A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability

IEEE Trans. on Image Process.

et al. 2020

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Introducing spatial prior information in hyperspectral imaging (HSI) analysis has led to an overall improvement of the performance of many HSI methods applied for denoising, classification, and unmixing. Extending such methodologies to nonlinear settings is not always straightforward, specially for unmixing problems where the consideration of spatial relationships between neighboring pixels might comprise intricate interactions between their fractional abundances and nonlinear contributions. In this paper, we consider a multiscale regularization strategy for nonlinear spectral unmixing with kernels. The proposed methodology splits the unmixing problem into two sub-problems at two different spatial scales: a coarse scale containing lowdimensional structures, and the original fine scale. The coarse spatial domain is defined using superpixels that result from a multiscale transformation. Spectral unmixing is then formulated as the solution of quadratically constrained optimization problems, which are solved efficiently by exploring a reformulation of their dual cost functions in the form of root-finding problems. Furthermore, we employ a theory-based statistical framework to devise a consistent strategy to estimate all required parameters, including both the regularization parameters of the algorithm and the number of superpixels of the transformation, resulting in a truly blind (from the parameters setting perspective) unmixing method. Experimental results attest the superior performance of the proposed method when comparing with other, state-of-theart, related strategies.

Section: B Experiments With Real Datasupporting

confidence: 84%

Section: A Multiscale Nonlinear Mixing Modelmentioning

confidence: 99%

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A Blind Multiscale Spatial Regularization Framework for Kernel-Based Spectral Unmixing

IEEE Trans. on Image Process.

et al. 2020

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“…Other approaches pursue different ways to improve the conditioning of the inverse problem, employing for instance multiscale regularization on the abundance maps [27], or using additional information in the form of spectral libraries known a priori [28], [29] or extracted from the observed HI [30].…”

Section: A Extended Linear Mixing Modelsmentioning

confidence: 99%

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

IEEE Trans. Geosci. Remote Sensing

2020

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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.

“…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

IEEE Trans. Comput. Imaging

2020

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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.