Spatial resolution enhancement of hyperspectral image based on the combination of spectral mixing model and observation model

In this paper, a wavelet-based Bayesian fusion framework is presented, in which a low spatial resolution hyperspectral (HS) image is fused with a high spatial resolution multispectral (MS) image. Particularly, a multivariate model, Gaussian Scale Mixture (GSM) model, is employed, which is believed to be capable of modeling the distribution of wavelet coefficients more accurately. A practical implementation scheme is also presented for feasible calculations. The proposed approach is validated by simulation experiments for HS and MS image fusion. The experimental results of the proposed approach are also compared with its counterpart employing a Gaussian model for performance evaluation.

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Spatial Resolution Enhancement Mapping of Hyperspectral Image via Pixel Filling Algorithm

Journal of the Indian Society of Remote Sensing ◽

10.1007/s12524-019-01088-1 ◽

2019 ◽

Vol 48 (3) ◽

pp. 385-395

Author(s):

N. Prabhu ◽

Manoj K. Arora ◽

R. Balasubramanian

Keyword(s):

Spatial Resolution ◽

Hyperspectral Image ◽

Resolution Enhancement

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Joint Spatial-spectral Resolution Enhancement of Multispectral Images with Spectral Matrix Factorization and Spatial Sparsity Constraints

Remote Sensing ◽

10.3390/rs12060993 ◽

2020 ◽

Vol 12 (6) ◽

pp. 993 ◽

Cited By ~ 1

Author(s):

Chen Yi ◽

Yong-qiang Zhao ◽

Jonathan Cheung-Wai Chan ◽

Seong G. Kong

Keyword(s):

Spatial Resolution ◽

Spectral Resolution ◽

High Spatial Resolution ◽

Resolution Enhancement ◽

Multispectral Image ◽

Observation Model ◽

Multispectral Images ◽

Spectral Matrix ◽

Spectral Bands ◽

Landsat 7

This paper presents a joint spatial-spectral resolution enhancement technique to improve the resolution of multispectral images in the spatial and spectral domain simultaneously. Reconstructed hyperspectral images (HSIs) from an input multispectral image represent the same scene in higher spatial resolution, with more spectral bands of narrower wavelength width than the input multispectral image. Many existing improvement techniques focus on spatial- or spectral-resolution enhancement, which may cause spectral distortions and spatial inconsistency. The proposed scheme introduces virtual intermediate variables to formulate a spectral observation model and a spatial observation model. The models alternately solve spectral dictionary and abundances to reconstruct desired high-resolution HSIs. An initial spectral dictionary is trained from prior HSIs captured in different landscapes. A spatial dictionary trained from a panchromatic image and its sparse coefficients provide high spatial-resolution information. The sparse coefficients are used as constraints to obtain high spatial-resolution abundances. Experiments performed on simulated datasets from AVIRIS/Landsat 7 and a real Hyperion/ALI dataset demonstrate that the proposed method outperforms the state-of-the-art spatial- and spectral-resolution enhancement methods. The proposed method also worked well for combination of exiting spatial- and spectral-resolution enhancement methods.

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