In this study, the authors address the fusion of low-resolution multi-spectral image with the corresponding high-resolution panchromatic image to provide high-resolution multi-spectral (HRM) one, i.e. pan sharpening. ...
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In this study, the authors address the fusion of low-resolution multi-spectral image with the corresponding high-resolution panchromatic image to provide high-resolution multi-spectral (HRM) one, i.e. pan sharpening. The intensity-hue-saturation (IHS)-based pan-sharpening methods are popular because they are simple, efficient, and of high-spatial quality. However, their frameworks are unavoidably subject to spectral distortion. To reduce the inevitable spectral distortion of IHS-based pan-sharpening approaches, the spectral consistency constraint is used in the proposed method. Moreover, to stabilise fusion results obtained from the ill-posed pan-sharpening problem and to keep the smoothness of the HRM image, a total variation regularisation term is considered. These considerations are formulated in a non-quadratic optimisation problem. To solve this problem, a kind of variable splitting method, known as half-quadratic approximation is utilised, and also an alternating optimisation procedure is used to reconstruct HRM image. To gain convenient control on the local spectral and the spatial information, and also to reduce the required memory, in the optimisation stage, the patch-based strategy is employed. The proposed method was tested on two datasets acquired by GeoEye-1 and Pleiades satellites. To evaluate the proposed method, visual assessment, as well as quantitative comparison with different pan-sharpening methods, was carried out.
In this paper, we propose a method for fusion of low-resolutionmultispectral (LRM) image and high-resolution panchromatic (HRP) image to obtain high-resolutionmultispectral (HRM) image based on distributed compresse...
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ISBN:
(纸本)9781479970612
In this paper, we propose a method for fusion of low-resolutionmultispectral (LRM) image and high-resolution panchromatic (HRP) image to obtain high-resolutionmultispectral (HRM) image based on distributed compressed sensing (DCS). In the proposed method, HRP image is firstly used to obtain approximation and detail dictionary. Then, joint-sparsity-model-1 (JSM-1) is applied directly to both LRM bands and HRM bands. Each band in LRM image is decomposed into common component and innovation component which can be sparsely represented over the approximation dictionary. Based on Orthogonal Matching Pursuit (OMP) algorithm, the sparse coefficients are calculated from JSM-1 of the LRM image. Lastly, each band in HRM image is modeled as the fusion of the corresponding LRM band and detail band over the detail dictionary. Two datasets are used in the experiments to validate the proposed method and the results show that the proposed method has better performance than the traditional methods.
The remote sensing image fusion is an important preprocessing technique in remote sensing image processing. In this paper, a remote sensing image fusion method based on the nonsubsampled shearlet transform (NSST) with...
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The remote sensing image fusion is an important preprocessing technique in remote sensing image processing. In this paper, a remote sensing image fusion method based on the nonsubsampled shearlet transform (NSST) with sparse representation (SR) is proposed. Firstly, the lowresolutionmultispectral (MS) image is upsampled and color space is transformed from Red-Green-Blue (RGB) to Intensity-Hue-Saturation (IHS). Then, the high resolution panchromatic (PAN) image and intensity component of MS image are decomposed by NSST to high and low frequency coefficients. The low frequency coefficients of PAN and the intensity component are fused by the SR with the learned dictionary. The high frequency coefficients of intensity component and PAN image are fused by local energy based fusion rule. Finally, the fused result is obtained by performing inverse NSST and inverse IHS transform. The experimental results on IKONOS and QuickBird satellites demonstrate that the proposed method provides better spectral quality and superior spatial information in the fused image than other remote sensing image fusion methods both in visual effect and object evaluation.
A new algorithm is developed to merge a high-resolution panchromatic image and a low-resolution multispectral image based on the combination of multiresolution wavelet decomposition, evolutionary strategy and the IHS ...
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ISBN:
(纸本)0780386531
A new algorithm is developed to merge a high-resolution panchromatic image and a low-resolution multispectral image based on the combination of multiresolution wavelet decomposition, evolutionary strategy and the IHS transform. The high-resolution panchromatic image is firstly decomposed to the wavelet planes, then the regions are partitioned by evolutionary strategy in terms of difference of edge information from wavelet planes and the merging algorithm is done by adding edge influence factor in different region. The proposed method is compared with the IHS and the MWT methods. The results of the comparison show the proposed merger performing the best in combining and preserving spectral-spatial infonnation for the test images.
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