A Convex Formulation for Hyperspectral Image Superresolution via Subspace-Based Regularization

Simões, Miguel; Bioucas-Dias, José M.; Almeida, Luís B.; Chanussot, Jocelyn

doi:10.1109/tgrs.2014.2375320

Cited by 643 publications

(564 citation statements)

References 55 publications

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“…Akhtar et al [10] learn a non-negative spectral basis from the HSI, and then solve for the MSI coefficients under a sparsity constraint, again using OMP. Simões et al [11] also recover a linear basis, and include a total variation regularizer to achieve spatial smoothness of the mixing coefficients. Like the previously mentioned works, they proceed sequentially and first construct a basis, which is then held fixed to solve for the coefficients in a second step.…”

Section: Related Workmentioning

confidence: 99%

“…The formulation of Wei et al [20] leads to an alternating optimization of fusion and unmixing, with Sylvester equation solvers; whereas we use efficient proximal mappings to impose the constraints. Moreover, to account for the influence of the constraints on the spectral basis, we update the endmembers together with the abundances, whereas [8][9][10][11]21] estimate the spectral basis in advance and then keep it fixed. Finally, while several other methods include some sort of smoothness prior, e.g., vector total variation in [11], or the L 2 -distance to the bicubic upsampling in [17], we are not aware of any prior work that uses the MSI to obtain an adaptive regularizer.…”

Section: Related Workmentioning

confidence: 99%

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Hyperspectral Super-Resolution with Spectral Unmixing Constraints

2017

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Hyperspectral sensors capture a portion of the visible and near-infrared spectrum with many narrow spectral bands. This makes it possible to better discriminate objects based on their reflectance spectra and to derive more detailed object properties. For technical reasons, the high spectral resolution comes at the cost of lower spatial resolution. To mitigate that problem, one may combine such images with conventional multispectral images of higher spatial, but lower spectral resolution. The process of fusing the two types of imagery into a product with both high spatial and spectral resolution is called hyperspectral super-resolution. We propose a method that performs hyperspectral super-resolution by jointly unmixing the two input images into pure reflectance spectra of the observed materials, along with the associated mixing coefficients. Joint super-resolution and unmixing is solved by a coupled matrix factorization, taking into account several useful physical constraints. The formulation also includes adaptive spatial regularization to exploit local geometric information from the multispectral image. Moreover, we estimate the relative spatial and spectral responses of the two sensors from the data. That information is required for the super-resolution, but often at most approximately known for real-world images. In experiments with five public datasets, we show that the proposed approach delivers up to 15% improved hyperspectral super-resolution.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Hyperspectral Super-Resolution with Spectral Unmixing Constraints

2017

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“…The amount of image information is bound to be changed which is fused before and after. According to the principle of Shannon information theory, an image's information entropy is shown by - (20) Where, is the probability of grey level i, L is image's total gray level and the dynamic range of analyzed image is [0, ]. If the value of entropy becomes higher after fusing, it indicates that the information increases and the fusion performances are improved [6] …”

Section: Entropy (H)mentioning

confidence: 99%

Image Fusion in Remote Sensing Applications: A Review

Pandit¹,

Bhiwani²

2015

IJCA

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Major technical constraints like minimum data storage at satellite platform in space, less bandwidth for communication with earth station, etc. limits the satellite sensors from capturing images with high spatial and high spectral resolutions simultaneously. To overcome this limitation, image fusion has proved to be a potential tool in remote sensing applications which integrates the information from combinations of panchromatic, multispectral or hyperspectral images; intended to result in a composite image having both higher spatial and higher spectral resolutions. The research in this area cites date back to last few decades, but the diverse approaches proposed so far by different researchers have been rarely discussed at one place. This paper is an honest attempt to collectively discuss all possible algorithms along with quality metrics following two assessment procedures i.e. at full and reduced scale resolutions to evaluate performance of these algorithms. General TermsRemote sensing, Image fusion, Satellite image processing.

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“…Several HSI-MSI fusion algorithms have been proposed in the last decades [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. HR HSI can be reconstructed by combining endmember of LR HSI and an abundance of HR MSI.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral and Multispectral Image Fusion via Deep Two-Branches Convolutional Neural Network

2018

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Abstract:Enhancing the spatial resolution of hyperspectral image (HSI) is of significance for applications. Fusing HSI with a high resolution (HR) multispectral image (MSI) is an important technology for HSI enhancement. Inspired by the success of deep learning in image enhancement, in this paper, we propose a HSI-MSI fusion method by designing a deep convolutional neural network (CNN) with two branches which are devoted to features of HSI and MSI. In order to exploit spectral correlation and fuse the MSI, we extract the features from the spectrum of each pixel in low resolution HSI, and its corresponding spatial neighborhood in MSI, with the two CNN branches. The extracted features are then concatenated and fed to fully connected (FC) layers, where the information of HSI and MSI could be fully fused. The output of the FC layers is the spectrum of the expected HR HSI. In the experiment, we evaluate the proposed method on Airborne Visible Infrared Imaging Spectrometer (AVIRIS), and Environmental Mapping and Analysis Program (EnMAP) data. We also apply it to real Hyperion-Sentinel data fusion. The results on the simulated and the real data demonstrate that the proposed method is competitive with other state-of-the-art fusion methods.

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A Convex Formulation for Hyperspectral Image Superresolution via Subspace-Based Regularization

Cited by 643 publications

References 55 publications

Hyperspectral Super-Resolution with Spectral Unmixing Constraints

Hyperspectral Super-Resolution with Spectral Unmixing Constraints

Image Fusion in Remote Sensing Applications: A Review

Hyperspectral and Multispectral Image Fusion via Deep Two-Branches Convolutional Neural Network

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