Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability

Borsoi, Ricardo Augusto; Imbiriba, Tales; Bermudez, J.C.M.

doi:10.1109/tip.2019.2928895

Cited by 93 publications

(72 citation statements)

References 43 publications

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“…Many fusion methods have been proposed for this purpose. The methods most commonly used for image fusion are multi-scale transforms [4] [44] and representation learning based methods [19] [3].…”

Section: Introductionmentioning

confidence: 99%

MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion

Kittler

2020

IEEE Trans. on Image Process.

364

149

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Image decomposition is crucial for many image processing tasks, as it allows to extract salient features from source images. A good image decomposition method could lead to a better performance, especially in image fusion tasks. We propose a multi-level image decomposition method based on latent lowrank representation(LatLRR), which is called MDLatLRR. This decomposition method is applicable to many image processing fields. In this paper, we focus on the image fusion task. We build a novel image fusion framework based on MDLatLRR which is used to decompose source images into detail parts(salient features) and base parts. A nuclear-norm based fusion strategy is used to fuse the detail parts and the base parts are fused by an averaging strategy. Compared with other state-of-the-art fusion methods, the proposed algorithm exhibits better fusion performance in both subjective and objective evaluation.

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Section: Introductionmentioning

confidence: 99%

MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion

Kittler

2020

IEEE Trans. on Image Process.

364

149

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“…Though the simplicity of the LMM leads to fast and reliable unmixing strategies in some situations, it turns out to be simplistic to explain the mixing process in many practical applications. Hence, several approaches have been proposed in the literature to account for nonlinear mixing effects [4]- [6] and endmember variability [7]- [9] often present in practical scenes.…”

Section: Introductionmentioning

confidence: 99%

Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variability

Imbiriba

Borsoi

Bermudez

2020

IEEE Trans. Geosci. Remote Sensing

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Traditional hyperspectral unmixing methods neglect the underlying variability of spectral signatures often observed in typical hyperspectral images (HI), propagating these missmodeling errors throughout the whole unmixing process. Attempts to model material spectra as members of sets or as random variables tend to lead to severely ill-posed unmixing problems. Although parametric models have been proposed to overcome this drawback by handling endmember variability through generalizations of the mixing model, the success of these techniques depend on employing appropriate regularization strategies. Moreover, the existing approaches fail to adequately explore the natural multidimensinal representation of HIs. Recently, tensor-based strategies considered low-rank decompositions of hyperspectral images as an alternative to impose low-dimensional structures on the solutions of standard and multitemporal unmixing problems . These strategies, however, present two main drawbacks: 1) they confine the solutions to low-rank tensors, which often cannot represent the complexity of real-world scenarios; and 2) they lack guarantees that endmembers and abundances will be correctly factorized in their respective tensors. In this work, we propose a more flexible approach, called ULTRA-V, that imposes low-rank structures through regularizations whose strictness is controlled by scalar parameters. Simulations attest the superior accuracy of the method when compared with state-of-the-art unmixing algorithms that account for spectral variability.

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“…Parametric models are raising considerable interest since they lead to good unmixing results and avoid the main drawbacks of the other groups of SU methods that address EM variability, namely the dependence on a priori knowledge of libraries of material spectra or the need for strong assumptions on the statistical distribution of the EMs for mathematical tractability [13], [14]. Recently proposed parametric models attempt to capture spectral variability by extending the LMM using either additive [10] or multiplicative [11], [12], [15], [16] scaling factors, or by considering tensor-based formulations [17], [18].…”

Section: A Em Variability and Learning-based Su Methodsmentioning

confidence: 99%

Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing

Borsoi

Imbiriba

Bermudez

2020

IEEE Trans. Comput. Imaging

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Endmember (EM) spectral variability can greatly impact the performance of standard hyperspectral image analysis algorithms. Extended parametric models have been successfully applied to account for the EM spectral variability. However, these models still lack the compromise between flexibility and low-dimensional representation that is necessary to properly explore the fact that spectral variability is often confined to a low-dimensional manifold in real scenes. In this paper we propose to learn a spectral variability model directly from the observed data, instead of imposing it a priori. This is achieved through a deep generative EM model, which is estimated using a variational autoencoder (VAE). The encoder and decoder that compose the generative model are trained using pure pixel information extracted directly from the observed image, what allows for an unsupervised formulation. The proposed EM model is applied to the solution of a spectral unmixing problem, which we cast as an alternating nonlinear least-squares problem that is solved iteratively with respect to the abundances and to the low-dimensional representations of the EMs in the latent space of the deep generative model. Simulations using both synthetic and real data indicate that the proposed strategy can outperform the competing state-of-the-art algorithms.

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Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability

Cited by 93 publications

References 43 publications

MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion

MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion

Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variability

Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing

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