scholarly journals A comparative study for the assessment of Ikonos satellite image-fusion techniques

2022 ◽  
Vol 25 (1) ◽  
pp. 256
Author(s):  
Javier Medina ◽  
Nelson Vera ◽  
Erika Upegui
Keyword(s):  
Image Fusion ◽  
Satellite Image ◽  
Mean Value ◽  
Spectral Correlation ◽  
Pass Filter ◽  
Fusion Methods ◽  
Spatial Quality ◽  
Q Index ◽  
Urban Planning And Management ◽  
High Pass

I<span>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</span>: MULTI<sub>Wavelet 2D-M</sub>, MULTI<sub>G-S</sub>, MULTI<sub>MHF</sub>, MULTI<sub>HPF</sub>, MULTI<sub>SMV</sub>, and MULTI<sub>PCA</sub>. <span>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 </span> MULTI<sub>SMV</sub> 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.

scholarly journals Optical SAR Images Fusion: Comparative Analysis of Resulting Images Data

2018 ◽  
Vol 215 ◽  
pp. 01002
Author(s):  
Yuhendra ◽  
Minarni
Keyword(s):  
Image Fusion ◽  
Spatial Resolution ◽  
Mean Squared Error ◽  
Statistical Parameter ◽  
Speckle Noise ◽  
Sar Images ◽  
Squared Error ◽  
Fused Image ◽  
Fusion Methods ◽  
High Pass

Image fusion is a useful tool for integrating low spatial resolution multispectral (MS) images with a high spatial resolution panchromatic (PAN) image, thus producing a high resolution multispectral image for better understanding of the observed earth surface. A main proposed the research were the effectiveness of different image fusion methods while filtering methods added to speckle suppression in synthetic aperture radar (SAR) images. The quality assessment of the filtering fused image implemented by statistical parameter namely mean, standard deviation, bias, universal index quality image (UIQI) and root mean squared error (RMSE). In order to test the robustness of the image quality, either speckle noise (Gamma map filter) is intentionally added to the fused image. When comparing and testing result, Gram Scmidth (GS) methods have shown better results for good colour reproduction, as compared with high pass filtering (HPF). And the other hands, GS, and wavelet intensity hue saturation (W-IHS) have shown the preserving good colour with original image for Landsat TM data.

Optimizing the High-Pass Filter Addition Technique for Image Fusion

2008 ◽  
Vol 74 (9) ◽  
pp. 1107-1118 ◽  
Author(s):  
Ute G. Gangkofner ◽  
Pushkar S. Pradhan ◽  
Derrold W. Holcomb
Keyword(s):  
Image Fusion ◽  
Pass Filter ◽  
High Pass Filter ◽  
High Pass

scholarly journals Satellite image fusion using fuzzy logic

2016 ◽  
Vol 8 (2) ◽  
pp. 241-253 ◽  
Author(s):  
Suda Kumaraswamy ◽  
Dammavalam Srinivasa Rao ◽  
Nuthanapati Naveen Kumar
Keyword(s):  
Fuzzy Logic ◽  
Wavelet Transform ◽  
Side Effects ◽  
Image Fusion ◽  
Spatial Information ◽  
Satellite Image ◽  
Principal Component ◽  
Input Image ◽  
Fusion Methods ◽  
Fuzzy Logic Technique

Abstract Image fusion is a method of combining the Multispectral (MS) and Panchromatic (PAN) images into one image contains more information than any of the input. Image fusion aim is to decrease unknown and weaken common data in the fused output image at the same time improving necessary information. Fused images are helpful in various applications like, remote sensing, computer vision, biometrics, change detection, image analysis and image classification. Conventional fusion methods are having some side effects like assertive spatial information and uncertain color information is an usually the problem in PCA and wavelet transform based fusion is a computationally in depth process. In order to overcome these side effects and to propose alternative soft computing fusion approach for conventional fusion methods we exploit image fusion using fuzzy logic technique to fuse two source images obtained from different sensors to enhance both spectral and spatial information. The proposed work here further compared with two common fusion methods like, principal component analysis (PCA) and wavelet transform along with quality assessment metrics. Exploratory outputs demonstrated in order that fuzzy based image fusion technique can actively retains more information compared to PCA and wavelet transform approaches while enhancing the spatial and spectral resolution of the satellite images.

Satellite image fusion based on principal component analysis and high-pass filtering

2010 ◽  
Vol 27 (6) ◽  
pp. 1385 ◽  
Author(s):  
Mohamed R. Metwalli ◽  
Ayman H. Nasr ◽  
Osama S. Farag Allah ◽  
S. El-Rabaie ◽  
Fathi E. Abd El-Samie
Keyword(s):  
Principal Component Analysis ◽  
Image Fusion ◽  
Satellite Image ◽  
Principal Component ◽  
Component Analysis ◽  
High Pass

scholarly journals Comparison of Image Fusion Techniques Using Worldview-3 Data

2020 ◽  
Author(s):  
Milad Farhadi ◽  
Hossein Hoshyarmanesh
Keyword(s):  
Image Fusion ◽  
Nearest Neighbor ◽  
Color Space ◽  
Principal Component ◽  
Land Cover Mapping ◽  
Original Image ◽  
Stationary Wavelet Transform ◽  
Blue Band ◽  
Pass Filter ◽  
High Pass

Image fusion is a useful tool for producing a high-resolution multispectral image to be used for land use and land cover mapping. In this study, we use nine pansharpening algorithms namely Color Normalized (CN), Gram-Schmidt (GS), Hyperspherical Color Space (HCS), High Pass Filter (HPF), Nearest-Neighbor Diffusion (NND), Principal Component Analysis (PCA), Resolution Merge (RM), Stationary Wavelet Transform (SWT), and Wavelet Resolution Merge (WRM) to fusion Worldview-3 multispectral Bands and panchromatic band. In spectral and spatial fidelity, several image quality metrics are used to evaluate the performance of pansharpening algorithms. The SWT and PCA algorithms showed better results compared to other pansharpening algorithms while GS and CN algorithms showed the worst results for the original image fusion. The effect of fusion on each band was separately investigated and according to the calculations, we found that the CoastalBlue band and the Blue band showed the best result and the NIR-1 band and NIR-2 band show the worst result for the original image fusion. In the end, we conclude that the choice of fusion method depends on the requirement of remote sensing application.

A Flood Disaster Area Extraction Using KOMPSAT-1 EOC Satellite Image Data

2005 ◽  
Vol 277-279 ◽  
pp. 809-815
Author(s):  
Jung Nam Jun ◽  
Doo Chun Seo ◽  
Hyo Suk Lim
Keyword(s):  
Satellite Image ◽  
Image Data ◽  
Flood Disaster ◽  
Geographical Information ◽  
Edge Preserving ◽  
Pass Filter ◽  
Disaster Area ◽  
Geometric Relation ◽  
Sensor Modeling ◽  
High Pass

The main objective of this study is to observe and extract relevant geographical information of areas afflicted with floods using KOMPSAT-1 EOC image data. The satellite images taken on September 2, 2002 of the downstream of the river of Nakdong in the province of South Gyeongsang afflicted with floods at that time are used for the purpose of demonstration. To extract information which is the boundary lines and area in flood disaster area should be made to the ortho-image with characteristic of map. The generation of ortho-images involve sensor modeling using control points and DEM to restore a geometric relation of a satellite, its images and ground of which images are provided by the satellite. Candidate areas for edge extraction are selected based on ortho-corrected images through edge preserving smoothing method, high-pass filter and Prewitt operator. Based on the generated candidate areas, edges are extracted by use of edge extracting algorithm. The resultant extracted edges enable the overall status of flooded areas to be promptly grasped. This paper demonstrates such scope of utilization of satellite data for investigation and recovery of areas damaged by natural disasters.

Optimizing the High-Pass Filter Addition Technique for Image Fusion

2007 ◽  
Vol 73 (9) ◽  
pp. 1107-1118 ◽  
Author(s):  
Ute G. Gangkofner ◽  
Pushkar S. Pradhan ◽  
Derrold W. Holcomb
Keyword(s):  
Image Fusion ◽  
Pass Filter ◽  
High Pass Filter ◽  
High Pass

scholarly journals Research on fusion of GF-6 imagery and quality evaluation

2020 ◽  
Vol 165 ◽  
pp. 03016
Author(s):  
Guo Liu ◽  
Yizhe Wang ◽  
Li Guo ◽  
Cuifeng Ma
Keyword(s):  
Image Fusion ◽  
Multispectral Imaging ◽  
Satellite Image ◽  
Engineering Application ◽  
Color Retention ◽  
Spectral Fidelity ◽  
Fusion Methods ◽  
Multiple Resolutions ◽  
Image Fusion Method ◽  
Local Image

Gaofen-6 (GF-6) has the advantages of wide coverage, multiple resolutions, and multiple bands. Image fusion method is the key process in high resolution remote sensing application. Beijing Daxing International Airport was selected as the experiment area and four image fusion methods of HPF, NND, Gram-Schmidt and Pansharp were employed to process panchromatic and multispectral imaging. The results demonstrated that Pansharp was the best algorithm for image information and spectral fidelity of GF-6, taking into account the preservation of color effects of images and enhancement of spatial details, which can meet most fusion needs. HPF’s color retention is not as good as Pansharp algorithm. The contrast of the NND algorithm result is relatively high, which may cause the local image to be too bright and the texture to be lost. The GS algorithm has lower information entropy and average gradient. Compared with the other three algorithms, it has a worse effect on spatial details and texture expression. This conclusion can provide key reference for scientific research and engineering application using GF-6 satellite image．

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