Successful performance of radiological search mission is dependent on effective utilization of mixture of signals. Examples of modalities include, e.g., EO imagery and gamma radiation data, or radiation data collected during multiple events. In addition, elevation data or spatial proximity can be used to enhance the performance of acquisition systems. State of the art techniques in processing and exploitation of complex information manifolds rely on diffusion operators. Our approach involves machine learning techniques based on analysis of joint data- dependent graphs and their associated diffusion kernels. Then, the significant eigenvectors of the derived fused graph Laplace and Schroedinger operators form the new representation, which provides integrated features from the heterogeneous input data. The families of data-dependent Laplace and Schroedinger operators on joint data graphs, shall be integrated by means of appropriately designed fusion metrics. These fused representations are used for target and anomaly detection.
As new imaging modalities arise, the problem of inpainting becomes increasing important. Typical techniques for inpainting are completely determined by the penalization term used in the optimization scheme. These methods range from minimizing over total variation to finding a sparsest solution in a given basis to minimizing the Ginzburg-Landau energy. In this paper, we propose a novel approach to inpainting of remote sensing images, which uses previous measurements taken from heterogeneous image soures in conjunction with these well studied penalization methods. These previous measurements could be images with different illumination or weather conditions, images with spatio-temporal changes, or even all together different imaging modalities. Our approach utilizes manifold learning techniques such as diffusion maps or Laplacian eigenmaps that are applied to each image. This is followed by learning a rotation between the two feature spaces in an effort to place data points from both images in a common feature space. Then, we apply a novel preimage algorithm to the fused data in conjunction with an inpainting penalization method to recreate the missing pixels.
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