Hyperspectral Image Super-Resolution by Spectral Mixture Analysis and Spatial–Spectral Group Sparsity

Li, Jie; Yuan, Qiangqiang; Shen, Huanfeng; Meng, Xiangchao; Zhang, Liangpei

doi:10.1109/lgrs.2016.2579661

Cited by 85 publications

(44 citation statements)

References 20 publications

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“…To improve the quality of the acquired HSI due to limited spatial resolution, super-resolution is an important enhancement technique [4][5][6][7][8][9][10][11][12]. In order to evaluate the reconstructed high resolution HSI, conventional strategy is to degrade the original data into a coarser resolution by down-sampling.…”

Section: Introductionmentioning

confidence: 99%

No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning

et al. 2017

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Abstract:Assessing the quality of a reconstructed hyperspectral image (HSI) is of significance for restoration and super-resolution. Current image quality assessment methods such as peak signal-noise-ratio require the availability of pristine reference image, which is often not available in reality. In this paper, we propose a no-reference hyperspectral image quality assessment method based on quality-sensitive features extraction. Difference of statistical properties between pristine and distorted HSIs is analyzed in both spectral and spatial domains, then multiple statistics features that are sensitive to image quality are extracted. By combining all these statistics features, we learn a multivariate Gaussian (MVG) model as benchmark from the pristine hyperspectral datasets. In order to assess the quality of a reconstructed HSI, we partition it into different local blocks and fit a MVG model on each block. A modified Bhattacharyya distance between the MVG model of each reconstructed HSI block and the benchmark MVG model is computed to measure the quality. The final quality score is obtained by average pooling over all the blocks. We assess five state-of-the-art super-resolution methods on Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Hyperspec-VNIR-C (HyperspecVC) data using our proposed method. It is verified that the proposed quality score is consistent with current reference-based assessment indices, which demonstrates the effectiveness and potential of the proposed no-reference image quality assessment method.

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Section: Introductionmentioning

confidence: 99%

No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning

et al. 2017

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“…After removing seriously polluted bands and cropping images for each data set, the HSI cube used for the experiments are of 256 × 256 × 146, 256 × 256 × 140 and 256 × 256 × 140, respectively. To thoroughly evaluate the performance of the proposed approach, we considered three popular super-resolution methods for comparison, that is, the nonlocal autoregressive model (NARM) proposed by [21], the spatial-spectral group sparsity method (SSGS) proposed by [19], the low-rank and total variation regulariztions (LRTV) method proposed by [23]. We also considered the nearest neighbor interpolation (NN) method that is used to achieve the upsampled HSI for comparison.…”

Section: Experimental Studymentioning

confidence: 99%

“…In this set of experiments, the blurring kernel is chosen as the popular Gaussian kernel and all the LR HSIs are obtained by downsampling the original HR HSIs with a factor of 2 or 3, i.e., the LR HSIs are of spatial size 128 × 128 or 85 × 85. In the following experiments, similar to [19], the gray values of each band of HSI were normalized to [0, 255] to facilitate the numerical calculation, though this operation may change the relative spectral properties of the HSI bands. In addition, the parameters a and α i in (2) and (3) were fixed to 5 and 1 4 , respectively.…”

Section: Experimental Studymentioning

confidence: 99%

“…Their approach can also deal with the situation that the system blurring is unknown. In a recent study, Li et al [19] explored sparse properties in both spectral and spatial domains for HSI super-resolution. Recently, sparse representation and compressed sensing have successfully been used in various real image super-resolution applications.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

et al. 2017

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Hyperspectral image (HSI) possesses three intrinsic characteristics: the global correlation across spectral domain, the nonlocal self-similarity across spatial domain, and the local smooth structure across both spatial and spectral domains. This paper proposes a novel tensor based approach to handle the problem of HSI spatial super-resolution by modeling such three underlying characteristics. Specifically, a noncovex tensor penalty is used to exploit the former two intrinsic characteristics hidden in several 4D tensors formed by nonlocal similar patches within the 3D HSI. In addition, the local smoothness in both spatial and spectral modes of the HSI cube is characterized by a 3D total variation (TV) term. Then, we develop an effective algorithm for solving the resulting optimization by using the local linear approximation (LLA) strategy and the alternative direction method of multipliers (ADMM). A series of experiments are carried out to illustrate the superiority of the proposed approach over some state-of-the-art approaches.

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“…Therefore, in order to alleviate spectral distortion by extending existing CNN based SR algorithms to HSIs, effectively utilizing both spatial context and spectral discrimination is of crucial importance. Such integration of spatial context and spectral discrimination has been demonstrated to be of great superiority in many hyperspectral applications, e.g., noise removal [48,49], classification [50,51], and SR [52]. In CNN based hyperspectral applications, Makantasis et al integrated spatial-spectral information into CNN using a randomized principal component analysis (RPCA) [53].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network

et al. 2017

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Hyperspectral images are well-known for their fine spectral resolution to discriminate different materials. However, their spatial resolution is relatively low due to the trade-off in imaging sensor technologies, resulting in limitations in their applications. Inspired by recent achievements in convolutional neural network (CNN) based super-resolution (SR) for natural images, a novel three-dimensional full CNN (3D-FCNN) is constructed for spatial SR of hyperspectral images in this paper. Specifically, 3D convolution is used to exploit both the spatial context of neighboring pixels and spectral correlation of neighboring bands, such that spectral distortion when directly applying traditional CNN based SR algorithms to hyperspectral images in band-wise manners is alleviated. Furthermore, a sensor-specific mode is designed for the proposed 3D-FCNN such that none of the samples from the target scene are required for training. Fine-tuning by a small number of training samples from the target scene can further improve the performance of such a sensor-specific method. Extensive experimental results on four benchmark datasets from two well-known hyperspectral sensors, namely hyperspectral digital imagery collection experiment (HYDICE) and reflective optics system imaging spectrometer (ROSIS) sensors, demonstrate that our proposed 3D-FCNN outperforms several existing SR methods by ensuring higher quality both in reconstruction and spectral fidelity.

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Hyperspectral Image Super-Resolution by Spectral Mixture Analysis and Spatial–Spectral Group Sparsity

Cited by 85 publications

References 20 publications

No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning

No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning

Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network

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