Image fusion by spatially adaptive filtering using downscaling cokriging

Pardo‐Igúzquiza, Eulogio; Rodríguez-Galiano, Víctor; Chica-Olmo, Mario; Atkinson, Peter M.

doi:10.1016/j.isprsjprs.2011.01.001

Cited by 55 publications

(25 citation statements)

References 27 publications

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“…such as Ehlers Fusion [37] may perform better because the pixel values in the Pan and VI images represent different types of information (visible light reflectance and vegetation abundance, respectively). Statistically- [38] or geostatistically-based [39] pansharpening methods may also be better-suited for this type of pansharpening.…”

Section: Future Considerations For Pansharpening VI Imagesmentioning

confidence: 99%

Effects of Pansharpening on Vegetation Indices

Johnson

2014

IJGI

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This study evaluated the effects of image pansharpening on Vegetation Indices (VIs), and found that pansharpening was able to downscale single-date and multi-temporal Landsat 8 VI data without introducing significant distortions in VI values. Four fast pansharpening methods-Fast Intensity-Hue-Saturation (FIHS), Brovey Transform (BT), Additive Wavelet Transform (AWT), and Smoothing Filter-based Intensity Modulation (SFIM)-and two VIs-Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR)-were tested. The NDVI and SR formulas were both found to cause some spatial information loss in the pansharpened multispectral (MS) bands, and this spatial information loss from VI transformations was not specific to Landsat 8 imagery (it will occur for any type of imagery). BT, SFIM, and other similar pansharpening methods that inject spatial information from the panchromatic (Pan) band by multiplication, lose all of the injected spatial information after the VI calculations. FIHS, AWT, and other similar pansharpening methods that inject spatial information by addition, lose some spatial information from the Pan band after VI calculations as well. Nevertheless, for all of the single-and multi-date VI images, the FIHS and AWT pansharpened images were more similar to the higher resolution reference data than the unsharpened VI images were, indicating that pansharpening was effective in downscaling the VI data. FIHS best enhanced the spectral and spatial information of the single-date and multi-date VI images, followed by AWT, and neither significantly over-or under-estimated VI values.

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Section: Future Considerations For Pansharpening VI Imagesmentioning

confidence: 99%

Effects of Pansharpening on Vegetation Indices

Johnson

2014

IJGI

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“…However, linear regression formula is occasionally incapable of representing nonlinear relationships between LST and remote sensing indices. Thus, various models were established to present linear or nonlinear relationships between LST and factors, including piecewise linear and nonlinear regression model [35,36], conditional expectation model [37], co-Kriging model [38,39], Bayesian-based model [40], artificial neural networks [41], genetic algorithm techniques [42], support vector machines [43], and random forest (RF) regression [44]. Notably, Hutengs and Vohland [44] pioneered RF regression with red and near-infrared (NIR) bands, which are related to vegetation index to downscale MODIS LST products in the vegetated regions; this method yielded accurate results.…”

Section: Introductionmentioning

confidence: 99%

Downscaling Land Surface Temperature in an Arid Area by Using Multiple Remote Sensing Indices with Random Forest Regression

et al. 2017

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Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in arid regions (especially in deserts) because of inaccurate remote sensing LST products. In this study, LST was downscaled by a random forest model between LST and multiple remote sensing indices (such as soil-adjusted vegetation index, normalized multi-band drought index, modified normalized difference water index, and normalized difference building index) in an arid region with an oasis-desert ecotone. The proposed downscaling approach, which involves the selection of remote sensing indices, was evaluated using LST derived from the MODIS LST product of Zhangye City in Heihe Basin. The spatial resolution of MODIS LST was downscaled from 1 km to 500 m. Results of visual and quantitative analyses show that the distribution of downscaled LST matched that of the oasis and desert ecosystem. The lowest (approximately 22 • C) and highest temperatures (higher than 37 • C) were detected in the middle oasis and desert regions, respectively. Furthermore, the proposed approach achieves relatively satisfactory downscaling results, with coefficient of determination and root mean square error of 0.84 and 2.42 • C, respectively. The proposed approach shows higher accuracy and minimization of the MODIS LST in the desert region compared with other methods. Optimal availability occurs in the vegetated region during summer and autumn. In addition, the approach is also efficient and reliable for LST downscaling of Landsat images. Future tasks include reliable LST downscaling in challenging regions.

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“…Future research may attempt to segment the image into regions to make the assumptions more reasonable in each region rather than in the whole image. Alternative solutions are to make a non-Gaussian distribution assumption and to consider the spatial correlation as did in the co-kriging downscaling method [90,91], which may need more complicated optimization techniques to solve the models.…”

Section: Discussionmentioning

confidence: 99%

A New Look at Image Fusion Methods from a Bayesian Perspective

Zhang

Huang

2015

Remote Sensing

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Component substitution (CS) and multi-resolution analysis (MRA) are the two basic categories in the extended general image fusion (EGIF) framework for fusing panchromatic (Pan) and multispectral (MS) images. Despite of the method diversity, there are some unaddressed questions and contradictory conclusions about fusion. For example, is the spatial enhancement of CS methods better than MRA methods? Is spatial enhancement and spectral preservation competitive? How to achieve spectral consistency defined by Wald et al. in 1997? In their definition any synthetic image should be as identical as possible to the original image once degraded to its original resolution. To answer these questions, this research first finds out that all the CS and MRA methods can be derived from the Bayesian fusion method by adjusting a weight parameter to balance contributions from the spatial injection and spectral preservation models. The spectral preservation model assumes a Gaussian distribution of the desired high-resolution MS images, with the up-sampled low-resolution MS images comprising the mean value. The spatial injection model assumes a linear correlation between Pan and MS images. Thus the spatial enhancement depends on the weight parameter but is irrelevant of which category (i.e., MRA or CS) the method belongs to. This paper then adds a spectral consistency model in the Bayesian fusion framework to guarantee Wald's spectral consistency with OPEN ACCESSRemote Sens. 2015, 7 6829 regard to arbitrary sensor point spread function. Although the spectral preservation in the EGIF methods is competitive to spatial enhancement, the Wald's spectral consistency property is complementary with spatial enhancement. We conducted experiments on satellite images acquired by the QuickBird and WorldView-2 satellites to confirm our analysis, and found that the performance of the traditional EGIF methods improved significantly after adding the spectral consistency model.

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Image fusion by spatially adaptive filtering using downscaling cokriging

Cited by 55 publications

References 27 publications

Effects of Pansharpening on Vegetation Indices

Effects of Pansharpening on Vegetation Indices

Downscaling Land Surface Temperature in an Arid Area by Using Multiple Remote Sensing Indices with Random Forest Regression

A New Look at Image Fusion Methods from a Bayesian Perspective

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