A comparative analysis of pansharpening techniques on QuickBird and WorldView-3 images

Snehmani,; Gore, Akshay; Ganju, Ashwagosh; Kumar, Satish; Srivastava, Priyanka; P, Hari Ram R

doi:10.1080/10106049.2016.1206627

Cited by 31 publications

(23 citation statements)

References 42 publications

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“…Three current pan‐sharpening algorithms are implemented: high‐pass filter, principal component analysis, and Gram–Schmidt transformation. The reasons for choosing these are that they have produced appropriate results in previous studies, that they represent the main types of image pan‐sharpening techniques (Cánovas‐García & Alonso‐Sarría, 2014; Gangkofner et al., 2008; Karathanassi, Kolokousis, & Ioannidou, 2007; Sarp, 2014; Yuhendra et al., 2010; Zhang & Mishra, 2012), and that they are considered by several authors as state‐of‐the‐art pan‐sharpening methods (Snehmani et al., 2016; Vivone et al., 2015).…”

Section: Methodsmentioning

confidence: 99%

“…The quantitative evaluation was carried out using two algorithms: the spectral ERGAS index (or ERGAS index) and the spatial ERGAS index. We are aware that some researchers have used a large number of quality indices (Snehmani et al., 2016; Vivone et al., 2015). However, without wishing to suggest that such an approach is not appropriate, we stress that the goal of the present research was to provide remote sensing practitioners with a powerful, but also rapid and efficient computation tool, for which reason we opted for the simplest quantitative evaluation possible.…”

Section: Methodsmentioning

confidence: 99%

“…The increasing number and availability of high‐resolution optical satellites as well as the ever‐improving revisit cycles allow complementary high‐resolution and MS images to be obtained during the same season and possibly under similar atmospheric and illumination conditions (Yokoya, Grohnfeldt, & Chanussot, 2017). Snehmani, Ganju, Kumar, Srivastava, and Hari Ram (2016) also state that pan‐sharpening is one of the essential steps for improving the image quality of many remote sensing applications and that it is not obvious to non‐specialists how to select one method in preference to others for a given case.…”

Section: Introductionmentioning

confidence: 99%

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fusionImage: An R package for pan‐sharpening images in open source software

García

Pesántez-Cobos

Alonso‐Sarría

2020

Transactions in GIS

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The objective of this article is to evaluate the performance of three pan-sharpening algorithms (high-pass filter, principal component analysis and Gram-Schmidt) to increase the spatial resolution of five types of multispectral images and to evaluate the results in terms of color, coherence and spatial sharpness, both qualitatively and quantitatively. A secondary objective is to present an implementation of the aforementioned pan-sharpening techniques within the open source software R. From a qualitative point of view, pan-sharpening of images with a high spatial resolution ratio give better results than those whose spatial resolution ratio is 2. According

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

fusionImage: An R package for pan‐sharpening images in open source software

García

Pesántez-Cobos

Alonso‐Sarría

2020

Transactions in GIS

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“…In fact, standard weight data set supplies better results, but inertial weight one is comparable with it in order to the performance level. Even if, according to Snehmani et al [27], no single pan sharpening method can be considered the best, introducing different weights for each band of WV-3 images in IHS and Brovey method's better results are achieved. Therefore, the good quality and high geometric resolution (pixel 30 cm) of the multispectral pan-sharpened images allow a better representation of the territory.…”

Section: Discussionmentioning

confidence: 99%

Influence of the weights in IHS and Brovey methods for pan-sharpening WorldView-3 satellite images

Parente

Pepe

2017

IJET

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The purpose of this paper is to investigate the impact of weights in pan-sharpening methods applied to satellite images. Indeed, different data sets of weights have been considered and compared in the IHS and Brovey methods. The first dataset contains the same weight for each band while the second takes in account the weighs obtained by spectral radiance response; these two data sets are most common in pan-sharpening application. The third data set is resulting by a new method. It consists to compute the inertial moment of first order of each band taking in account the spectral response. For testing the impact of the weights of the different data sets, WorlView-3 satellite images have been considered. In particular, two different scenes (the first in urban landscape, the latter in rural landscape) have been investigated. The quality of pan-sharpened images has been analysed by three different quality indexes: Root mean square error (RMSE), Relative average spectral error (RASE) and Erreur Relative Global Adimensionnelle de Synthèse (ERGAS).

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“…The fusion techniques has become very important in remote sensing industry due to their important role in remote sensing problems such as classification [7], change detection [8], object identification [9], hazard monitoring, stereophotogrammetry [10]. Many studies have been published on evaluation of the performance of fusion methods on Ikonos and Quickbird data [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] and a few studies have been presented on Geoeye and Worldview2 data [28][29][30][31]. Some widely performed pan-sharpening algorithms are the Intensity-Hue-Saturation (IHS) [32][33][34], the Principal Component Analysis (PCA) [35][36][37] and the Brovey transform [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Image Fusion Techniques Using Worldview-3 Data

Farhadi¹,

Hoshyarmanesh²

2020

Preprint

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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.

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A comparative analysis of pansharpening techniques on QuickBird and WorldView-3 images

Cited by 31 publications

References 42 publications

fusionImage: An R package for pan‐sharpening images in open source software

fusionImage: An R package for pan‐sharpening images in open source software

Influence of the weights in IHS and Brovey methods for pan-sharpening WorldView-3 satellite images

Comparison of Image Fusion Techniques Using Worldview-3 Data

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