Improved Pansharpening with Un-Mixing of Mixed MS Sub-Pixels near Boundaries between Vegetation and Non-Vegetation Objects

Li, Hui; Jing, Linhai; Wei, Liming; Cheng, Qiuming

doi:10.3390/rs8020083

Cited by 6 publications

(6 citation statements)

References 44 publications

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“…The pan-sharpening methods can be generalised as the injection of spatial details derived from the PAN image into the up-sampled MS images to obtain high spatial resolution MS images. Currently, the focus is on reducing the spectral distortions of fused images, optimising the spatial details derived from the PAN image, as well as optimising the weights by which the spatial details are multiplied during the injection [39].…”

Section: Pan-sharpening Methods and Product Evaluation 21 Pan-sharpening Methodsmentioning

confidence: 99%

Automation of Pan-Sharpening Methods for Pléiades Images Using GIS Basic Functions

2021

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Pan-sharpening methods allow the transfer of higher resolution panchromatic images to multispectral ones concerning the same scene. Different approaches are available in the literature, and only a part of these approaches is included in remote sensing software for automatic application. In addition, the quality of the results supplied by a specific method varies according to the characteristics of the scene; for consequence, different algorithms must be compared to find the best performing one. Nevertheless, pan-sharpening methods can be applied using GIS basic functions in the absence of specific pan-sharpening tools, but this operation is expensive and time-consuming. This paper aims to explain the approach implemented in Quantum GIS (QGIS) for automatic pan-sharpening of Pléiades images. The experiments are carried out on data concerning the Greek island named Lesbo. In total, 14 different pan-sharpening methods are applied to reduce pixel dimensions of the four multispectral bands from 2 m to 0.5 m. The automatic procedure involves basic functions already included in GIS software; it also permits the evaluation of the quality of the resulting images supplying the values of appropriate indices. The results demonstrate that the approach provides the user with the highest performing method every time, so the best possible fused products are obtained with minimal effort in a reduced timeframe.

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Section: Pan-sharpening Methods and Product Evaluation 21 Pan-sharpening Methodsmentioning

confidence: 99%

Automation of Pan-Sharpening Methods for Pléiades Images Using GIS Basic Functions

2021

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“…The image fusion algorithm of PCI Geomatica preserves the original color accuracy in the high‐resolution color image obtained as a result of fusing and allows for better visualization and interpretation 53 . The spectral distortion problem in the fused image, which is frequently encountered in other fusion methods, is significantly reduced with this method and a more effective fusion process is realized 51,54 . In addition, the fusion methods applied to the images were investigated by conducting a literature study, and since it was stated that the automatic image fusion algorithm used by PCI Geomatica gave successful results in the images and the spectral distortion was less the automatic image fusion algorithm was chosen for this study 55–57 …”

Section: Methodsmentioning

confidence: 99%

“…53 The spectral distortion problem in the fused image, which is frequently encountered in other fusion methods, is significantly reduced with this method and a more effective fusion process is realized. 51,54 In addition, the fusion methods applied to the images were investigated by conducting a literature study, and since it was stated that the automatic image fusion algorithm used by PCI Geomatica gave successful results in the images and the spectral distortion was less the automatic image fusion algorithm was chosen for this study. [55][56][57] fused MSUAV orthophoto was obtained by image fusion of the WV-2 MS bands and the mean UAV orthophoto (Figure 5).…”

Section: Automatic Image Fusion Algorithmmentioning

confidence: 99%

Improving the accuracy of random forest‐based land‐use classification using fused images and digital surface models produced via different interpolation methods

Akar

2021

Concurrency and Computation

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Land-use maps produced with high accuracy are extremely important as a basis for better use of the land as they are widely utilized in many areas such as agricultural policies, natural resources management, and environmental operations. This study aimed to produce a high accuracy land-use map using digital surface models (DSMs) produced using different interpolation methods and a random forest (RF) classifier. High spatial resolution Triplesat-2 images, Worldview-2 (WV-2) images, and unmanned aerial vehicle (UAV) images were used in this study. First, DSMs were produced from the point cloud using different interpolation methods. The image fusion process was applied and these fused images were classified using an RF classifier together with the DSMs. Overall results showed that the DSM obtained by the kriging method yielded better results than the other methods by increasing the classification accuracy by 7%-13%. In addition, the McNemar test was applied to the images resulting from the classification of fused images with and without using DSMs to investigate the statistical significance of the differences. McNemar test demonstrated that the 𝜒 2 values were greater than 3.84, which revealed that the DSM produced via kriging interpolation had significantly increased the classification accuracy at a 95% confidence interval.

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“…However, restricting this assumption in a spatial homogenous area is always a better option. To this aim, some authors have improved CS methods by restricting this assumption in a local sliding window [44][45][46], in a group of homogenous pixels after image classification [47,48], or paying attention to the mixed pixels [49].…”

Section: The Usefulness Of the Revealed Statistical Assumptionsmentioning

confidence: 99%

Revealing Implicit Assumptions of the Component Substitution Pansharpening Methods

2017

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The component substitution (CS) pansharpening methods have been developed for almost three decades and have become better understood recently by generalizing them into one framework. However, few studies focus on the statistical assumptions implicit in the CS methods. This paper reveals their implicit statistical assumptions from a Bayesian data fusion framework and suggests best practices for histogram matching of the panchromatic image to the intensity image, a weighted summation of the multispectral images, to better satisfy these assumptions. The purpose of histogram matching was found to make the difference between the high-resolution panchromatic and intensity images as small as possible, as one implicit assumption claims their negligible difference. The statistical relationship between the high-resolution panchromatic and intensity images and the relationship between their corresponding low-resolution images are the same, as long as the low resolution panchromatic image is derived by considering the modulation transfer functions of the multispectral sensors. Hence, the histogram-matching equation should be derived from the low-resolution panchromatic and intensity images, but not derived from the high-resolution panchromatic and expanded low-resolution intensity images. Experiments using three example CS methods, each using the two different histogram-matching equations, was conducted on the four-band QuickBird and eight-band WorldView-2 top-of-atmosphere reflectance data. The results verified the best practices and showed that the histogram-matching equation derived from the high-resolution panchromatic and expanded low-resolution intensity images provides more-blurred histogram-matched panchromatic image and, hence less-sharpened pansharpened images than that derived from the low-resolution image pair. The usefulness of the assumptions revealed in this study for method developers is discussed. For example, the CS methods can be improved by satisfying the assumptions better, e.g., classifying the images into homogenous areas before pansharpening, and by changing the assumptions to be more general to address their deficiencies.

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Improved Pansharpening with Un-Mixing of Mixed MS Sub-Pixels near Boundaries between Vegetation and Non-Vegetation Objects

Cited by 6 publications

References 44 publications

Automation of Pan-Sharpening Methods for Pléiades Images Using GIS Basic Functions

Automation of Pan-Sharpening Methods for Pléiades Images Using GIS Basic Functions

Improving the accuracy of random forest‐based land‐use classification using fused images and digital surface models produced via different interpolation methods

Revealing Implicit Assumptions of the Component Substitution Pansharpening Methods

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