Regularised IHS‐based pan‐sharpening approach using spectral consistency constraint and total variation

Khateri, Mohammad; Shabanzade, Fahim; Mirzapour, Fardin

doi:10.1049/iet-ipr.2019.0283

Cited by 5 publications

(3 citation statements)

References 50 publications

(81 reference statements)

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“…Now, the majorizer of structural and anisotropic TV terms could be replaced with the actual ones in (9). It's worth to mention that the last term in (17) and (20) are independent of the variable M H,B , so in the optimization problem they are considered as a constant.…”

Section: G Solving Objective Function Via MM Algorithmmentioning

confidence: 99%

“…The well-known CS-based methods are principal component analysis (PCA) [9], Gram-Schmidt (GS) [10], intensity-hue-saturation (IHS) [11]. Some recent methods belong to this group are adaptive PCA [12], adaptive GS (GSA) [13], segmented GSA (SGSA) [14], adaptive IHS (AIHS) [15], non-linear IHS (NIHS) [16], and regularized IHS [17]. CS-based methods are popular because they are of high spatial quality, easy and fast to implement.…”

Section: Introductionmentioning

confidence: 99%

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A Variational Approach for Fusion of Panchromatic and Multispectral Images Using a New Spatial–Spectral Consistency Term

Khateri

Shabanzade

Mirzapour

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this paper, we propose a variational approach for fusion of two co-registered high-resolution panchromatic (HRP) and low-resolution multi-spectral (LRM) images to reach the high-resolution multi-spectral (HRM) one, i.e. pan-sharpening. In this fusion technique, there is a trade-off between structural information of HRP image and spectral information of LRM one. To reconstruct the HRM image, which benefits from the best characteristics of both images, we consider several fidelity terms. The structural fidelity term is used to transfer structural information of HRP image to HRM one, and spectral fidelity term is utilized to preserve spectral consistency between HRM and LRM images throughout the fusion process. To reduce the spectral distortion occurred due to the discrepancy between intensity values of HRP and LRM images, a novel spatial-spectral fidelity term is designed to keep the intensity ratio between multispectral and panchromatic pixels in the high-resolution space as the same as the low-resolution space. Moreover, the total variation (TV) regularization term is employed as a prior to promote the sparseness of gradient in HRM bands. These fidelity terms were formulated in a convex optimization problem. However, the structural and TV terms made this optimization problem nondifferentiable. Therefore, we developed an efficient majorizationminimization (MM) algorithm for solving the optimization problem. The proposed method applied to three datasets, acquired by WorldView-3, Deimos-2, and QuickBird satellites. To assess the effectiveness of the proposed method, visual analysis, as well as quantitative comparison to various pan-sharpening methods, were carried out. The experimental results suggested that the proposed method outperformed the competitors visually and quantitatively.

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Section: G Solving Objective Function Via MM Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Variational Approach for Fusion of Panchromatic and Multispectral Images Using a New Spatial–Spectral Consistency Term

Khateri

Shabanzade

Mirzapour

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In [18] spatial information regularization is carried out to combat local dissimilarities by optimizing a convex energy function. Spectral consistency based variational optimization model based on half quadratic optimization [19] presented in [20] aims to reduce spectral distortion during pansharpening. This model shows good spectral preservation while also preserving spatial smoothness of HR-PAN.…”

Section: Introductionmentioning

confidence: 99%

Learning deep multiresolution representations for pansharpening

Adeel¹,

Ali²,

Riaz³

et al. 2021

Preprint

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Retaining spatial characteristics of panchromatic image and spectral information of multispectral bands is a critical issue in pansharpening. This paper proposes a pyramid based deep fusion framework that preserves spectral and spatial characteristics at different scales. The spectral information is preserved by passing the corresponding low resolution multispectral image as residual component of the network at each scale. The spatial information is preserved by training the network at each scale with the high frequencies of panchromatic image alongside the corresponding low resolution multispectral image. The parameters of different networks are shared across the pyramid in order to add spatial details consistently across scales. The parameters are also shared across fusion layers within a network at a specific scale. Experiments suggest that the proposed architecture outperforms state of the art pansharpening models. The proposed model, code and dataset is publically available at github 1 .

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