Fusion of Panchromatic and Multispectral Images Using Multiscale Convolution Sparse Decomposition

Zhang, Kai; Zhang, Feng; Feng, Zhixi; Sun, Jiande; Wu, Quanyuan

doi:10.1109/jstars.2020.3043521

Cited by 11 publications

(5 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Classical image fusion methods fall into two main categories: multi-resolution analysis (MRA) and the component substitution method (CS) [1,3,5]. CS-based methods replace the components of a multispectral image with those of a PAN image, such as principal component analysis (PCA) [6,7], Brovey transform [8,9], and Gram-Schmidt transform (GS) [10,11]. These methods typically improve spatial quality at the expense of spectral quality.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Fusion of Panchromatic and Multispectral Images Based on Adaptive Iterative Filtering

Zhang,

Xu,

Wang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

This paper proposes an efficient and high-fidelity image fusion method based on adaptive smoothing filtering for panchromatic (PAN) and multispectral (MS) image fusion. The scale ratio reflects the ratio of spatial resolution between the panchromatic image and the multispectral image. When facing a multiscale fusion task, traditional methods are unable to simultaneously handle the problems of spectral resolution loss resulting from high scale ratios and the issue of reduced spatial resolution due to low scale ratios. To adapt to the fusion of panchromatic and multispectral satellite images of different scales, this paper improves the problem of the insufficient filtering of high-frequency information of remote sensing images of different scales by the classic filter-based intensity modulation (SFIM) model. It uses Gaussian convolution kernels instead of traditional mean convolution kernels and builds a Gaussian pyramid to adaptively construct convolution kernels of different scales to filter out high-frequency information of high-resolution images. It can adaptively process panchromatic multispectral images of different scales, iteratively filter the spatial information in panchromatic images, and ensure that the scale transformation is consistent with the definition of multispectral images. Using 15 common fusion methods, this paper compares the experimental results of ZY-3 with scale ratio 2.7 and SV-1 with scale ratio 4 data. The results show that the method proposed in this paper retains good spatial information for image fusion at different scales and has good spectral preservation.

show abstract

Section: Introductionmentioning

confidence: 99%

Multiscale Fusion of Panchromatic and Multispectral Images Based on Adaptive Iterative Filtering

Zhang,

Xu,

Wang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…For these methods, it is important to select a proper transform as it significantly affects the quality of the obtained HR MS image. The commonly used transforms are intensity-hue-saturation (IHS) [6]- [7], Gram-Schmidt (GS) transform [8]- [9], principal component analysis (PCA) [10]- [11], and band-dependent spatial detail (BDSD) [12]- [13]. The primary advantage of CS-based methods is the simplicity of implementation.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Spectral Dual Back-Projection Network for Pansharpening

Zhang

Wang

Zhang

et al. 2023

IEEE Trans. Geosci. Remote Sensing

Self Cite

View full text Add to dashboard Cite

Deep unfolding networks have obtained satisfactory performance in the pansharpening task owing to their sufficient interpretability. Inspired by the back-projection (BP) mechanism, we propose a BP-driven model, spatial-spectral dual back-project network (S 2 DBPN), to fuse the low spatial resolution multispectral (LR MS) and the high spatial resolution panchromatic (PAN) images by exploiting the BP in spatial and spectral domains. Specifically, the proposed S 2 DBPN is made up of a spatial BP network, a spectral BP network, and a reconstruction network. In the spatial BP network, spatial down-and up-projection modules are derived from BP, which is responsible for the projection of the LR MS image into the spatial domain. By analogy with the spatial BP, we reformulate the degradation between high spatial resolution multispectral (HR MS) and PAN images as spectral down-and up-projections. Then, the spectral BP network is constructed for the projection of the PAN image along the channel dimension. Finally, the features from spatial and spectral BP networks are integrated to produce the desired HR MS image through the reconstruction network. Compared to the state-of-the-art methods, extensive experiments on QuickBird, GeoEye-1, and WorldView-2 datasets demonstrate that our S 2 DBPN produces better HR MS images in terms of qualitative and quantitative evaluation metrics. The code of S 2 DBPN is released at: https://github.com/RSMagneto/S2DBPN.

show abstract

“…Classical pansharpening methods can be roughly grouped into two categories: based on component substitution (CS) and multiresolution analysis (MRA) [1], [3], [7]. On the one hand, CS-based methods substitute the components of a highspectral-resolution image by those of a PAN image, such as methods based on principal component analysis (PCA) [9]- [11], Brovey transform (BT) [12], [13], and Gram-Schmidt (GS) transform [14], [15], and these methods transform a low-spatial-resolution image into another domain and then replace the main component by the spatial details from the PAN image. On the other hand, MRA-based approaches, such as approaches based on decimated wavelet transform [16], smoothing-filter-based intensity modulation (SFIM) [17], high-pass filtering (HPF) [18], morphological filtering [19], [20], and Laplacian pyramid [21], [22], involve multiresolution decomposition to extract the spatial details from a PAN image [7].…”

Section: Introductionmentioning

confidence: 99%

Smoothing Filter-Based Panchromatic Spectral Decomposition for Multispectral and Hyperspectral Image Pansharpening

Xie

Zhang

et al. 2022

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

View full text Add to dashboard Cite

This paper proposes an efficient and high-fidelity panchromatic (PAN) spectral decomposition method based on smoothing filtering for multispectral (MS) and hyperspectral (HS) image pansharpening. The proposed method assumes that the high-frequency spatial details at the same scale are the same for different spectral images captured by the satellite at the same time. When the PAN image is pre-filtered and downsampled to the MS scale, it will have the same high-frequency spatial detail as the MS image, with only low-frequency spectral differences. Pre-filtering for anti-aliasing when downsampling. Then the spectral decomposition coefficients from down-sampled PAN image to MS image can be calculated on MS scale. The lowfrequency spectral information of an image at different scales is the same, the spectral decomposition coefficients on the MS scale can be up-sampled to the PAN scale, and the original PAN image can be spectrally decomposed to obtain the sharpened image. The proposed method only decomposes the low-frequency spectrum, and the high-frequency spatial details are the same as the original PAN image, the spatial detail is well preserved. This paper verifies the effect of the proposed method on MS and HS image sharpening through experiments, and the results show that the proposed method is better than the comparison method. This paper also controls other variables to compare with the HPM method. The results show that the hybrid quality with no reference (HQN R) and spatial distortion index (Ds) of the proposed method are better than the HPM method.

show abstract

Fusion of Panchromatic and Multispectral Images Using Multiscale Convolution Sparse Decomposition

Cited by 11 publications

References 58 publications

Multiscale Fusion of Panchromatic and Multispectral Images Based on Adaptive Iterative Filtering

Multiscale Fusion of Panchromatic and Multispectral Images Based on Adaptive Iterative Filtering

Spatial-Spectral Dual Back-Projection Network for Pansharpening

Smoothing Filter-Based Panchromatic Spectral Decomposition for Multispectral and Hyperspectral Image Pansharpening

Contact Info

Product

Resources

About