Pansharpening With Multiscale Geometric Support Tensor Machine

Xing, Yinghui; Wang, Min; Yang, Shuyuan; Zhang, Kai

doi:10.1109/tgrs.2017.2742002

Cited by 25 publications

(8 citation statements)

References 39 publications

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“…In [16], Choi et al also employed curvelet transform [17] to better represent the edges in the fused images because it is effective to improve the spatial resolution by enhancing the edges. Subsequently, some methods based on MRA [18]- [20] are also developed by estimating more accurate gain coefficients to achieve better fusion results. These methods have a better performance in spectral preservation but some spatial details from PAN images are excessively injected into LR MS images.…”

Section: Multispectral (Hr Ms) Imagesmentioning

confidence: 99%

Exploiting Low-Rank and Sparse Properties in Strided Convolution Matrix for Pansharpening

Zhang

et al. 2021

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

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Fusion of low spatial resolution multispectral (LR MS) and panchromatic (PAN) images to acquire high spatial resolution multispectral (HR MS) images has attracted increasing attention in recent years. In this paper, we first utilize the form of convolution matrix (CM) to formulate the image fusion problem. In order to reduce the complexity of CM, the step size is introduced and strided convolution matrix (SCM) is constructed. Then, we explore the low-rank property in SCM and impose the prior on the spatial and spectral degradation model of LR MS and PAN images. Meanwhile, sparsity in SCM is considered to further enhance the local structures in the fused image. Finally, the proposed model is optimized efficiently by the alternative direction method of multipliers (ADMM). By exploiting the low-rank and sparse priors in SCM of HR MS image, the local and global structures can be better preserved. The experimental results on the reduced-resolution and full-resolution datasets also show that the proposed method behaves well in qualitative and quantitative assessments.

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Section: Multispectral (Hr Ms) Imagesmentioning

confidence: 99%

Exploiting Low-Rank and Sparse Properties in Strided Convolution Matrix for Pansharpening

Zhang

et al. 2021

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

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“…For example, Shah et al [15] utilized nonsubsampled contourlet (NSCT) to enhance the spatial details in the LR MS image. Following the decomposition framework, some MRA-like filters [17,18] were constructed to infer more reasonable spatial information. The fused images of MRAbased methods exhibit better preservation in terms of the spectral information because only spatial details are injected into the up-sampled LR MS image.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Spatial–Spectral Interaction Transformer for Pan-Sharpening

Zhang

Sun

2022

Remote Sensing

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Pan-sharpening methods based on deep neural network (DNN) have produced state-of-the-art fusion performance. However, DNN-based methods mainly focus on the modeling of the local properties in low spatial resolution multispectral (LR MS) and panchromatic (PAN) images by convolution neural networks. The global dependencies in the images are ignored. To capture the local and global properties of the images concurrently, we propose a multiscale spatial–spectral interaction transformer (MSIT) for pan-sharpening. Specifically, we construct the multiscale sub-networks containing convolution–transformer encoder to extract the local and global features at different scales from LR MS and PAN images, respectively. Then, a spatial–spectral interaction attention module (SIAM) is designed to merge the features at each scale. In SIAM, the interaction attention is used to decouple the spatial and spectral information efficiently for the enhancement of complementarity and the reduction of redundancy in the extracted features. The features from different scales are further integrated into a multiscale reconstruction module (MRM) to generate the desired high spatial resolution multispectral image, in which the spatial and spectral information is fused scale by scale. The experiments on reduced- and full-scale datasets demonstrate that the proposed MSIT can produce better results in terms of visual and numerical analysis when compared with state-of-the-art methods.

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“…(1) Component substitution-based (CS) methods [2][3][4] (2) Multiscale analysis-based (MRA) methods [5,6] (3) Degradation model-based (DM) methods [7][8][9][10][11][12][13][14][15][16][17] (4) Deep neural network-based (DNN) methods [18][19][20][21] CS methods include principal component analysis (PCA) and intensity-hue-saturation (IHS). CS methods behave well in computational e ciency.…”

Section: Introductionmentioning

confidence: 99%

Pansharpening with the Multidirection Tree Ridgelet Dictionary

Gai-ning

et al. 2022

Mathematical Problems in Engineering

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In this work, we propose a novel pansharpening method based on the multidirection tree ridgelet dictionary. A pansharpened image has a wide-ranging application area, such as object detection, image segmentation, feature extraction, and so on. Remote sensing (RS) imagery contains more abundant information on surface features. In order to represent different object information, we use three main classes of different dictionaries, which can reveal the latent structure of RS image. First, RS imagery is divided into several blocks. Each block is classified as smooth, irregular, or multidirection categories. Different categories are sparsely represented in different dictionaries. Second, the smooth blocks are sparsely represented in the discrete cosine transform (DCT) dictionary. The irregular and the multidirection blocks are sparsely represented in the KSVD and multidirection tree ridgelet (MDTR) dictionary, respectively. Finally, we can obtain the fusion image by reconstructing those blocks. Some experiments are taken on three different datasets acquired by QuickBird, GeoEye, and IKONOS satellites. Experimental results show that the proposed method can reduce spectral distortion and enhance spatial information. Meanwhile, numerical guidelines outperform some related methods.

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Pansharpening With Multiscale Geometric Support Tensor Machine

Cited by 25 publications

References 39 publications

Exploiting Low-Rank and Sparse Properties in Strided Convolution Matrix for Pansharpening

Exploiting Low-Rank and Sparse Properties in Strided Convolution Matrix for Pansharpening

Multiscale Spatial–Spectral Interaction Transformer for Pan-Sharpening

Pansharpening with the Multidirection Tree Ridgelet Dictionary

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