A New Look at Image Fusion Methods from a  Bayesian Perspective

Zhang, Hankui K.; Huang, Bo

doi:10.3390/rs70606828

Cited by 62 publications

(38 citation statements)

References 86 publications

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“…B1 60 443 20 B2 10 490 65 B3 10 560 35 B4 10 665 30 B5 20 705 15 B6 20 740 15 B7 20 783 20 B8 10 842 115 B8A 20 865 20 B9 60 945 20 B10 60 1375 30 B11 20 1610 90 B12 20 2190 180 An alternative and advisable way to enhance the performance of water bodies' mapping using the Sentinel-2 imagery is to produce MNDWI at the 10-m resolution by downscaling the SWIR band (Band 11) from 20 m to 10 m. Obviously, the key issue is how to increase the spatial resolution of the SWIR band accurately. In general, spatial interpolation [30,31] and image fusion [32,33] (e.g., pan-sharpening [34]) are the two most popular kinds of methods applied to increase the spatial resolution of remote sensing imagery. The spatial interpolation method is always applied to coarse spatial resolution images directly and does not use any additional datasets.…”

Section: Introductionmentioning

confidence: 99%

Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

et al. 2016

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Abstract:Monitoring open water bodies accurately is an important and basic application in remote sensing. Various water body mapping approaches have been developed to extract water bodies from multispectral images. The method based on the spectral water index, especially the Modified Normalized Difference Water Index (MDNWI) calculated from the green and Shortwave-Infrared (SWIR) bands, is one of the most popular methods. The recently launched Sentinel-2 satellite can provide fine spatial resolution multispectral images. This new dataset is potentially of important significance for regional water bodies' mapping, due to its free access and frequent revisit capabilities. It is noted that the green and SWIR bands of Sentinel-2 have different spatial resolutions of 10 m and 20 m, respectively. Straightforwardly, MNDWI can be produced from Sentinel-2 at the spatial resolution of 20 m, by upscaling the 10-m green band to 20 m correspondingly. This scheme, however, wastes the detailed information available at the 10-m resolution. In this paper, to take full advantage of the 10-m information provided by Sentinel-2 images, a novel 10-m spatial resolution MNDWI is produced from Sentinel-2 images by downscaling the 20-m resolution SWIR band to 10 m based on pan-sharpening. Four popular pan-sharpening algorithms, including Principle Component Analysis (PCA), Intensity Hue Saturation (IHS), High Pass Filter (HPF) and À Trous Wavelet Transform (ATWT), were applied in this study. The performance of the proposed method was assessed experimentally using a Sentinel-2 image located at the Venice coastland. In the experiment, six water indexes, including 10-m NDWI, 20-m MNDWI and 10-m MNDWI, produced by four pan-sharpening algorithms, were compared. Three levels of results, including the sharpened images, the produced MNDWI images and the finally mapped water bodies, were analysed quantitatively. The results showed that MNDWI can enhance water bodies and suppressbuilt-up features more efficiently than NDWI. Moreover, 10-m MNDWIs produced by all four pan-sharpening algorithms can represent more detailed spatial information of water bodies than 20-m MNDWI produced by the original image. Thus, MNDWIs at the 10-m resolution can extract more accurate water body maps than 10-m NDWI and 20-m MNDWI. In addition, although HPF can produce more accurate sharpened images and MNDWI images than the other three benchmark pan-sharpening algorithms, the ATWT algorithm leads to the best 10-m water bodies mapping results. This is no necessary positive connection between the accuracy of the sharpened MNDWI image and the map-level accuracy of the resultant water body maps.

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Section: Introductionmentioning

confidence: 99%

Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

et al. 2016

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“…Downscaling using panchromatic bands has been studied for several decades and the detailed critical surveys can be found in [40][41][42]. Such methods are often grouped into component substitution (CS) and multiresolution analysis (MRA) methods [36,41,43,44].…”

Section: Downscaling Landsat-8 30-m Data To 15 M Using the Panchromatmentioning

confidence: 99%

Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

Zhang

Roy

et al. 2017

Remote Sensing

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Abstract:The Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) method to downscale Landsat-8 Operational Land Imager (OLI) 30-m data to Sentinel-2 multi-spectral instrument (MSI) 20-m resolution is presented. The method first downscales the Landsat-8 30-m OLI bands to 15-m using the spatial detail provided by the Landsat-8 15-m panchromatic band and then reprojects and resamples the downscaled 15-m data into registration with Sentinel-2A 20-m data. The LPAD method is demonstrated using pairs of contemporaneous Landsat-8 OLI and Sentinel-2A MSI images sensed less than 19 min apart over diverse geographic environments. The LPAD method is shown to introduce less spectral and spatial distortion and to provide visually more coherent data than conventional bilinear and cubic convolution resampled 20-m Landsat OLI data. In addition, results for a pair of Landsat-8 and Sentinel-2A images sensed one day apart suggest that image fusion should be undertaken with caution when the images are acquired under different atmospheric conditions. The LPAD source code is available at GitHub for public use.

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“…F is a total variation in (Palsson et al 2014), ERM3 is applied only on high frequency components of signal and F is a spectral correlation dependent regularization (Aly and Sharma 2015). Moreover, the methods can be formulated using a Bayesian (Fasbender et al 2008, Zhang et al 2012, Zhang and Huang 2015 or sparsity regularization frameworks (Li and Yang 2011, Zhu and Bamler 2013, Vicinanza et al 2015. Further, unmixing models can be included into the cost function definition as, e.g.…”

Section: Minimization Of Model Error Residualsmentioning

confidence: 99%

“…In recent review (Pohl and van Genderen 2015), which is an update of the old review (Pohl and van Genderen 1998), methods are divided into five groups: CS, numerical and statistical image fusion, modulation-based techniques, multi-resolution approaches and hybrid techniques. In (Zhang and Huang 2015) a new look at image fusion methods from a Bayesian perspective is presented, which shows that CS and MRA based methods are special cases of Bayesian based method under Gaussian model assumption.…”

Section: Introductionmentioning

confidence: 99%

Pan-Sharpening Approaches Based on Unmixing of Multispectral Remote Sensing Imagery

Palubinskas¹

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VII, WG VII/6KEY WORDS: image processing, multi-resolution image fusion, pan-sharpening, quality assessment, model based approach, remote sensing ABSTRACT:Model based analysis or explicit definition/listing of all models/assumptions used in the derivation of a pan-sharpening method allows us to understand the rationale or properties of existing methods and shows a way for a proper usage or proposal/selection of new methods 'better' satisfying the needs of a particular application. Most existing pan-sharpening methods are based mainly on the two models/assumptions: spectral consistency for high resolution multispectral data (physical relationship between multispectral and panchromatic data in a high resolution scale) and spatial consistency for multispectral data (so-called Wald's protocol first property or relationship between multispectral data in different resolution scales). Two methods, one based on a linear unmixing model and another one based on spatial unmixing, are described/proposed/modified which respect models assumed and thus can produce correct or physically justified fusion results. Earlier mentioned property 'better' should be measurable quantitatively, e.g. by means of so-called quality measures. The difficulty of a quality assessment task in multi-resolution image fusion or pan-sharpening is that a reference image is missing. Existing measures or so-called protocols are still not satisfactory because quite often the rationale or assumptions used are not valid or not fulfilled. From a model based view it follows naturally that a quality assessment measure can be defined as a combination of error model residuals using common or general models assumed in all fusion methods. Thus in this paper a comparison of the two earlier proposed/modified pan-sharpening methods is performed. Preliminary experiments based on visual analysis are carried out in the urban area of Munich city for optical remote sensing multispectral data and panchromatic imagery of the WorldView-2 satellite sensor.

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A New Look at Image Fusion Methods from a Bayesian Perspective

Cited by 62 publications

References 86 publications

Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

Pan-Sharpening Approaches Based on Unmixing of Multispectral Remote Sensing Imagery

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