Investigation of a multiphysics joint inversion framework

<p>We present a 3-D multiphysics joint inversion framework that in a certain sense is a trade-off between “simultaneous” and “cooperative” approaches to data integration. Using a variable splitting approach, inverse problems are solved by individual inverse solvers, on individual grids, while coupling combined with interpolation is implemented separately. Up to date, first-arrival seismic tomography, gravity and magnetotelluric inverse problems are included in this framework. Magnetotelluric inversion uses the NLCG method implemented in the ModEM code (Kelbert et al., 2014). Seismic tomography is based on the Gauss-Newton method with a finite-difference eikonal solver and posterior ray tracing. Gravity inversion uses the conjugate gradient method with wavelet compression of the sensitivity matrix. Structure coupling is based on mixed-norm regularization inducing joint sparsity between the models. Among particular functionals that were studied, following numerical experiments, joint total variation and joint minimum support have proved to<span> </span>be the most efficient options. In the numerical experiments, we invert synthetics simulating regional datasets observed in Ireland.</p>

Multi-frame super resolution via deep plug-and-play CNN regularization

Because of the ill-posedness of multi-frame super resolution (MSR), the regularization method plays an important role in the MSR field. Various regularization terms have been proposed to constrain the image to be estimated. However, artifacts also exist in the estimated image due to the artificial tendency in the manually designed prior model. To solve this problem, we propose a novel regularization-based MSR method with learned prior knowledge. By using the variable splitting technique, the fidelity term and regularization term are separated. The fidelity term is associated with an “{L^{2}}-{L^{2}}” form sub-problem. Meanwhile, the sub-problem respect to regularization term is a denoising problem, which can be solved by denoisers learned from a deep convolutional neural network. Different from the traditional regularization methods which employ hand-crafted image priors, in this paper the image prior model is replaced by learned prior implicitly. The two sub-problems are solved alternately and iteratively. The proposed method cannot only handle complex degradation model, but also use the learned prior knowledge to guide the reconstruction process to avoid the artifacts. Both the quantitative and qualitative results demonstrate that the proposed method gains better quality than the state-of-the-art methods.

ASSESSING AND COMPARING THE PERFORMANCE OF ENDMEMBER EXTRACTION METHODS IN MULTIPLE CHANGE DETECTION USING HYPERSPECTRAL DATA

Endmember extraction is a process to identify the hidden pure source signals from the mixture. Endmember finding has become increasingly important in hyperspectral data exploitation because endmembers can be used to specify unknown particular spectral classes. This paper evaluates the change detection problem in bi-temporal hyperspectral remote sensing images using the unmixing process. A complete spectral unmixing process contains estimating the number of endmembers, endmember extraction and abundance estimation. Endmember extraction is a vital step in spectral unmixing of hyperspectral images. Hyperspectral change detection by unmixing has the potential to provide subpixel information from hyperspectral images. In this study, four methods including Simplex Identification via variable Splitting and Augmented Lagrangian (SISAL), N-finder algorithm (N-FINDR), Vertex Component Analysis (VCA), and Fast algorithm for linearly Unmixing (FUN) are used to produce multiple change detection maps. This paper explores and compares the performance of these methods in multiple change detection. The empirical results reveal the superiority of the FUN method in providing multiple change map with an overall accuracy of 87% and a kappa coefficient of 0.70.