A Robust Pansharpening Algorithm Based on Convolutional Sparse Coding for Spatial Enhancement

Gogineni, Rajesh; Chaturvedi, Ashvini

doi:10.1109/jstars.2019.2945815

Cited by 19 publications

(3 citation statements)

References 44 publications

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“…Where 𝐽(𝑢) is indicates the cartoon image prior that favours the structural information and 𝑇(𝑣) indicates the texture image priors that favours the oscillations and 𝛾 is the balancing parameter for maintaining the trade-off between image components and should be adapted to the amount of edges and texture [27] 𝐽 Representing the Total variation prior and 𝑇 used to define the texture prior with the use of Hilbert space [47].…”

Section: Tv-hilbert Model-based Image Decompositionmentioning

confidence: 99%

“…[24] proposed hybrid approach for fusion with NSCT for band separation and SR for low pass sub band fusion. [25] created dictionary from PAN image in a' torus wavelet domain .In Pansharpening process crucial point is construction of appropriate dictionary .In dictionary construction image is divided in overlapped patches and due to partition and process strategy smoothing and ringing artifacts are visible in fused image which causes misregistration problem .Limitation of sparse representation method is overcome with convolutional sparse coding [26] in which dictionary is constructed from whole image instead of image patches .In [27] proposed convolutional sparse coding based pansharpening on separated texture and cartoon component .…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing Pansharpening with TV-H−1 Decomposition and PSO-Based Adaptive Weighting Method

Sangani¹,

Thakker

Panchal

et al. 2023

Int. J. Image Grap.

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In remote sensing, owing to existing sensors’ limitations and the tradeoff between signal-to-noise ratio (SNR) and instantaneous field of view (IFOV), it is difficult to obtain a single image with good spectral and spatial resolution. Pansharpening (PS) is the technique for sharpening multispectral (MS) images by extracting structural and edge information of panchromatic (PAN) image. Multiscale decomposition methods are used for decomposing image in sub-bands but are affected by ringing artifacts, therefore the resultant image seems to be blurred and misregistered. The proposed method overcomes this drawback by decomposing PAN and four band MS image into cartoon and texture components with total variation (TV) Hilbert[Formula: see text] model. The particle swarm optimization (PSO) algorithm is used for finding the optimum weight for fusing texture and cartoon details of PAN and MS images. The proposed method is practically validated on both full-scale and reduced-scale. Robustness of our proposed approach is tested on different geographical areas such as hilly, urban, and vegetation areas. From the visual analysis and qualitative parameters, the proposed method is proved effective compared with other traditional approaches.

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Section: Tv-hilbert Model-based Image Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remote Sensing Pansharpening with TV-H−1 Decomposition and PSO-Based Adaptive Weighting Method

Sangani¹,

Thakker

Panchal

et al. 2023

Int. J. Image Grap.

View full text Add to dashboard Cite

show abstract

“…( 7), and is referred to as CBPDN (convolutional basis pursuit denoising). Many methods have been developed in the literature to address the CBPDN problem, with the alternate direction method of multipliers (ADMM) framework proving to be the most efficient [23,24]. The CSR model was established to look at shift-invariant sparse representation, which is still a valuable feature in image fusion.…”

Section: Convolutional Sparse Representationmentioning

confidence: 99%

A Competent Convolutional Sparse Representation Model for Pan-Sharpening of Multi-Spectral Images

Gogineni¹,

Darisi²

2022

JOAASR

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Two types of images are produced by Earth observation satellites, each having complementing spatial andspectral characteristics. Pan-sharpening (PS) is based on remote sensing and image fusion approach thatproduces a high spatial resolution multi-spectral image by merging spectral information from a low spatialresolution multispectral (MS) image with intrinsic spatial details from a high spatial resolution panchromatic(PAN) image. Traditional pan-sharpening methods continue to seek for a fused image that contains thenecessary spatial and spectral information. This work proposes a pan-sharpening method based on a recentinvention, convolutional sparse representation (CSR). Geometric structural characteristics are extracted fromthe PAN image using a CSR-based filtering procedure. The challenge of learning filters, convolutional basispursuit denoising (CBPDN), is handled using a modified dictionary learning method based on the concept ofAlternating Direction Method of Multipliers (ADMM). The retrieved details are put into MS bands usingapplicable weighting coefficients. Because the proposed fusion model avoids the standard patch-basedmethod, spatial and structural features are preserved while spectral quality is maintained. The spectraldistortion index SAM and the spatial measure ERGAS improve by 4.4 and 6.2 percent, respectively, whencompared to SR-based techniques. The computational complexity is reduced by 200 seconds when compared to the most recent SR-based fusion technique. The proposed method's efficacy is demonstrated by reduced-scale and full-scale experimental findings utilising the QuickBird and GeoEye-1 datasets.

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Increasing pre-monsoon rain days over four stations of Kerala, India

et al. 2022

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A Robust Pansharpening Algorithm Based on Convolutional Sparse Coding for Spatial Enhancement

Cited by 19 publications

References 44 publications

Remote Sensing Pansharpening with TV-H−1 Decomposition and PSO-Based Adaptive Weighting Method

Remote Sensing Pansharpening with TV-H−1 Decomposition and PSO-Based Adaptive Weighting Method

A Competent Convolutional Sparse Representation Model for Pan-Sharpening of Multi-Spectral Images

Increasing pre-monsoon rain days over four stations of Kerala, India

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