A hybrid approach for pansharpening using Hilbert vibration decomposition
01 Sep 2017-
TL;DR: Experimental results of the proposed Hybrid pansharpening approach using Hilbert vibration decomposition (HVD) demonstrate that the proposed fusion scheme has improved spectral and spatial quality as compared to the existing schemes.
Abstract: Pansharpening scheme improves the spatial and spectral resolution of the multispectral (MS) images using the Panchromatic (PAN) image. In this paper, a new Hybrid pansharpening approach using Hilbert vibration decomposition (HVD) is proposed. In the proposed method both the MS and PAN images are decomposed into many instantaneous amplitudes and frequency components in the decreasing order of energy using the HVD. The instantaneous amplitude of the first component (having highest energy) in the decomposition of the MS and PAN images are used to generate the pansharpened image using pansharpening model. Experimental results of the proposed technique in terms of both visual perception and objective metrics demonstrate that the proposed fusion scheme has improved spectral and spatial quality as compared to the existing schemes.
Citations
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TL;DR: This paper illustrates the pansharpening approach that is based on multistage multichannel spectral graph wavelet transform and convolutional neural network (SGWT-PNN) and demonstrates the effectiveness of the proposed scheme applied on datasets collected by different satellites.
Abstract: The objective of the multispectral pansharpening scheme is to obtain high spatial-spectral resolution multispectral (MS) images using high spectral resolution MS and high spatial resolution panchro...
7 citations
Cites methods from "A hybrid approach for pansharpening..."
...The combination of both MRA and CS schemes is to obtain high spatial-pansharpened images in hybrid methods as given in Saxena and Sharma (2018); González-Audícana et al. (2004); Saxena and Sharma (2017b)....
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...2015), Hilbert vibration decomposition (HVD) (Saxena and Sharma 2017a), etc....
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TL;DR: In this article , a CNN model is used to extract the PAN detail image that is suitable for the MRA-based pansharpening scheme which significantly reduces the spatial and spectral distortions.
Abstract: Pansharpening produces a high spatial-spectral resolution pansharpened image by combining multispectral (MS) and panchromatic (PAN) images. In the traditional multi-resolution analysis (MRA) method, detailed PAN images are extracted by transformation methods that are injected into MS images. This gives spatial and spectral distortions in the pansharpened image. These distortions can be reduced in the pansharpened image by the correct matching of the PAN detail image component. This correct matching is possible by the convolutional neural network (CNN)–based models. This paper obtains the detailed image component using the CNN models. This CNN model extracts the PAN detail image that is suitable for the MRA-based pansharpening scheme which significantly reduces the spatial and spectral distortions. It is demonstrated by qualitative and quantitative analysis applied on GeoEye-1 and IKONOS satellite images and shows the effectiveness of the proposed scheme.
1 citations
TL;DR: In this paper , a panchromatic image was used to extract spatial detail information of the PAN image to reduce the feature values of the input and applied to the CNN to produce the nonlinear changes in the image pixels and transformed into the perfect spatial detail image.
Abstract: The pansharpening is a combination of multispectral (MS) and panchromatic (PAN) images that produce a high-spatial-spectral-resolution MS images. In multiresolution analysis–based pansharpening schemes, some spatial and spectral distortions are found. It can be reduced by adding spatial detail images of the PAN image into MS images. In the convolution neural network– (CNN) based method, the lowpass filter image extracted by the CNN model when MS and PAN images are directly applied into the input. The feature values are very high and reduce the conversion efficiency. In the proposed scheme, bi-dimensional empirical mode decomposition is used to extract the spatial detail information of the PAN image to reduce the feature values of the input. This extracted PAN image information is applied to the CNN to produce the non-linear changes in the image pixels and transformed into the perfect spatial detail image. It identifies the spatial and spectral detail quantity for the proposed scheme and it also varies with the different datasets automatically of the same satellite images. Simulation results in the context of qualitative and quantitative analysis demonstrate the effectiveness of proposed scheme applied on datasets collected by different satellites.
References
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TL;DR: This work addresses the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image, and uses the K-SVD algorithm to obtain a dictionary that describes the image content effectively.
Abstract: We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itself, or training on a corpus of high-quality image database. Since the K-SVD is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm. This leads to a state-of-the-art denoising performance, equivalent and sometimes surpassing recently published leading alternative denoising methods
5,493 citations
"A hybrid approach for pansharpening..." refers methods in this paper
...techniques which include multi-scale decompositions (MSD) [11] , multi-resolution analysis (MRA) [12], intensity hue saturation (IHS) [13], principal component analysis (PCA) [14], dictionary learning [15], Brovey transform [6], hybrid methods [16], transform domain methods [17] and methods in other domains....
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TL;DR: Although the new index is mathematically defined and no human visual system model is explicitly employed, experiments on various image distortion types indicate that it performs significantly better than the widely used distortion metric mean squared error.
Abstract: We propose a new universal objective image quality index, which is easy to calculate and applicable to various image processing applications. Instead of using traditional error summation methods, the proposed index is designed by modeling any image distortion as a combination of three factors: loss of correlation, luminance distortion, and contrast distortion. Although the new index is mathematically defined and no human visual system model is explicitly employed, our experiments on various image distortion types indicate that it performs significantly better than the widely used distortion metric mean squared error. Demonstrative images and an efficient MATLAB implementation of the algorithm are available online at http://anchovy.ece.utexas.edu//spl sim/zwang/research/quality_index/demo.html.
5,285 citations
"A hybrid approach for pansharpening..." refers background in this paper
...The quality metrics for evaluating the quality of the pansharpened images considered in this paper are Q-index (Q4) [30], spectral angle mapper (SAM) [31], relative dimensionless global error (ERGAS) [28], and quality with no-reference (QNR)....
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TL;DR: The authors developed a technique, based on multiresolution wavelet decomposition, for the merging and data fusion of high-resolution panchromatic and multispectral images which is clearly better than the IHS and LHS mergers in preserving both spectral and spatial information.
Abstract: The standard data fusion methods may not be satisfactory to merge a high-resolution panchromatic image and a low-resolution multispectral image because they can distort the spectral characteristics of the multispectral data. The authors developed a technique, based on multiresolution wavelet decomposition, for the merging and data fusion of such images. The method presented consists of adding the wavelet coefficients of the high-resolution image to the multispectral (low-resolution) data. They have studied several possibilities concluding that the method which produces the best results consists in adding the high order coefficients of the wavelet transform of the panchromatic image to the intensity component (defined as L=(R+G+B)/3) of the multispectral image. The method is, thus, an improvement on standard intensity-hue-saturation (IHS or LHS) mergers. They used the "a trous" algorithm which allows the use of a dyadic wavelet to merge nondyadic data in a simple and efficient scheme. They used the method to merge SPOT and LANDSAT/sup TM/ images. The technique presented is clearly better than the IHS and LHS mergers in preserving both spectral and spatial information.
1,151 citations
"A hybrid approach for pansharpening..." refers methods in this paper
...In the hybrid pansharpening methods [7], [9], [10] make use of both CS and MRA techniques....
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TL;DR: The authors attempt to fill the gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods by offering a detailed comparison of their performances with respect to the different instruments.
Abstract: Pansharpening aims at fusing a multispectral and a panchromatic image, featuring the result of the processing with the spectral resolution of the former and the spatial resolution of the latter. In the last decades, many algorithms addressing this task have been presented in the literature. However, the lack of universally recognized evaluation criteria, available image data sets for benchmarking, and standardized implementations of the algorithms makes a thorough evaluation and comparison of the different pansharpening techniques difficult to achieve. In this paper, the authors attempt to fill this gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods. In greater details, several pansharpening algorithms belonging to the component substitution or multiresolution analysis families are considered. Such techniques are evaluated through the two main protocols for the assessment of pansharpening results, i.e., based on the full- and reduced-resolution validations. Five data sets acquired by different satellites allow for a detailed comparison of the algorithms, characterization of their performances with respect to the different instruments, and consistency of the two validation procedures. In addition, the implementation of all the pansharpening techniques considered in this paper and the framework used for running the simulations, comprising the two validation procedures and the main assessment indexes, are collected in a MATLAB toolbox that is made available to the community.
980 citations
"A hybrid approach for pansharpening..." refers methods in this paper
...According to this protocol, the PAN and MS images are degraded to the lower resolution to compare the pansharpened image with the reference original MS images [29]....
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TL;DR: Multivariate regression is adopted to improve spectral quality, without diminishing spatial quality, in image fusion methods based on the well-established component substitution (CS) approach and quantitative scores carried out on spatially degraded data clearly confirm the superiority of the enhanced methods over their baselines.
Abstract: In this paper, multivariate regression is adopted to improve spectral quality, without diminishing spatial quality, in image fusion methods based on the well-established component substitution (CS) approach. A general scheme that is capable of modeling any CS image fusion method is presented and discussed. According to this scheme, a generalized intensity component is defined as the weighted average of the multispectral (MS) bands. The weights are obtained as regression coefficients between the MS bands and the spatially degraded panchromatic (Pan) image, with the aim of capturing the spectral responses of the sensors. Once it has been integrated into the Gram-Schmidt spectral-sharpening method, which is implemented in environment for visualizing images (ENVI) program, and into the generalized intensity-hue-saturation fusion method, the proposed preprocessing module allows the production of fused images of the same spatial sharpness but of increased spectral quality with respect to the standard implementations. In addition, quantitative scores carried out on spatially degraded data clearly confirm the superiority of the enhanced methods over their baselines.
895 citations
"A hybrid approach for pansharpening..." refers methods in this paper
...The injection coefficients Gr are calculated as [27]...
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