Hyperspectral Image Super-Resolution Based on Spatial Correlation-Regularized Unmixing Convolutional Neural Network

Lu, Xiaochen; Yang, Dezheng; Zhang, Junping; Jia, Fengde

doi:10.3390/rs13204074

Cited by 12 publications

(4 citation statements)

References 53 publications

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“…[36]- [193] Fig. 2: Pavia University cube with spectral signature of the pixel at position (11,9) sample of Pavia University dataset is shown in Figure 2.…”

Section: Dcnnmentioning

confidence: 99%

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A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

Aburaed

Alkhatib

Marshall

et al. 2023

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

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Remote sensing technology has undeniable importance in various industrial applications, such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding, and optical gas imaging, to name a few. Remote sensing technology has been continuously evolving, offering a range of image modalities that can facilitate the aforementioned applications. One such modality is Hyperspectral Imaging (HSI). Unlike Multispectral Images (MSI) and natural images, HSI consist of hundreds of bands. Despite their high spectral resolution, HSI suffer from low spatial resolution in comparison to their MSI counterpart, which hinders the utilization of their full potential. Therefore, spatial enhancement, or Super Resolution (SR), of HSI is a classical problem that has been gaining rapid attention over the past two decades. The literature is rich with various SR algorithms that enhance the spatial resolution of HSI while preserving their spectral fidelity. This paper reviews and discusses the most important algorithms relevant to this area of research between 2002-2022, along with the most frequently used datasets, HSI sensors, and quality metrics. Meta-analysis are drawn based on the aforementioned information, which is used as a foundation that summarizes the state of the field in a way that bridges the past and the present, identifies the current gap in it, and recommends possible future directions.

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“…[36]- [193] Fig. 2: Pavia University cube with spectral signature of the pixel at position (11,9) sample of Pavia University dataset is shown in Figure 2.…”

Section: Dcnnmentioning

confidence: 99%

“…A similar blind approach idea is presented in [174], where Wei et al used a deep recursive residual network to fuse LR-HSI with HR-MSI. Other works in this area include [175]- [177], [177]- [193], [278]- [291].…”

Section: Deep Learning-based Fusionmentioning

confidence: 99%

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

Aburaed

Alkhatib

Marshall

et al. 2023

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

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show abstract

“…Inspired by the non-negative matrix factorization, Liu et al designed an unsupervised MIAE network for HIS SRR [39]. Admittedly, these methods need an auxiliary higher resolution co-registered imagery, which is sometimes unavailable [40]. For this reason, Wang et al proposed a novel dilated projection correction network aeDPCN using single low-resolution HIS for SRR [41].…”

Section: Introductionmentioning

confidence: 99%

ZY-1 02D Hyperspectral Imagery Super-Resolution via Endmember Matrix Constraint Unmixing

Zhang

Portelli

et al. 2022

Remote Sensing

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This paper proposes an endmember matrix constraint unmixing method for ZY-1 02D hyperspectral imagery (HSI) super-resolution reconstruction (SRR) to overcome the low resolution of ZY-1 02D HSI. The proposed method combines spectral unmixing and adds novel smoothing constraints to traditional non-negative matrix factorization to improve details and preserve the spectral information of traditional SRR methods. The full utilization of the endmember spectral matrix and endmember abundance matrix of HSI and multispectral imagery (MSI) reconstructs the high spatial resolution and high spectral fidelity HSI. Furthermore, given the ZY-1 02D HSI infrared bands are seriously corrupted by noise, the influence of denoising on the SRR accuracy is also discussed. Experiments show that the proposed method restores spatial details and spectral information and is robust for noise, preserving more spectral information. Therefore, the proposed method is a ZY-1 02D HSI SRR method with high spatial resolution and high spectral fidelity, which improves the spatial resolution while simultaneously solving spectral mixing and provides the possibility for the data further expansion.

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“…Recently, an increasing number of HSI-MSI fusion algorithms have been proposed [32][33][34]. These algorithms have been proved to be effective with good fusion performance.…”

Section: Introductionmentioning

confidence: 99%

A Spatial-Enhanced LSE-SFIM Algorithm for Hyperspectral and Multispectral Images Fusion

et al. 2021

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The fusion of a hyperspectral image (HSI) and multispectral image (MSI) can significantly improve the ability of ground target recognition and identification. The quality of spatial information and the fidelity of spectral information are normally contradictory. However, these two properties are non-negligible indicators for multi-source remote-sensing images fusion. The smoothing filter-based intensity modulation (SFIM) method is a simple yet effective model for image fusion, which can improve the spatial texture details of the image well, and maintain the spectral characteristics of the image significantly. However, traditional SFIM has a poor effect for edge information sharpening, leading to a bad overall fusion result. In order to obtain better spatial information, a spatial filter-based improved LSE-SFIM algorithm is proposed in this paper. Firstly, the least square estimation (LSE) algorithm is combined with SFIM, which can effectively improve the spatial information quality of the fused image. At the same time, in order to better maintain the spatial information, four spatial filters (mean, median, nearest and bilinear) are used for the simulated MSI image to extract fine spatial information. Six quality indexes are used to compare the performance of different algorithms, and the experimental results demonstrate that the LSE-SFIM based on bilinear (LES-SFIM-B) performs significantly better than the traditional SFIM algorithm and other spatially enhanced LSE-SFIM algorithms proposed in this paper. Furthermore, LSE-SFIM-B could also obtain similar performance compared with three state-of-the-art HSI-MSI fusion algorithms (CNMF, HySure, and FUSE), while the computing time is much shorter.

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Hyperspectral Image Super-Resolution Based on Spatial Correlation-Regularized Unmixing Convolutional Neural Network

Cited by 12 publications

References 53 publications

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

ZY-1 02D Hyperspectral Imagery Super-Resolution via Endmember Matrix Constraint Unmixing

A Spatial-Enhanced LSE-SFIM Algorithm for Hyperspectral and Multispectral Images Fusion

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