IKONOS image fusion process using steepest descent method with bi-linear interpolation

Kalpoma, KaziA.; Kawano, K.; Kudoh, J.

doi:10.1080/01431161.2012.712233

Cited by 13 publications

(4 citation statements)

References 14 publications

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“…Generally, bilinear resampling can exactly reconstruct at most a first-degree polynomial, whereas cubic convolution resampling has the potential to reconstruct exactly any second-degree polynomial [48]. Although the difference between the bilinear and cubic convolution resamplers has been found to be negligible for certain remote sensing applications such as supervised classification [49] cubic convolution is recommended for pansharpening applications [21,50,51]. Consequently, in this study, the cubic convolution resampler was used to resample the 30 m data to 15 m.…”

Section: Resampling Landsat 8 30 M Multispectral Bands To 15 M Panchrmentioning

confidence: 99%

Computationally Inexpensive Landsat 8 Operational Land Imager (OLI) Pansharpening

Zhang

Roy

2016

Remote Sensing

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Pansharpening algorithms fuse higher spatial resolution panchromatic with lower spatial resolution multispectral imagery to create higher spatial resolution multispectral images. The free-availability and systematic global acquisition of Landsat 8 data indicate an expected need for global coverage and so computationally efficient Landsat 8 pansharpening. This study adapts and evaluates the established, and relatively computationally inexpensive, Brovey and context adaptive Gram Schmidt component substitution (CS) pansharpening methods for application to the Landsat 8 15 m panchromatic and 30 m red, green, blue, and near-infrared bands. The intensity images used by these CS pansharpening methods are derived as a weighted linear combination of the multispectral bands in three different ways using band spectral weights set (i) equally as the reciprocal of the number of bands; (ii) using fixed Landsat 8 spectral response function based (SRFB) weights derived considering laboratory spectra; and (iii) using image specific spectral weights derived by regression between the multispectral and the degraded panchromatic bands. The spatial and spectral distortion and computational cost of the different methods are assessed using Landsat 8 test images acquired over agricultural scenes in South Dakota, China, and India. The results of this study indicate that, for global Landsat 8 application, the context adaptive Gram Schmidt pansharpening with an intensity image defined using the SRFB spectral weights is appropriate. The context adaptive Gram Schmidt pansharpened results had lower distortion than the Brovey results and the least distortion was found using intensity images derived using the SRFB and image specific spectral weights but the computational cost using the image specific weights was greater than the using the SRFB weights. Recommendations for large area Landsat 8 pansharpening application are described briefly and the SRFB spectral weights are provided so users may implement computationally inexpensive Landsat 8 pansharpening themselves.

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Section: Resampling Landsat 8 30 M Multispectral Bands To 15 M Panchrmentioning

confidence: 99%

Computationally Inexpensive Landsat 8 Operational Land Imager (OLI) Pansharpening

Zhang

Roy

2016

Remote Sensing

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“…In this paper, several commonly known objective quality indexes, including correlation coefficient (CC) [4], spatial CC (SCC) [5], spectral angle mapper (SAM) [6], Q4 [7], and QNR [8] are adopted to evaluate the sharpened products. QNR uses the original MS and PAN images as the references, and the other indexes use the bicubic interpolated MS image as the reference.…”

Section: Test Data and Evaluation Indexmentioning

confidence: 99%

Using Guided Filtering to Improve Gram-Schmidt Based Pansharpening Method for GeoEye-1 Satellite Images

Xu¹,

Zhang²,

Gao³

et al. 2016

Proceedings of the 4th International Conference on Information Systems and Computing Technology

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Gram-Schmidt based spectral pansharpening is a well-known scheme for fusion of panchromatic (PAN) and multispectral (MS) images. However, it relies more on the relative spectral responses between the PAN and MS sensors, which may easily suffer from spectral distortions. To deal with this problem, we present a new Gram-Schmidt based pansharpening method using guided filtering to improve the sharpened quality. The spatial information of PAN image including details and structures are effectively extracted through guided filtering and then injected into the MS imagery. The experiment is carried out on GeoEye-1 satellite images. Visual and objective analysis show that our method can produce high-quality pansharpened results and outperform some existing methods.

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“…The resolution of each level is different, and the number of pixels in current level i is half of that in previous level i − 1. Therefore, C A 3 is first up-sampled by bilinear interpolation 22 to have the same size as C A 2.…”

Section: -D-dwtmentioning

confidence: 99%

Wavelet domain compounding for speckle reduction in optical coherence tomography

Sun

et al. 2013

J. Biomed. Opt

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Visibility of optical coherence tomography (OCT) images can be severely degraded by speckle noise. A computationally efficient despeckling approach that strongly reduces the speckle noise is reported. It is based on discrete wavelet transform (DWT), but eliminates the conventional process of threshold estimation. By decomposing an image into different levels, a set of sub-band images are generated, where speckle noise is additive. These sub-band images can be compounded to suppress the additive speckle noise, as DWT coefficients resulting from speckle noise tend to be approximately decorrelated. The final despeckled image is reconstructed by taking the inverse wavelet transform of the new compounded sub-band images. The performance of speckle reduction and edge preservation is controlled by a single parameter: the level of wavelet decomposition. The proposed technique is applied to intravascular OCT imaging of porcine carotid arterial wall and ophthalmic OCT images. Results demonstrate the effectiveness of this technique for speckle noise reduction and simultaneous edge preservation. The presented method is fast and easy to implement and to improve the quality of OCT images.

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IKONOS image fusion process using steepest descent method with bi-linear interpolation

Cited by 13 publications

References 14 publications

Computationally Inexpensive Landsat 8 Operational Land Imager (OLI) Pansharpening

Computationally Inexpensive Landsat 8 Operational Land Imager (OLI) Pansharpening

Using Guided Filtering to Improve Gram-Schmidt Based Pansharpening Method for GeoEye-1 Satellite Images

Wavelet domain compounding for speckle reduction in optical coherence tomography

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