Fusion of Hyperspectral and Multispectral Images With Sparse and Proximal Regularization

Yang, Feixia; Ping, Ziliang; Ma, Fanyuan; Wang, Yanwei

doi:10.1109/access.2019.2961240

Cited by 6 publications

(4 citation statements)

References 48 publications

(78 reference statements)

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“…Yang et al in 2019 [18] introduced a sparsity and proximal minimum-volume regularized CNMF method named as SPR-CNMF. The minimum-volume regularizer controls and minimizes the distance between selected endmembers and the center of mass of the selected region in the image to reduce the computational complexity.…”

Section: Traditional Methodsmentioning

confidence: 99%

Hyperspectral and Multispectral Image Fusion Using Deep Convolutional Neural Network - ResNet Fusion

Priya¹,

Rajkumar²

2023

Hyperspectral Imaging - A Perspective on Recent Advances and Applications

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In recent years, deep learning HS-MS fusion has become a very active research tool for the super resolution of hyperspectral image. The deep conventional neural networks (CNN) help to extract more detailed spectral and spatial features from the hyperspectral image. In CNN, each convolution layer takes the input from the previous layer which may cause the problems of information loss as the depth of the network increases. This loss of information causes vanishing gradient problems, particularly in the case of very high-resolution images. To overcome this problem in this work we propose a novel HS–MS ResNet fusion architecture with help of skip connection. The ResNet fusion architecture contains residual block with different stacked convolution layer, in this work we tested the residual block with two-, three-, and four- stacked convolution layers. To strengthens the gradients and for decreases negative effects from gradient vanishing, we implemented ResNet fusion architecture with different skip connections like short, long, and dense skip connection. We measure the strength and superiority of our ResNet fusion method against traditional methods by using four public datasets using standard quality measures and found that our method shows outstanding performance than all other compared methods.

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Section: Traditional Methodsmentioning

confidence: 99%

Hyperspectral and Multispectral Image Fusion Using Deep Convolutional Neural Network - ResNet Fusion

Priya¹,

Rajkumar²

2023

Hyperspectral Imaging - A Perspective on Recent Advances and Applications

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“…4) Spectral graph regularizer: As we all know, the endmember signature describes the spectral reflection of a material that varies gradually with the spectra, so it is usually a smooth curve. Similar to the endmember signature in the linear unmixing model [9], the third factor matrix also represents the spectral signature in the tensor decomposition. Thus, this paper develops a neighborhood graph to regularize the spectral factor matrix for smoothing [42] [43].…”

Section: Regularization Termsmentioning

confidence: 99%

“…Obviously, data fusion is an ill-posed inverse problem, which can be solved by the regularization method. Therefore, the regularized terms of low-rankness [8], sparsity [9], smoothness [10] and minimum-volume [11] were developed to improve the quality of reconstruction images. For example, Veganzones et al [12] proposed a low-rank super-resolution method to partition the image into sub-graphs and solve the data fusion problem of each sub-graph independently in the low-dimensional subspace by exploiting the local low-rank property of real datasets.…”

Section: Introductionmentioning

confidence: 99%

Graph-Based Logarithmic Low-Rank Tensor Decomposition for the Fusion of Remotely Sensed Images

Huo

Yang

2021

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

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Hyperspectral images with high spatial resolution play an important role in material classification, change detection, and others. However, owing to the limitation of imaging sensors, it is difficult to directly acquire images with both high spatial resolution and high spectral resolution. Therefore, the fusion of remotely-sensed images is an effective way to obtain highresolution desired data, which is usually an ill-posed inverse problem and susceptible to noise corruption. To address these issues, a low-rank model based on tensor decomposition is proposed to fuse hyperspectral and multispectral images by incorporating graph regularization, in which the logarithmic low-rank function is utilized to suppress the small components for denoising. Furthermore, this paper takes advantage of the local spatial similarity of remotely-sensed images to enhance the reconstruction performance by constructing spatial graphs, and also promotes signature smoothing between adjacent endmember spectra using the neighborhood-based spectral graph regularization. Finally, a set of efficient solvers is carefully designed via alternating optimization for closed-from solutions and computational reduction, in which vector-matrix operators are adapted to solve the three-dimensional core tensor. Experimental tests on several real datasets illustrate that the proposed fusion method yields better reconstruction performance than the current stateof-the-art methods, and can significantly suppress noise at the same time.

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“…Some researchers have made improvements to CNMF. For instance, Yang et al [36] introduced endmember vertex distance and iterative centroid proximity regularization terms, incorporating sparse and proximal regularization terms into CNMF. This approach reduces the computational complexity through proximal alternating optimization.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution Algorithm Based on Graph Regular Tensor Ring Decomposition

Sun,

Bao,

et al. 2023

Remote Sensing

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This paper introduces a novel hyperspectral image super-resolution algorithm based on graph-regularized tensor ring decomposition aimed at resolving the challenges of hyperspectral image super-resolution. This algorithm seamlessly integrates graph regularization and tensor ring decomposition, presenting an innovative fusion model that effectively leverages the spatial structure and spectral information inherent in hyperspectral images. At the core of the algorithm lies an iterative optimization process embedded within the objective function. This iterative process incrementally refines latent feature representations. It incorporates spatial smoothness constraints and graph regularization terms to enhance the quality of super-resolution reconstruction and preserve image features. Specifically, low-resolution hyperspectral images (HSIs) and high-resolution multispectral images (MSIs) are obtained through spatial and spectral downsampling, which are then treated as nodes in a constructed graph, efficiently fusing spatial and spectral information. By utilizing tensor ring decomposition, HSIs and MSIs undergo feature decomposition, and the objective function is formulated to merge reconstructed results with the original images. Through a multi-stage iterative optimization procedure, the algorithm progressively enhances latent feature representations, leading to super-resolution hyperspectral image reconstruction. The algorithm’s significant achievements are demonstrated through experiments, producing sharper, more detailed high-resolution hyperspectral images (HRIs) with an improved reconstruction quality and retained spectral information. By combining the advantages of graph regularization and tensor ring decomposition, the proposed algorithm showcases substantial potential and feasibility within the domain of hyperspectral image super-resolution.

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Fusion of Hyperspectral and Multispectral Images With Sparse and Proximal Regularization

Cited by 6 publications

References 48 publications

Hyperspectral and Multispectral Image Fusion Using Deep Convolutional Neural Network - ResNet Fusion

Hyperspectral and Multispectral Image Fusion Using Deep Convolutional Neural Network - ResNet Fusion

Graph-Based Logarithmic Low-Rank Tensor Decomposition for the Fusion of Remotely Sensed Images

Hyperspectral Image Super-Resolution Algorithm Based on Graph Regular Tensor Ring Decomposition

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