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.

scholarly journals Multi -Focus Image Fusion Based on Stationary Wavelet Transform and PCA on YCBCR Color Space

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Alaa A. Abdullatif ◽  
Firas A. Abdullatif ◽  
Amna Al Safar
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Color Space ◽  
Principal Component ◽  
Fusion Method ◽  
Stationary Wavelet Transform ◽  
Method Performance ◽  
Ycbcr Color Space ◽  
Focus Image ◽  
Image Fusion Method

The multi-focus image fusion method can fuse more than one focused image to generate a single image with more accurate description. The purpose of image fusion is to generate one image by combining information from many source images of the same scene. In this paper, a multi-focus image fusion method is proposed with a hybrid pixel level obtained in the spatial and transform domains. The proposed method is implemented on multi-focus source images in YCbCr color space. As the first step two-level stationary wavelet transform was applied on the Y channel of two source images. The fused Y channel is implemented by using many fusion rule techniques. The Cb and Cr channels of the source images are fused using principal component analysis (PCA). The proposed method performance is evaluated in terms of PSNR, RMSE and SSIM. The results show that the fusion quality of the proposed algorithm is better than obtained by several other fusion methods, including SWT, PCA with RGB source images and PCA with YCbCr source images.

A Study on The Use of UAV Images to Improve the Separation Accuracy of Agricultural Land Areas

2021 ◽  
Author(s):  
Fahime Arabi Aliabad ◽  
Hamid Reza Ghafarian Malamiri ◽  
Saeed Shojaei
Keyword(s):  
Spatial Resolution ◽  
Agricultural Land ◽  
Principal Component ◽  
Landsat Image ◽  
Fuzzy Artmap ◽  
Landsat 8 ◽  
Pass Filter ◽  
Tree Methods ◽  
Uav Images ◽  
High Pass

Abstract Classifying satellite images with medium spatial resolution such as Landsat, it is usually difficult to distinguish between plant species, and it is impossible to determine the area covered with weeds. In this study, a Landsat 8 image along with UAV images was used to separate pistachio cultivars and separate weed from trees. In order to use the high spatial resolution of UAV images, image fusion was carried out through high-pass filter, wavelet, principal component transformation, BROVEY, IHS and Gram Schmidt methods, and ERGAS, RMSE and correlation criteria were applied to assess their accuracy. The results represented that the wavelet method with R2, RMSE and ERGAS 0.91, 12.22 cm and 2.05 respectively had the highest accuracy in combining these images. Then, images obtained by this method were chosen with a spatial resolution of 20 cm for classification. Different classification methods including unsupervised method, maximum likelihood, minimum distance, fuzzy artmap, perceptron and tree methods were evaluated. Moreover, six soil classes, Ahmad Aghaei, Akbari, Kalleh Ghoochi, Fandoghi and a mixing class of Kalleh Ghoochi and Fandoghi were applied and also three classes of soil, pistachio tree and weeds were extracted from the trees. The results demonstrated that the fuzzy artmap method had the highest accuracy in separating weeds from trees, differentiating various pistachio cultivars with Landsat image and also classification with combined image and had 0.87, 0.79 and 0.87 kappa coefficients respectively. The comparison between pistachio cultivars through Landsat image and combined image showed that the validation accuracy obtained from harvest has raised by 17% because of combination of images. The results of this study indicated that the combination of UAV and Landsat 8 images affects well to separate pistachio cultivars and determine the area covered with weeds.

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 HYPERSPECTRAL IMAGE SHARPENING BASED ON EHLERS FUSION

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 941-947
Author(s):  
S. Xu ◽  
M. Ehlers
Keyword(s):  
Hyperspectral Image ◽  
Principal Component ◽  
Hyperspectral Images ◽  
Data Sets ◽  
Image Sharpening ◽  
Pass Filter ◽  
Fusion Procedure ◽  
Fused Image ◽  
Spectral Profiles ◽  
High Pass

As the application of hyperspectral images is increasing, many researchers attempt to extend existing pansharpening techniques to hyperspectral images. This paper focuses on the application of Ehlers fusion to hyperspectral image sharpening. Ehlers fusion involves two crucial algorithms: filter technique in the frequency domain and intensity transform. In this study, different filter types and intensity transform methods were analysed separately. With a combination of filter types and intensity transforms, the fusion procedure was implemented to test data sets. The spectral profiles of the pixels of the images were then used as a tool to control the quality of the fused image. Finally, the performance of Ehlers fusion is compared with Principle Component (PC) analysis, Gram-Schmidt transform (Gram-Schmidt), High-Pass Filtering in the spatial domain (HPF), and Wavelet Principal Component (Wavelet-PC) analysis using the same input data. The comparison shows that Ehlers high-pass filter fusion shows outstanding performance both on spatial enhancement and colour preservation.

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.

Fusion of Satellite Images in Transform Domain

2018 ◽  
Author(s):  
Venkatesh H
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Satellite Images ◽  
Principal Component ◽  
Fusion Rule ◽  
Discrete Wavelet ◽  
Trans Form ◽  
Geo Satellite ◽  
Fourier Trans ◽  
High Pass

Unique—Image fusion in light of the wavelet and fourier trans-form comes about rich multispectral points of interest yet gives less spatial subtle elements from source images. Wavelet transform performs well at straight highlights yet not at non-direct discontinuities since Wavelets don't utilize the geometric properties of structures. Curvelet transforms defeat such troubles in include rep-resentation. A novel Image fusion rule by means of high pass balance utilizing Local Magnitude Ratio (LMR) in Fast Discrete Curvelet Transforms domain (FDCT) and Discrete wavelet transform (DWT) is characterized. Indian Remote Sensing Geo satellite images are utilized for MS and Pan images. This fusion rule creates HR multispectral image with high spatial resolution. This technique is contrasted and wavelet, Principal Component Analysis (PCA), Fast Discrete Curvelet Transforms domain fusion strategies. Master postured technique spatially performs alternate strategies and results rich multispectral information.

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