low resolution mul...

2014

Most Earth observational satellites are not able to acquire high spatial and spectral resolution data simultaneously because of design or observational constraints. To overcome such limitations, image fusion techniques are use. Image fusion is process combine different satellite images on a pixel by pixel basis to produce fused images of higher value. The value adding is meant in terms of information extraction capability, reliability and increased accuracy. The objective of this paper is to describe basics of image fusion, various pixel level mage fusion techniques for evaluating and assessing the performance of these fusion algorithms. Keywords— -Image Fusion, Pixel Level, Multi-sensor, IHS, PCA, Multiplicative, Brovey, DCT, DWT. INTRODUCTION Image Fusion is process of combine two different images which are acquired by different sensor or single sensor. Output image contain more information than input images and more suitable for human visual perception or for machine perception. ...

Applied Sciences, 2020

Preservation of spectral and spatial information is an important requirement for most quantitative remote sensing applications. In this study, we use image quality metrics to evaluate the performance of several image fusion techniques to assess the spectral and spatial quality of pansharpened images. We evaluated twelve pansharpening algorithms in this study; the Local Mean and Variance Matching (IMVM) algorithm was the best in terms of spectral consistency and synthesis followed by the ratio component substitution (RCS) algorithm. Whereas the IMVM and RCS image fusion techniques showed better results compared to other pansharpening methods, it is pertinent to highlight that our study also showed the credibility of other pansharpening algorithms in terms of spatial and spectral consistency as shown by the high correlation coefficients achieved in all methods. We noted that the algorithms that ranked higher in terms of spectral consistency and synthesis were outperformed by other com...

Image fusion in remote sensing has emerged as a sought-after protocol because it has proven beneficial in many areas, especially in studies of agriculture, environment, and related fields. Simply put, image fusion involves garnering all pivotal data from many images and then merging them in fewer images, ideally into a solitary image. This is because this one fused image packs all the pertinent information and is more correct than any picture extracted from one solitary source. It also includes all the data that is required. Additional advantages are: it lessens the amount of data and it creates images that are appropriate and that can be understood by humans and machines. This paper reviews the three image fusing processing levels, which include feature level, decision level, and pixel level. This paper also dwells upon image fusion methods that fall under four classes: MRA, CS, model-based solutions and hybrid and shows how each class has some distinct advantages as well as drawbacks.

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013

It is of great value to fuse a high-resolution panchromatic image and low-resolution multi-spectral images for object recognition. In the paper, tow frames of remotely sensed imagery, including ZY03 and SPOT05, are selected as the source data. Four fusion methods, including Brovey, PCA, Pansharp, and SFIM , are used to fuse the images of multispectral bands and panchromatic band. Three quantitative indicators were calculated and analyzed, that is, gradient, correlation coefficient and deviation. According to comprehensive evaluation and comparison, the best effect is SFIM transformation, combined with fusion image through four transformation methods.

International journal of remote sensing, 1998

With the availability of multisensor, multitemporal, multiresolution and multifrequency image data from operational Earth observation satellites the fusion of digital image data has become a valuable tool in remote sensing image evaluation. Digital image fusion is a relatively new research ® eld at the leading edge of available technology. It forms a rapidly developing area of research in remote sensing. This review paper describes and explains mainly pixel based image fusion of Earth observation satellite data as a contribution to multisensor integration oriented data processing.

2014

Pixel-level image fusion (PLIF) performance assessment includes information theory, feature-based, structural similarity, and perception-based objective metrics. However, to relate these metrics to human understanding requires subjective metrics. This paper proposes to use statistical analyses to assess PLIF performance over objective and subjective metrics. Nonparametric tests are applied to the subjective and objective assessment data from three multi-resolution image fusion methods using visual and infrared images. The tests can offer the performance information about the fusion algorithm at a designated significance level. Statistical analysis of PLIF facilitates the establishment of a baseline for the research in image fusion and serves as a statistical validation for proposing, comparing, and adopting a new PLIF algorithm.