Pansharpening for Multiband Images With Adaptive Spectral–Intensity Modulation

Yang, Yong; Wu, Lei; Huang, Shuying; Tang, Yingjun; Wan, Weiguo

doi:10.1109/jstars.2018.2849011

Cited by 23 publications

(18 citation statements)

References 41 publications

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“…Most existing fusion models only consider modulation coefficient estimation for the spatial detail part, but some fusion models add coefficient modulation for the spectral part. In literatures [32,33], the spectral modulation coefficients are introduced into the pansharpening methods, and thus the spectral information of the MS image can be better preserved. Based on this, the fusion model can be expressed asM…”

Section: Model For the Pansharpening Problemmentioning

confidence: 99%

“…In fact, optical remote sensing uses optical systems to collect and record reflected and emitted radiation from ground objects into space, while MS images have multiple bands and cover a wide range of reflectivity, and the same ground object may have significantly different reflection characteristics among different bands. Therefore, spectral preservation needs to consider the interband relationships of MS images [33], that is, the relationship between the MS channels is important to improve the spectral fidelity of the fusion results. Thus, we constructed the LASM coefficient based on the interband relationships of MS images and the spatial structure relationship to modulate the preservation of spectral information.…”

Section: Lasm Coefficient Constructionmentioning

confidence: 99%

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A Segmentation-Cooperated Pansharpening Method Using Local Adaptive Spectral Modulation

Jiao

Qian

2019

Electronics

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In order to improve the spatial resolution of multispectral (MS) images and reduce spectral distortion, a segmentation-cooperated pansharpening method using local adaptive spectral modulation (LASM) is proposed in this paper. By using the k-means algorithm for the segmentation of MS images, different connected component groups can be obtained according to their spectral characteristics. For spectral information modulation of fusion images, the LASM coefficients are constructed based on details extracted from images and local spectral relationships among MS bands. Moreover, we introduce a cooperative theory for the pansharpening process. The local injection coefficient matrix and LASM coefficient matrix are estimated based on the connected component groups to optimize the fusion result, and the parameters of the segmentation algorithm are adjusted according to the feedback from the pansharpening result. In the experimental part, degraded and real data sets from GeoEye-1 and QuickBird satellites are used to assess the performance of our proposed method. Experimental results demonstrate the validity and effectiveness of our method. Generally, the method is superior to several classic and state-of-the-art pansharpening methods in both subjective visual effect and objective evaluation indices, achieving a balance between the injection of spatial details and maintenance of spectral information, while effectively reducing the spectral distortion of the fusion image.

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Section: Model For the Pansharpening Problemmentioning

confidence: 99%

Section: Lasm Coefficient Constructionmentioning

confidence: 99%

A Segmentation-Cooperated Pansharpening Method Using Local Adaptive Spectral Modulation

Jiao

Qian

2019

Electronics

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“…With the rapid development of satellite sensors, remote sensing images have been widely applied in the areas of geography, military, and scientific research. However, owing to the limitations of radiation energy and the amount of data received by existing sensors [2], most satellites are currently unable to directly collect high spatial resolution multispectral (HRMS) images [3]. Optical satellites usually carry two different sensors: a panchromatic (PAN) sensor and a multispectral (MS) sensor.…”

Section: Introductionmentioning

confidence: 99%

“…Popular MRA methods include contourlet [10], Laplacian pyramid [11], and à trous wavelet [12] transforms. These methods provide local information in the spatial and frequency domains concurrently, and achieve improved performance; however, the use of a multiscale transformation increases the computational complexity, and may cause spatial distortion [2].…”

Section: Introductionmentioning

confidence: 99%

Pansharpening Based on Joint-Guided Detail Extraction

Yang

Huang

et al. 2021

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

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Pansharpening is the process of fusing low spatial resolution multispectral (MS) images with high spatial resolution panchromatic (PAN) images, so as to obtain high spatial resolution multispectral (HRMS) images. In this study, a new pansharpening method based on a joint-guided detail extraction is proposed to maintain the spectral and spatial fidelity of a pansharpened image. First, to obtain details that are highly correlated with an MS image, a new PAN image is constructed and guided by the intensity component of the MS image through a variational model. The construction of the new PAN image improves the correlation between the PAN and MS images, and thus reduces the spectral distortion. The variational model is rapidly solved using the least-squares method. Second, to obtain accurate details from the new PAN image, the extraction of the details is guided by each band of the MS image through a regression model, which can further reduce the spatial distortion. The regression model is effectively solved using the gradient descend method. Finally, the details are injected into the up-sampled MS image to obtain a fused image. Numerous experiments on the proposed approach were conducted and the results were compared with previous state-of-the-art pansharpening methods. The experimental results verify that the proposed method can efficiently achieve high-quality HRMS images.

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“…Spatio-spectral image fusion has aroused widespread interest in academia and numerous methods have been proposed so far [9], [10], [11], [12], [13], [14], [15]. Most of them can be generally classified into four major categories [16], [17] The CS methods are the earliest and the most widely used methods due to their extremely low computational cost [17].…”

mentioning

confidence: 99%

A New Variational Approach Based on Proximal Deep Injection and Gradient Intensity Similarity for Spatio-Spectral Image Fusion

Huang

Deng

et al. 2020

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

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Pansharpening is a very debated spatio-spectral fusion problem. It refers to the fusion of a high spatial resolution panchromatic (PAN) image with a lower spatial but higher spectral resolution multispectral (LRMS) image in order to obtain an image with high resolution in both the domains. In this paper, we propose a novel variational optimizationbased (VO) approach to address this issue incorporating the outcome of a deep convolutional neural network (DCNN). This solution can take advantages of both the paradigms. On one hand, higher performance can be expected introducing machine learning methods based on the training by examples philosophy into VO approaches. On other hand, the combination of VO techniques with DCNNs can aid the generalization ability of these latter. In particular, we formulate a 2-based proximal deep injection term to evaluate the distance between the DCNN outcome and the desired high spatial resolution multispectral image. This represents the regularization term for our VO model. Furthermore, a new data fitting term measuring the spatial fidelity is proposed. Finally, the proposed convex VO problem is efficiently solved by exploiting the framework of the alternating direction method of multipliers, thus guaranteeing the convergence of the algorithm. Extensive experiments both on simulated and real datasets demonstrate that the proposed approach can outperform state-of-the-art spatio-spectral fusion methods, even showing a significant generalization ability. Please find the project page: https://liangjiandeng.github.io/Projects Res/DMPIF 2020jstars.html.

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Pansharpening for Multiband Images With Adaptive Spectral–Intensity Modulation

Cited by 23 publications

References 41 publications

A Segmentation-Cooperated Pansharpening Method Using Local Adaptive Spectral Modulation

A Segmentation-Cooperated Pansharpening Method Using Local Adaptive Spectral Modulation

Pansharpening Based on Joint-Guided Detail Extraction

A New Variational Approach Based on Proximal Deep Injection and Gradient Intensity Similarity for Spatio-Spectral Image Fusion

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