Some Aspects of Satellite Imagery Integration From Eros B and Landsat 8

Fryśkowska, Anna; Wojtkowska, Michalina; Delis, P.; Grochala, A.

doi:10.5194/isprsarchives-xli-b7-647-2016

Cited by 12 publications

(12 citation statements)

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“…The ratio of the spatial resolution was 1:60, so a value, which significantly reduces the correlation between the original MS image and the post-fusion image. When integrating data from the same satellite, the correlation coefficient is usually in the range 0.80-1.00 [42][43][44], whereas, with data from different sensors, the value drops to the range of 0.60-0.85 [14], depending on the method used. This is closely related to the fact that the spectral quality deteriorates as the GSD ratio of the integrated images increases.…”

Section: Resultsmentioning

confidence: 99%

Section: Analysis Of Correlation Coefficientmentioning

confidence: 99%

“…An evaluation of the correlation coefficients had shown, the best results were obtained for the HPF method, where the values are highest (mean 0.86). However, based on previous studies [14], it is known that the high spectral quality of the HPF method also leads to low spatial quality. Much better spatial results can be obtained using the PCA method with not much lower correlation values.…”

Section: Analysis Of Correlation Coefficientmentioning

confidence: 99%

“…The quality of the pansharpened imagery data obtained using the PAN from EROS B and eight multispectral channels from Landsat were also investigated. The worst results were reached when using the IHS, PCA and Brovey transform methods, and the HPF method gave the best results, with the correlation coefficient between the fused imagery and the original multispectral image ranging from 0.25 to 0.75 depending on the method and area [14]. These results indicate a much lower spectral quality of the sharpened images when the resolution ratio is high, therefore, in this article, we try to solve the problem presented by proposing a new algorithm.…”

Section: Introductionmentioning

confidence: 99%

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A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion

Grochala

Kędzierski

2017

Remote Sensing

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The standard ratio of spatial resolution between bands for high resolution satellites is 1:4, which is typical when combining images obtained from the same sensor. However, the cost of simultaneously purchasing a set of panchromatic and multispectral images is still relatively high. There is therefore a need to develop methods of data fusion of very high resolution panchromatic imagery with low-cost multispectral data (e.g., Landsat). Combining high resolution images with low resolution images broadens the scope of use of satellite data, however, it is also accompanied by the problem of a large ratio between spatial resolutions, which results in large spectral distortions in the merged images. The authors propose a modification of the panchromatic image in such a way that it includes the spectral and spatial information from both the panchromatic and multispectral images to improve the quality of spectral data integration. This fusion is done based on a weighted average. The weight is determined using a coefficient, which determines the ratio of the amount of information contained in the corresponding pixels of the integrated images. The effectiveness of the author's algorithm had been tested for six of the most popular fusion methods. The proposed methodology is ideal mainly for statistical and numerical methods, especially Principal Component Analysis and Gram-Schmidt. The author's algorithm makes it possible to lower the root mean square error by up to 20% for the Principal Component Analysis. The spectral quality was also increased, especially for the spectral bands extending beyond the panchromatic image, where the correlation rose by 18% for the Gram-Schmidt orthogonalization.

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Section: Resultsmentioning

confidence: 99%

Section: Analysis Of Correlation Coefficientmentioning

confidence: 99%

Section: Analysis Of Correlation Coefficientmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion

Grochala

Kędzierski

2017

Remote Sensing

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“…Also, data fusion images with a smaller spatial resolution ratio (e.g., 1/3) have been created, but these images come from different providers of satellite data such as e.g., Spot 10 m PAN with Landsat TM 30 m MS [12]. Finally, relatively recently, there have been image fusion attempts with a much higher spatial resolution ratio (e.g., 1/60), also from different satellite data providers such as, for example, Eros B 0.7 m PAN with Landsat-8 30 m MS [13], WorldView-2 0.5 m PAN with Landsat-8 30 m MS [14]. For all the above-mentioned image fusion cases, different methodologies and techniques have been developed over time, which in many cases, gave the same or even better results (in the sense of maintaining the same or more of the initial spectral information) of the previous methodologies and techniques of image fusion.…”

Section: Introductionmentioning

confidence: 99%

Data Fusion of Scanned Black and White Aerial Photographs with Multispectral Satellite Images

Kaimaris¹,

Πατιάς²,

Mallinis³

et al. 2019

Sci

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To date, countless satellite image fusions have been made, mainly with panchromatic spatial resolution to a multispectral image ratio of 1/4, fewer fusions with lower ratios, and relatively recently fusions with much higher spatial resolution ratios have been published. Apart from this, there is a small number of publications studying the fusion of aerial photographs with satellite images, with the year of image acquisition varying and the dates of acquisition not mentioned. In addition, in these publications, either no quantitative controls are performed on the composite images produced, or the aerial photographs are recent and colorful and only the RGB bands of the satellite images are used for data fusion purposes. The objective of this paper is the study of the addition of multispectral information from satellite images to black and white aerial photographs of the 2nd half of the 20th century (1950–1999) with small difference (just a few days) in their image acquisition date, the same year and season. Quantitative tests are performed in two case studies and the results are encouraging, as the accuracy of the classification of the features and objects of the Earth’s surface is improved and the automatic digital extraction of their form and shape from the archived aerial photographs is now allowed. This opens up a new field of use for the black and white aerial photographs and archived multispectral satellite images of the same period in a variety of applications, such as the temporal changes of cities, forests and archaeological sites.

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