The fusion of panchromatic and multispectral remote sensing images via tensor-based sparse modeling and hyper-Laplacian prior

Deng, Liang; Feng, Minyu; Tai, Xue–Cheng

doi:10.1016/j.inffus.2018.11.014

Cited by 104 publications

(35 citation statements)

References 52 publications

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“…For instance, Chen et al [27] proposed the dynamic gradient sparse prior to enhance the edge information in the fusion results. Deng et al [28] further adopted the hyper-Laplacian prior to capture the differences between LR MS images and the fused images in gradient domain. For degradation model based methods, the spatial and spectral information is preserved well in the fuse 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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“…Recently published PAN-Sharpening approaches (Fu et al, 2019;Deng et al, 2019) evaluate the algorithms only on images with low resolution. By conducting several experiments we observed that processing high-resolution imagery with these methods is infeasible because of their high computational requirements.…”

Section: Pan-sharpeningmentioning

confidence: 99%

3d Surface Reconstruction From Multi-Date Satellite Images

Bullinger

Bodensteiner

Arens

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The reconstruction of accurate three-dimensional environment models is one of the most fundamental goals in the field of photogrammetry. Since satellite images provide suitable properties for obtaining large-scale environment reconstructions, there exist a variety of Stereo Matching based methods to reconstruct point clouds for satellite image pairs. Recently, a Structure from Motion (SfM) based approach has been proposed, which allows to reconstruct point clouds from multiple satellite images. In this work, we propose an extension of this SfM based pipeline that allows us to reconstruct not only point clouds but watertight meshes including texture information. We provide a detailed description of several steps that are mandatory to exploit state-of-the-art mesh reconstruction algorithms in the context of satellite imagery. This includes a decomposition of finite projective camera calibration matrices, a skew correction of corresponding depth maps and input images as well as the recovery of real-world depth maps from reparameterized depth values. The paper presents an extensive quantitative evaluation on multi-date satellite images demonstrating that the proposed pipeline combined with current meshing algorithms outperforms state-of-the-art point cloud reconstruction algorithms in terms of completeness and median error. We make the source code of our pipeline publicly available.

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“…We now solve the proposed model in (12) under the ADMM framework. We note that ADMM has been widely adopted in many areas such as machine learning and image processing, see, e.g., [49]- [53] and references therein. To begin, we partition X with s blocks as in (6).…”

Section: B Jsbunsal-tv Algorithmmentioning

confidence: 99%

Joint-Sparse-Blocks Regression for Total Variation Regularized Hyperspectral Unmixing

Huang

Zhao

et al. 2019

IEEE Access

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Sparse unmixing has attracted much attention in recent years. It aims at estimating the fractional abundances of pure spectral signatures in mixed pixels in hyperspectral images. To exploit spatial-contextual information present in the scene, the total variation (TV) regularization is incorporated into the sparse unmixing formulation, promoting adjacent pixels having similar not only endmembers but also fractional abundances, and thus having similar structural sparsity. It is therefore hoped to impose joint sparsity, instead of classic single sparsity, on these adjacent pixels to further improve the unmixing performance. To this end, we include the joint-sparse-blocks regression into the TV spatial regularization framework and present a new unmixing algorithm, termed joint-sparse-blocks unmixing via variable splitting augmented Lagrangian and total variation (JSBUnSAL-TV). In particular, a reweighting strategy is utilized to enhance sparsity along lines within each block. Simulated and real-data experiments show the advantages of the proposed algorithm.INDEX TERMS Hyperspectral images, spectral unmixing, total variation regularization, joint-sparse-blocks regression.

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The fusion of panchromatic and multispectral remote sensing images via tensor-based sparse modeling and hyper-Laplacian prior

Cited by 104 publications

References 52 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

3d Surface Reconstruction From Multi-Date Satellite Images

Joint-Sparse-Blocks Regression for Total Variation Regularized Hyperspectral Unmixing

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