Hyperspectral Super-Resolution Via Joint Regularization of Low-Rank Tensor Decomposition

Cao, Meng; Bao, Wenxing; Qu, Kewen

doi:10.3390/rs13204116

Cited by 6 publications

(3 citation statements)

References 44 publications

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“…Since the hyperspectral remote sensing imaging area only contains several types of ground objects, the dimension of the endmember matrix and abundance matrix will be much smaller than the dimension of the original hyperspectral data matrix [45]. According to the physical meaning of the abundance vector, it should satisfy ASC and ANC.…”

Section: E S Xmentioning

confidence: 99%

Endmember Extraction and Abundance Estimation Algorithm Based on Double-Compressed Sampling

Wang

et al. 2023

Preprint

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Based on double-compressed sampling, a hyperspectral spectral unmixing algorithm is proposed, which could directly complete the endmember extraction and abundance estimation. On the basis of the linear mixed model, we designed spatial and spectral sampling matrices, obtained spatial and spectral measurement data, and constructed a joint unmixing model containing endmember and abundance information. By using operator separation and Lagrangian multiplier algorithm, the endmember matrix, abundance matrix and remixing image can be quickly obtained by matrix operation. The parameters of the unmixing algorithm, including regularization parameter, convergence threshold and spatial sampling rate, are determined using synthetic simulated hyperspectral data. The proposed algorithm is applied to six groups of real hyperspectral data, and the experimental results fully verify the effectiveness and reliability of the algorithm. Compared with the algorithm that directly unmixes the original data, it is found that the proposed algorithm can effectively extract the endmembers and estimate the abundance even when there are few measurement data. Compared with the algorithm of sampling reconstruction first and then unmixing, the proposed algorithm can improve the efficiency of unmixing. Therefore, the main innovation of the proposed algorithm is that it can complete the unmixing directly from the measured data, maintain the accuracy and improve the efficiency of the unmixing.

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Section: E S Xmentioning

confidence: 99%

Endmember Extraction and Abundance Estimation Algorithm Based on Double-Compressed Sampling

Wang

et al. 2023

Preprint

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“…Graph regularization [48] is a graph-theory-based regularization method for processing data with a graph structure, such as social networks, images, videos, speech, etc. Its main aim is to improve the performance of a model by introducing structural information from the graph into the model.…”

Section: Graph Regularizationmentioning

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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“…Hyperspectral sensors are capable of imaging multiple target areas within the electromagnetic spectrum [ 1 ]. Due to the limitations of imaging cameras, there exists a trade-off between spectral and spatial resolution, leading to hyperspectral images often having a lower spatial resolution, especially in the majority of hyperspectral data bands concentrated in the visible light range (400–1000 nm).…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of GF-5 and Sentinel-2A Fusion Methods for Lithological Classification: The Tuanjie Peak, Xinjiang Case Study

Chi,

Zhang,

Jin

et al. 2024

Sensors

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This study investigates the application of hyperspectral image space–spectral fusion technology in lithologic classification, using data from China’s GF-5 and Europe’s Sentinel-2A. The research focuses on the southern region of Tuanjie Peak in the Western Kunlun Range, comparing five space–spectral fusion methods: GSA, SFIM, CNMF, HySure, and NonRegSRNet. To comprehensively evaluate the effectiveness and applicability of these fusion methods, the study conducts a comprehensive assessment from three aspects: evaluation of fusion effects, lithologic classification experiments, and field validation. In the evaluation of fusion effects, the study uses an index analysis and comparison of spectral curves before and after fusion, concluding that the GSA fusion method performs the best. For lithologic classification, the Random Forest (RF) classification method is used, training with both area and point samples. The classification results from area sample training show significantly higher overall accuracy compared to point samples, aligning well with 1:50,000 scale geological maps. In field validation, the study employs on-site verification combined with microscopic identification and comparison of images with actual spectral fusion, finding that the classification results for the five lithologies are essentially consistent with field validation results. The “GSA+RF” method combination established in this paper, based on data from GF-5 and Sentinel-2A satellites, can provide technical support for lithological classification in similar high-altitude regions.

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Hyperspectral Super-Resolution Via Joint Regularization of Low-Rank Tensor Decomposition

Cited by 6 publications

References 44 publications

Endmember Extraction and Abundance Estimation Algorithm Based on Double-Compressed Sampling

Endmember Extraction and Abundance Estimation Algorithm Based on Double-Compressed Sampling

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

Comparative Analysis of GF-5 and Sentinel-2A Fusion Methods for Lithological Classification: The Tuanjie Peak, Xinjiang Case Study

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