A new algorithm for multispectral image fusion

In remote sensing ,image fusion technique is useful tool use to fuse high spatial resolution panchromatic images(PAN)with lower spatial resolution multispectral images (MS) to create a high spatial resolution multispectral of image fusion (F) while preserving the spectral information in the multispectral image (MS) .there are many PAN sharpening techniques or pixel- Based image fusion techniques that have been developed to try to enhance the spatial resolution and the spectral property preservation of the MS. 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 include High-Pass Filter Additive Method (HPFA) .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 contrast and estimation homogenous to determine homogenous in different regions image using agent account interdependence of Block of the during shifting block to ten pixels ,therefore will be evaluation active and good because to take into consideration homogenous and edge quality measurements .

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. ...

In remote sensing image processing, the traditional fusion algorithm is based on the Intensity-Hue-Saturation (IHS) transformation. This method does not take into account the texture or spectrum information, spatial resolution and statistical information of the photos adequately, which leads to spectrum distortion of the image. Although traditional solutions in such application combine manifold methods, the fusion procedure is rather complicated and not suitable for practical operation. In this paper, an improved IHS transformation fusion algorithm based on the local variance weighting scheme is proposed for remote sensing images. In our proposal, firstly, the local variance of the SPOT (which comes from French "Systeme Probatoire d'Observation dela Tarre" and means "earth observing system") image is calculated by using different sliding windows. The optimal window size is then selected with the images being normalized with the optimal window local variance. Secondly, the power exponent is chosen as the mapping function, and the local variance is used to obtain the weight of the I component and match SPOT images. Then we obtain the I' component with the weight, the I component and the matched SPOT images. Finally, the final fusion image is obtained by the inverse Intensity-Hue-Saturation transformation of the I', H and S components. The proposed algorithm has been tested and compared with some other image fusion methods well known in the literature. Simulation result indicates that the proposed algorithm could obtain a superior fused image based on quantitative fusion evaluation indices.

2012

Image fusion is a formal framework for combining and utilizing data originating from different sources. It aims at producing high resolution multispectral images from a high-resolution panchromatic (PAN) image and low-resolution multispectral (MS) image. This fused image must contain more interpretable information than can be gained by using the original image. Ideally the fused image should not distort the spectral characteristics of multispectral data as well as, it should retain the basic colour content of the original data. There are many data fusion techniques that can be used which include Principal Component Analysis (PCA), Brovey Transform (BT), Multiplicative Transform (MT) and Discrete Wavelet Transform (DWT). One of the major problems associated with a data fusion technique is how to assess the quality of the fused (spatially enhanced) MS image. This paper presents a comprehensive analysis and evaluation of the most commonly used data fusion techniques. The performance of...

IEEE Transactions on Geoscience and Remote Sensing, 2000

Many image fusion techniques have been developed. However, most existing fusion processes produce color distortion in 1-m fused IKONOS images due to nonsymmetrical spectral responses of IKONOS imagery. Here, we proposed a fusion process to minimize this spectral distortion in IKONOS 1-m color images. The 1-m fused image is produced from a 4-m multispectral (MS) and 1-m panchromatic (PAN) image, maintaining the relations of spectral responses between PAN and each band of the MS images. To obtain this relation, four spectral weighting parameters are added with the pixel value of each band of the original MS image. Then, each pixel value is updated using a steepest descent method to reflect the maximum spectral response on the fused image.

KSCE Journal of Civil Engineering, 2003

In this paper, efforts were made to merge IKONOS panchromatic image with multispectral images using wavelet transform, Intensity-Hue-Saturation (IHS), multiplicative, and Principal Components Analysis (PCA) methods. Numerical comparisons were made to evaluate the effect of different fusion methods on the distortion of spectral characteristics. Likewise, analysis was done on different image fusion results based on land surface materials. Finally, the results of the building outline extraction from fused images and panchromatic image were compared. They show that the extraction of the building boundary from the fused image is better than from the panchromatic image in terms of boundary connection.

In remote sensing applications, lower spatial resolution multispectral images are fused with higher spatial resolution panchromatic ones. The objective of this fusion process is to enhance the spatial resolution of the multispectral images to make important features more apparent for human or machine perception. This enhancement is performed by injecting the high frequency component of the panchromatic image into the lower resolution ones without deteriorating the spectral component in the fused product. In this work, we propose a novel pixel based image fusion technique which exploits the statistical properties of the input images to compose the outcome images. Criteria for an optimal image fusion are proposed. The fused image is essentially constructed by using the statistical properties of panchromatic and multispectral images within a window to determine the weighting factors of the input images. This paper describes the principles of the proposed approach, assesses its properties and compares it with other popular fusion techniques. This study is carried out using Ikonos, QuickBird and SPOT images over areas with both urban and rural features. Analytical derivation, numerical analysis and graphic results are presented to support our discussions.

2006

The main topic of this paper is high-resolution image fusion. The techniques used to merge high spatial resolution panchromatic images with high spectral resolution multispectral images are described. The most commonly used image fusion methods that work on the principle of component substitution (intensity-hue-saturation method (IHS), Brovey transform (BT), and multiplicative method (MULTI)) have been applied to Ikonos, QuickBird, Landsat, and aerial orthophoto images. Visual comparison, histogram analyses, correlation coefficients, and difference images were used in order to analyze the spectral and spatial qualities of the fused images. It was discovered that for preserving spectral characteristics, one needs a high level of similarity between the panchromatic image and the respective multispectral intensity. In order to achieve this, spectral sensitivity of multispectral and panchromatic data was performed, and digital values in individual bands have been modified before fusion. It has also been determined that spatial resolution is best preserved in the event of an unchanged input panchromatic image.

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 methods. The fused images are then classified with Minimum Distance, Binary Encoding, Support Vector Machines, Random Forest, Maximum Likelihood and Artificial Neural Network classification methods by using exactly the same signatures. 450 points are used to calculate the post-classification accuracies of classified images. Some commonly-used metrics (Spectral and Spatial ERGAS, Spectral and Spatial RMSE, SID, SAM, RASE) are also calculated for all fused images. To determine the best fusion algorithm, the consistency between classification results and fusion metric results are evaluated together. HPF fusion method is found out to be the most successful fusion algorithm in terms of preserving the spectral quality and Support Vector Machines classifier is found out to be the best classification algorithm for these data sets.

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.

Science in China Series F: Information Sciences, 2010

Conventional image fusion algorithm, such as IHS, SVR, PCS, etc., may show some defects in inheriting the higher-spectral information embedded in the original lower-spatial resolution MS image. A fusion method based on spectral mixture analysis (FSMA) was proposed in previous study, which has potential in solving this problem. While published results are limited to well-behaved simulated data where the endmembers are known a priori and the FSMA method will not work well when applying to real remotely sensed images because the estimated reflectance ranging in panchromatic band derived from MS bands cannot be treated as the real panchromatic values. In this paper, an improved image fusion method based on spectral mixture analysis (IFSMA) is proposed, in which the original FSMA method was extended to real remotely sensed images by modifying the objective function of the constrained nonlinear optimization expressions. It was compared with the original FSMA, Zhang’s SVR, PCS and IHS method, and results indicated that the IFSMA method was superior to other methods in preserving the spectral and spatial information.

Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII, 2008

Generally, image fusion methods are classified into three levels: pixel level (iconic), feature level (symbolic) and knowledge or decision level. In this paper we focus on iconic techniques for image fusion. There exist a number of established fusion techniques that can be used to merge high spatial resolution panchromatic and lower spatial resolution multispectral images that are simultaneously recorded by one sensor. This is done to create high resolution multispectral image datasets (pansharpening). In most cases, these techniques provide very good results, i.e. they retain the high spatial resolution of the panchromatic image and the spectral information from the multispectral image. These techniques, when applied to multitemporal and/or multisensoral image data, still create spatially enhanced datasets but usually at the expense of the spectral consistency. In this study, a series of nine multitemporal multispectral remote sensing images (seven SPOT scenes and one FORMOSAT scene) is fused with one panchromatic Ikonos image. A number of techniques are employed to analyze the quality of the fusion process. The images are visually and quantitatively evaluated for spectral characteristics preservation and for spatial resolution improvement. Overall, the Ehlers fusion which was developed for spectral characteristics preservation for multi-date and multi-sensor fusion showed the best results. It could not only be proven that the Ehlers fusion is superior to all other tested algorithms but also the only one that guarantees an excellent color preservation for all dates and sensors.

Image Fusion and Its Applications, 2011

International Journal of Advanced Research in Electrical, Electronics and Instrumentation Energy, 2015

Image fusion has found many applications in computer vision, remote sensing, intelligent robots and military purposes. The use of different image fusion algorithms gives precise resultant images.In many remote sensing applications, the quantity of image data from the satellite sensors has been increasing because of advanced sensor technology.To avoid the limitations of single sensor images, multisensory image fusion provides the data that is suitable for further applications by eliminating the problem of lack of information. In this paper, a literature review has been made based on different techniques for combining multispectral images available. It includes IHS transform, High Pass filtering, PCA analysis, ANN, Wavelet transform and DCT. One of the effective techniques to get a good quality image is by using the Fuzzylet Fusion Algorithm in which the advantages of both Stationary Wavelet Transform and Fuzzy logic are combined.

Indonesian Journal of Electrical Engineering and Computer Science, 2022

Image-fusion provide users with detailed information about the urban and rural environment, which is useful for applications such as urban planning and management when higher spatial resolution images are not available. There are different image fusion methods. This paper implements, evaluates, and compares six satellite image-fusion methods, namely wavelet 2D-M transform, gram schmidt, high-frequency modulation, high pass filter (HPF) transform, simple mean value, and PCA. An Ikonos image (Panchromatic-PAN and multispectral-MULTI) showing the northwest of Bogotá (Colombia) is used to generate six fused images: MULTIWavelet 2D-M, MULTIG-S, MULTIMHF, MULTIHPF, MULTISMV, and MULTIPCA. In order to assess the efficiency of the six image-fusion methods, the resulting images were evaluated in terms of both spatial quality and spectral quality. To this end, four metrics were applied, namely the correlation index, erreur relative globale adimensionnelle de synthese (ERGAS), relative average spectral error (RASE) and the Q index. The best results were obtained for the MULTISMV image, which exhibited spectral correlation higher than 0.85, a Q index of 0.84, and the highest scores in spectral assessment according to ERGAS and RASE, 4.36% and 17.39% respectively.