Adaptive Nonnegative Sparse Representation for Hyperspectral Image Super-Resolution

Li, Xuesong; Zhang, Youqiang; Ge, Zixian; Cao, Guo; Shi, Hao; Fu, Peng

doi:10.1109/jstars.2021.3072044

Cited by 20 publications

(7 citation statements)

References 62 publications

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“…However, since HSI fusion is an ill-posed problem, to reduce the number of possible solutions, some priors are proposed to enhance the robustness of the fusion methods. For example, low-rank regularizers are utilized in unmixing-based methods [21], [26], [27] to constrain the number of final spectra; dictionary-learning-based methods learn a spectral dictionary from the low-resolution HSI, map the spectral dictionary to RGB dictionary using known SSFs, and apply sparse regularizers to obtain the HR HSIs [19], [28], [29], [57]; Bayesian-based method [18] assumes that the representation coefficients of high-resolution HSI follow a Gaussian distribution, and use the assumption as a prior to accomplish high-resolution HSI fusion; tensor-based methods [16], [20], [22] exploit the redundancy of HSI, group non-local similar cube patches to aggregate tensors, and apply sparse representation to construct the tensors. Especially, Liu et al [15] recast the tensor-trace-norm formulation to reconstruct HR HSIs via low-rank approximation; Dian et al [23] use low tensor-train rank (LTTR) as a regularization term on the grouped tensors consisting of non-local patches.…”

Section: Related Workmentioning

confidence: 99%

BUSIFusion: Blind Unsupervised Single Image Fusion of Hyperspectral and RGB Images

Wang

et al. 2023

IEEE Trans. Comput. Imaging

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Hyperspectral images (HSIs) provide rich spectral information that has been widely used in numerous computer vision tasks. However, their low spatial resolution often prevents their use in applications such as image segmentation and recognition. Fusing low-resolution HSIs with high-resolution RGB images to reconstruct high-resolution HSIs has attracted great research attention recently. In this paper, we propose an unsupervised blind fusion network that operates on a single HSI and RGB image pair and requires neither known degradation models nor any training data. Our method takes full advantage of an unrolling network and coordinate encoding to provide a state-ofthe-art HSI reconstruction. It can also estimate the degradation parameters relatively accurately through the neural representation and implicit regularization of the degradation model. The experimental results demonstrate the effectiveness of our method both in simulations and in our real experiments. The proposed method outperforms other state-of-the-art nonblind and blind fusion methods on two popular HSI datasets. Our related code and data is available at https://github.com/CPREgroup/Real-Spec-RGB-Fusion.

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Section: Related Workmentioning

confidence: 99%

BUSIFusion: Blind Unsupervised Single Image Fusion of Hyperspectral and RGB Images

Wang

et al. 2023

IEEE Trans. Comput. Imaging

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“…Fu et al [19] introduced bidirectional structure in single HSI super-resolution to exploit spatial-spectral correlation of HSI and global correlation along spectra. Li [20] fused MSI and HSI by utilizing a novel adaptive nonnegative sparse representationbased model.…”

Section: Related Workmentioning

confidence: 99%

Adversarial Spectral Super-Resolution for Multispectral Imagery Using Spatial Spectral Feature Attention Module

Liu

Han

Chen

2023

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

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Acquiring high-quality hyperspectral imagery with high spatial and spectral resolution plays an important role in remote sensing. Due to the limited capacity of sensors, providing high spatial and spectral resolution is still a challenging issue. Spectral super-resolution (SSR) increases the spectral dimensionality of multispectral images to achieve resolution enhancement. In this article, we propose a spectral resolution enhancement method based on the generative adversarial network framework without introducing additional spectral responses prior. In order to adaptively rescale informative features for capturing interdependencies in the spectral and spatial dimensions, a spatial spectral feature attention module is introduced. The proposed method jointly exploits spatio-spectral distribution in the hyperspectral manifold to increase spectral resolution while maintaining spatial content consistency. Experiments are conducted on both synthetic Landsat 8 and Sentinel-2 radiance data and real coregistered advanced land image and Hyperion (MS and HS) images, which indicates the superiority of the proposed method compared to other state-of-the-art methods.

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“…These methods effectively reduce data redundancy and preserve crucial information through dimensionality reduction and feature extraction. A novel adaptive non-negative sparse representation model is proposed for fusion in work [26]. Using a non-negative structured sparse representation model.…”

Section: Related Workmentioning

confidence: 99%

Correlation Matrix-Based Fusion of Hyperspectral and Multispectral Images

Lin

Peng

et al. 2023

Remote Sensing

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The fusion of the hyperspectral image (HSI) and the multispectral image (MSI) is commonly employed to obtain a high spatial resolution hyperspectral image (HR-HSI); however, existing methods often involve complex feature extraction and optimization steps, resulting in time-consuming fusion processes. Additionally, these methods typically require parameter adjustments for different datasets. Still, reliable references for parameter adjustment are often unavailable in practical scenarios, leading to subpar fusion results compared to simulated scenarios. To address these challenges, this paper proposes a fusion method based on a correlation matrix. Firstly, we assume the existence of a correlation matrix that effectively correlates the spectral and spatial information of HSI and MSI, enabling fast fusion. Subsequently, we derive a correlation matrix that satisfies the given assumption by deducing the generative relationship among HR-HSI, HSI, and MSI. Finally, we optimize the fused result using the Sylvester equation. We tested our proposed method on two simulated datasets and one real dataset. Experimental results demonstrate that our method outperforms existing state-of-the-art methods. Particularly, in terms of fusion time, our method achieves fusion in less than 0.1 seconds in some cases. This method provides a practical and feasible solution for the fusion of hyperspectral and multispectral images, overcoming the challenges of complex fusion processes and parameter adjustment while ensuring a quick fusion process.

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Adaptive Nonnegative Sparse Representation for Hyperspectral Image Super-Resolution

Cited by 20 publications

References 62 publications

BUSIFusion: Blind Unsupervised Single Image Fusion of Hyperspectral and RGB Images

BUSIFusion: Blind Unsupervised Single Image Fusion of Hyperspectral and RGB Images

Adversarial Spectral Super-Resolution for Multispectral Imagery Using Spatial Spectral Feature Attention Module

Correlation Matrix-Based Fusion of Hyperspectral and Multispectral Images

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