Pixel level image fusion refers to the processing and synergistic combination of information gathered by various imaging sources to provide a better understanding of a scene. We formulate the image fusion as an optimization problem and propose an inf

Deep dictionary learning seeks multiple dictionaries at different image scales to capture complementary coherent characteristics. We propose a method for learning a hierarchy of synthesis dictionaries with an image classification goal. The dictionaries and classification parameters are trained by a classification objective, and the sparse features are extracted by reducing a reconstruction loss in each layer. The reconstruction objectives in some sense regularize the classification problem and inject source signal information in the extracted features. The performance of the proposed hierarchical method increases by adding more layers, which consequently makes this model easier to tune and adapt. The proposed algorithm furthermore, shows remarkably lower fooling rate in presence of adversarial perturbation. The validation of the proposed approach is based on its classification performance using four benchmark datasets and is compared to a CNN of similar size.

Data heterogeneity can pose a great challenge to process and systematically fuse low-level data from different modalities with no recourse to heuristics and manual adjustments and refinements. In this paper, a new methodology is introduced for the fusion of measured data for detecting and predicting weather-driven natural hazards. The proposed research introduces a robust theoretical and algorithmic framework for the fusion of heterogeneous data in near real time. We establish a flexible information-based fusion framework with a target optimality criterion of choice, which for illustration, is specialized to a maximum entropy principle and a least effort principle for semisupervised learning with noisy labels. We develop a methodology to account for multimodality data and a solution for addressing inherent sensor limitations. In our case study of interest, namely, that of flood density estimation, we further show that by fusing remote sensing and social media data, we can develop well founded and actionable flood maps. This capability is valuable in situations where environmental hazards, such as hurricanes or severe weather, affect very large areas. Relative to the state of the art working with such data, our proposed information-theoretic solution is principled and systematic, while offering a joint exploitation of any set of heterogeneous sensor modalities with minimally assuming priors. This flexibility is coupled with the ability to quantitatively and clearly state the fusion principles with very reasonable computational costs. The proposed method is tested and substantiated with the multimodality data of a 2013 Boulder Colorado flood event.

Real-world social and/or operational networks consist of agents with associated states, whose connectivity forms complex topologies. This complexity is further compounded by interconnected information layers, consisting, for instance, documents/resources of the agents which mutually share topical similarities. Our goal in this paper is to predict the specific states of the agents, as their observed resources evolve in time and get updated. The information diffusion among the agents and the publications themselves effectively result in a dynamic process which we capture by an interconnected system of networks (i.e., layered). More specifically, we use a notion of a supra-Laplacian matrix to address such a generalized diffusion of an interconnected network starting with the classical “graph Laplacian.” The auxiliary and external input update is modeled by a multidimensional Brownian process, yielding two contributions to the variations in the states of the agents: one that is due to the intrinsic interactions in the network system, and the other due to the external inputs or innovations. A variation on this theme, a priori knowledge of a fraction of the agents’ states is shown to lead to a Kalman predictor problem. This helps us refine the predicted states exploiting the estimation error in the agents’ states. Three real-world datasets are used to evaluate and validate the information diffusion process in this novel-layered network approach. Our results demonstrate a lower prediction error when using the interconnected network rather than the single connectivity layer between the agents. The prediction error is further improved by using the estimated diffusion connection and by applying the Kalman approach with partial observations.