Fusion of aerial gamma-ray survey and remote sensing data for a deeper understanding of radionuclide fate after radiological incidents: examples from the Fukushima Dai-Ichi response

Czaja, Wojciech; Manning, Benjamin; McLean, Lance; Murphy, James M.

doi:10.1007/s10967-015-4650-z

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…Sensor fusion is a gigantic field. One line of work focuses on the extraction, analysis and comparison of the components expressed by the different sensors for the purpose of gaining understanding of the underlying scene [16,48,65]. However, due to the complex nature of such data, finding informative representations and metrics of these components by combining the information from the different sensors is challenging.…”

Section: Manifold Learning For Sensor Fusionmentioning

confidence: 99%

Spatiotemporal Analysis Using Riemannian Composition of Diffusion Operators

Shnitzer¹,

Wu²,

Talmon³

2022

Preprint

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Multivariate time-series have become abundant in recent years, as many dataacquisition systems record information through multiple sensors simultaneously. In this paper, we assume the variables pertain to some geometry and present an operatorbased approach for spatiotemporal analysis. Our approach combines three components that are often considered separately: (i) manifold learning for building operators representing the geometry of the variables, (ii) Riemannian geometry of symmetric positivedefinite matrices for multiscale composition of operators corresponding to different time samples, and (iii) spectral analysis of the composite operators for extracting different dynamic modes. We propose a method that is analogous to the classical wavelet analysis, which we term Riemannian multi-resolution analysis (RMRA). We provide some theoretical results on the spectral analysis of the composite operators, and we demonstrate the proposed method on simulations and on real data.

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Section: Manifold Learning For Sensor Fusionmentioning

confidence: 99%

Spatiotemporal Analysis Using Riemannian Composition of Diffusion Operators

Shnitzer¹,

Wu²,

Talmon³

2022

Preprint

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“…For data sampled from a common manifold, there is an asymptotic relationship as n → ∞ between the scale parameter σ used for constructing the underlying graph and the time parameter t [16]. Diffusion distances have been applied to a range of problems including molecular dynamics [52,65], learning of dynamical systems [47,15,55], latent variable estimation [30,28,54], remote sensing image processing [20,43,45,44], and medical signal processing [31,62,34,2].…”

Section: Notation Meaningmentioning

confidence: 99%

Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks

Cowen¹,

Devkota²,

Hu³

et al. 2020

Preprint

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Data-dependent metrics are powerful tools for learning the underlying structure of high-dimensional data. This article develops and analyzes a data-dependent metric known as diffusion state distance (DSD), which compares points using a data-driven diffusion process. Unlike related diffusion methods, DSDs incorporate information across time scales, which allows for the intrinsic data structure to be inferred in a parameterfree manner. This article develops a theory for DSD based on the multitemporal emergence of mesoscopic equilibria in the underlying diffusion process. New algorithms for denoising and dimension reduction with DSD are also proposed and analyzed. These approaches are based on a weighted spectral decomposition of the underlying diffusion process, and experiments on synthetic datasets and real biological networks illustrate the efficacy of the proposed algorithms in terms of both speed and accuracy. Throughout, comparisons with related methods are made, in order to illustrate the distinct advantages of DSD for datasets exhibiting multiscale structure. Introduction.Metrics for pairwise comparisons of data points X = {x i } n i=1 ⊂ R D are an essential tool in a wide range of data analysis tasks including classification, regression, clustering, and visualization [24]. Euclidean distances, and more generally data-independent metrics that depend only on the original coordinates of the data, may be inadequate for high-dimensional data due to the curse of dimensionality [60] or for low-dimensional data with nonlinear correlations. In order to address these concerns, metrics derived from the global structure of the data are necessary.A family of data-dependent metrics based on local similarity graphs has been developed to address this problem [29,58,53,3,4,21,17,16]. These methods typically construct an undirected, weighted graph G with nodes corresponding to X and weights between nodes x i , x j given by W ij = K(x i , x j ) for a suitable kernel function that is usually radial and rapidly decaying. Though constituted from local relationships among the points of X (due to the rapid decay of K), the global features of X may be gleaned from G by considering partial differential operators on G (e.g. Laplacian or Schrödinger operators), geodesics in the path space, or diffusion processes on X. These new metrics may vastly improve over data-independent metrics for a range of tasks including supervised classification and regression, unsupervised clustering, low-dimensional embeddings, and the visualization of high-dimensional data.While powerful, these methods may not adequately capture multiscale structure in the data. For example, when there are both small and large clusters in the underlying data, or geometric features at multiple scales, it may be challenging to select without supervision the optimal low-dimensional representation or parameters for these data-dependent metrics. In particular, when considering diffusion processes on graphs, the time scale crucially determines the granularity of the subsequent...

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“…The LE (Belkin & Niyogi, 2002) method, which has been previously applied to radiological data (Benedetto et al, 2014;Christie et al, 2014;Czaja, Manning, McLean, & Murphy, 2016), serves as benchmark for source detection. LE is a manifold learning technique that provides a geometrically faithful lower-dimensional representation of a data set.…”

Section: Le Source Detectionmentioning

confidence: 99%

Experiments in unmanned aerial vehicle/unmanned ground vehicle radiation search

Peterson

Cèsar-Tondreau

et al. 2019

Journal of Field Robotics

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This paper discusses the results of a field experiment conducted at Savannah River National Laboratory to test the performance of several algorithms for the localization of radioactive materials. In this multirobot system, both an unmanned aerial vehicle, a custom hexacopter, and an unmanned ground vehicle (UGV), the ClearPath Jackal, equipped with γ‐ray spectrometers, were used to collect data from two radioactive source configurations. Both the Fourier scattering transform and the Laplacian eigenmap algorithms for source detection were tested on the collected data sets. These algorithms transform raw spectral measurements into alternate spaces to allow clustering to detect trends within the data which indicate the presence of radioactive sources. This study also presents a point source model and accompanying information‐theoretic active exploration algorithm. Field testing validated the ability of this model to fuse aerial and ground collected radiation measurements, and the exploration algorithm’s ability to select informative actions to reduce model uncertainty, allowing the UGV to locate radioactive material online.

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Fusion of aerial gamma-ray survey and remote sensing data for a deeper understanding of radionuclide fate after radiological incidents: examples from the Fukushima Dai-Ichi response

Cited by 11 publications

References 10 publications

Spatiotemporal Analysis Using Riemannian Composition of Diffusion Operators

Spatiotemporal Analysis Using Riemannian Composition of Diffusion Operators

Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks

Experiments in unmanned aerial vehicle/unmanned ground vehicle radiation search

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