2021
DOI: 10.3390/s21051756
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Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion

Abstract: The rapid development of remote sensing and space technology provides multisource remote sensing image data for earth observation in the same area. Information provided by these images, however, is often complementary and cooperative, and multisource image fusion is still challenging. This paper proposes a novel multisource remote sensing image fusion algorithm. It integrates the contrast saliency map (CSM) and the sum-modified-Laplacian (SML) in the nonsubsampled shearlet transform (NSST) domain. The NSST is … Show more

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Cited by 16 publications
(12 citation statements)
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References 49 publications
(44 reference statements)
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“…After IHS color space transformation, components and of the MS image remain unchanged, and component of the MS image with spatial details is used for histogram matching with the PAN image containing richer spatial details; the grayscale value of the PAN image is then calculated, and the histogram is equalized with the component of the MS image, as expressed in Equation (8). According to the correspondence between and , the gray level of the PAN image is adjusted to obtain with a higher degree of matching with the original image and is matched with the PAN image histogram to obtain [ 35 , 36 ]. where denotes the PAN image grayscale value, denotes the probability estimate, and denotes the sum of .…”
Section: Methodsmentioning
confidence: 99%
“…After IHS color space transformation, components and of the MS image remain unchanged, and component of the MS image with spatial details is used for histogram matching with the PAN image containing richer spatial details; the grayscale value of the PAN image is then calculated, and the histogram is equalized with the component of the MS image, as expressed in Equation (8). According to the correspondence between and , the gray level of the PAN image is adjusted to obtain with a higher degree of matching with the original image and is matched with the PAN image histogram to obtain [ 35 , 36 ]. where denotes the PAN image grayscale value, denotes the probability estimate, and denotes the sum of .…”
Section: Methodsmentioning
confidence: 99%
“…Liu et al [20] introduced a change detection method based on mathematical morphology and a k-means clustering model, and the accuracy of the change detection improved. The nonsubsampled contourlet transform (NSCT) and nonsubsampled shearlet transform (NSST) are widely used in image fusion and denoising [21][22][23][24][25][26][27]. Chen et al [28] introduced the NSCT-hidden Markov tree (NSCT-HMT) model to the remote sensing image change detection; Li et al [29] proposed a multitemporal remote sensing image change detection algorithm based on the NSCT denoising model.…”
Section: Sar Image Preprocessingmentioning
confidence: 99%
“…It is necessary to improve the NSCT's multi-scale decomposition processing efficiency. Research has found that image blurring and poor contrast in various types of images can be significantly improved by the shearlet transform and the enhanced non-subsampled shearlet transform (NSST) [23,24]. In addition, the NSST achieves better contrast enhancement and clear target contours.…”
Section: Introductionmentioning
confidence: 99%