Super-resolution of hyperspectral image via superpixel-based sparse representation

Fang, Leyuan; Zhuo, Hui; Li, Shutao

doi:10.1016/j.neucom.2017.08.019

Cited by 103 publications

(39 citation statements)

References 22 publications

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“…E q and E p are the residual matrices. When substituting Equation 6into Equations (7) and (8),H α and L α can be reformulated asH…”

Section: Intra-fusionmentioning

confidence: 99%

“…Dong et al [6] proposed a nonnegative structured sparse representation approach, which jointly estimates the dictionary and sparse code of the high-resolution(HR) HSI based on the input low-resolution (LR) HSI and HR panchromatic (PAN) image. By utilizing the similarities between pixels in the super-pixel, Fang et al [7] proposed a super-pixel based sparse representation method. Dian et al [8] presented a non-local sparse tensor factorization HSI SR method, which achieves a fuller exploitation of the spatial-spectral structures in the HSI.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Image Super-Resolution by Deep Spatial-Spectral Exploitation

Zhao

2019

Remote Sensing

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Limited by the existing imagery sensors, hyperspectral images are characterized by high spectral resolution but low spatial resolution. The super-resolution (SR) technique aiming at enhancing the spatial resolution of the input image is a hot topic in computer vision. In this paper, we present a hyperspectral image (HSI) SR method based on a deep information distillation network (IDN) and an intra-fusion operation. Specifically, bands are firstly selected by a certain distance and super-resolved by an IDN. The IDN employs distillation blocks to gradually extract abundant and efficient features for reconstructing the selected bands. Second, the unselected bands are obtained via spectral correlation, yielding a coarse high-resolution (HR) HSI. Finally, the spectral-interpolated coarse HR HSI is intra-fused with the input HSI to achieve a finer HR HSI, making further use of the spatial-spectral information these unselected bands convey. Different from most existing fusion-based HSI SR methods, the proposed intra-fusion operation does not require any auxiliary co-registered image as the input, which makes this method more practical. Moreover, contrary to most single-based HSI SR methods whose performance decreases significantly as the image quality gets worse, the proposal deeply utilizes the spatial-spectral information and the mapping knowledge provided by the IDN, which achieves more robust performance. Experimental data and comparative analysis have demonstrated the effectiveness of this method.

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“…E q and E p are the residual matrices. When substituting Equation 6into Equations (7) and (8),H α and L α can be reformulated asH…”

Section: Intra-fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution by Deep Spatial-Spectral Exploitation

Zhao

2019

Remote Sensing

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“…In [25], the nonnegativity and sparsity constraints were introduced in a constrained sparse representation for HSI super-resolution. Fang et al [26] proposed a superpixel-based sparse representation model to fuse the observed HSI and MSI. Yi et al [27] presented a regularization model by exploiting the spatial and spectral correlations to achieve HSI-MSI fusion.…”

Section: Related Workmentioning

confidence: 99%

Domain Transfer Learning for Hyperspectral Image Super-Resolution

Zhang

You

2019

Remote Sensing

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A Hyperspectral Image (HSI) contains a great number of spectral bands for each pixel; however, the spatial resolution of HSI is low. Hyperspectral image super-resolution is effective to enhance the spatial resolution while preserving the high-spectral-resolution by software techniques. Recently, the existing methods have been presented to fuse HSI and Multispectral Images (MSI) by assuming that the MSI of the same scene is required with the observed HSI, which limits the super-resolution reconstruction quality. In this paper, a new framework based on domain transfer learning for HSI super-resolution is proposed to enhance the spatial resolution of HSI by learning the knowledge from the general purpose optical images (natural scene images) and exploiting the cross-correlation between the observed low-resolution HSI and high-resolution MSI. First, the relationship between low- and high-resolution images is learned by a single convolutional super-resolution network and then is transferred to HSI by the idea of transfer learning. Second, the obtained Pre-high-resolution HSI (pre-HSI), the observed low-resolution HSI, and high-resolution MSI are simultaneously considered to estimate the endmember matrix and the abundance code for learning the spectral characteristic. Experimental results on ground-based and remote sensing datasets demonstrate that the proposed method achieves comparable performance and outperforms the existing HSI super-resolution methods.

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“…Spatial information has been proven to be useful for improving the classification performance of HSI [38,39]. Recently, many DR methods have been proposed based on the spatial distribution of hyperspectral data and they can be divided into two main types: spatial filtering methods and spatial DR methods [40][41][42]. Spatial filtering methods focus on how to utilize spatial homogeneous regions to smooth the pixel-wise classification map, while spatial DR methods incorporate spatial information into the process of DR by modeling the spatial neighboring correlations [43,44].…”

Section: Introductionmentioning

confidence: 99%

Spatial-Spectral Multiple Manifold Discriminant Analysis for Dimensionality Reduction of Hyperspectral Imagery

et al. 2019

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Hyperspectral images (HSI) possess abundant spectral bands and rich spatial information, which can be utilized to discriminate different types of land cover. However, the high dimensional characteristics of spatial-spectral information commonly cause the Hughes phenomena. Traditional feature learning methods can reduce the dimensionality of HSI data and preserve the useful intrinsic information but they ignore the multi-manifold structure in hyperspectral image. In this paper, a novel dimensionality reduction (DR) method called spatial-spectral multiple manifold discriminant analysis (SSMMDA) was proposed for HSI classification. At first, several subsets are obtained from HSI data according to the prior label information. Then, a spectral-domain intramanifold graph is constructed for each submanifold to preserve the local neighborhood structure, a spatial-domain intramanifold scatter matrix and a spatial-domain intermanifold scatter matrix are constructed for each sub-manifold to characterize the within-manifold compactness and the between-manifold separability, respectively. Finally, a spatial-spectral combined objective function is designed for each submanifold to obtain an optimal projection and the discriminative features on different submanifolds are fused to improve the classification performance of HSI data. SSMMDA can explore spatial-spectral combined information and reveal the intrinsic multi-manifold structure in HSI. Experiments on three public HSI data sets demonstrate that the proposed SSMMDA method can achieve better classification accuracies in comparison with many state-of-the-art methods.

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Super-resolution of hyperspectral image via superpixel-based sparse representation

Cited by 103 publications

References 22 publications

Hyperspectral Image Super-Resolution by Deep Spatial-Spectral Exploitation

Hyperspectral Image Super-Resolution by Deep Spatial-Spectral Exploitation

Domain Transfer Learning for Hyperspectral Image Super-Resolution

Spatial-Spectral Multiple Manifold Discriminant Analysis for Dimensionality Reduction of Hyperspectral Imagery

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