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HYPERSPECTRAL ANOMALY DETECTION ON THE SPHERE 8

HYPERSPECTRAL ANOMALY DETECTION ON THE SPHERE

引用

8th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Frontera-Pons, Joana Inst Polytech Sci Avancees DR2I F-94200 Ivry France Univ Paris Saclay Lab AIM CEA F-91191 Gif Sur Yvette France

ISBN: (纸本)9781728155494

Anomaly detectors aim at finding any pixel that is different from its surrounding normal background pixels. Most of the classical anomaly detection algorithms are based on the Mahalanobis distance, and therefore, they are mainly sensitive to the signal energy. One could project the hyperspectral datacube onto the unit hypersphere in order to enhance detection for faint targets. In this context, we introduce the class of Angular Gaussian distributions for hyperspectral data modelling. Moreover, the corresponding maximum likelihood estimates and the generalized likelihood ratio test are both investigated. The resulting anomaly detection scheme is independent on the true distribution of the data within the family of elliptical distribuions. These results are illustrated through simulations and over one real hyperspectral image.

关键词： anomaly detection hyperspectral imaging robust estimation directional distributions

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A new clustering algorithm for PolSAR images segmentation 8

A new clustering algorithm for PolSAR images segmentation

引用

8th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Roizman, Violeta Draskovic, Gordana Pascal, Frederic Cent Supelec L2S Paris France Univ Buenos Aires Buenos Aires DF Argentina Univ Paris Saclay Cent Supelec L2S Paris France

ISBN: (纸本)9781728155494

This paper deals with polarimetric synthetic aperture radar (PolSAR) image segmentation. More precisely, we present a new robust clustering algorithm designed for non-Gaussian data. The algorithm is based on an expectationmaximization approach. Its novelty is that, in addition to the estimation of each cluster center and covariance matrix, it also provides for each observation an estimation of the scale parameter, allowing a better flexibility when assigning each observation in one cluster. The method performances are evaluated on both simulated and real multi-looked PolSAR data. It is demonstrated that the algorithm outperforms the classical clustering algorithms such as k-means and GMM (Gaussian-based EM algorithm) in various scenarios.

关键词： Covariance matrix

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Spectral Shrinkage of Tyler's M-Estimator of Covariance Matrix 8

Spectral Shrinkage of Tyler's M-Estimator of Covariance Matr...

引用

8th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Breloy, Arnaud Ollila, Esa Pascal, Frederic Univ Paris Nanterre LEME EA 4416 Nanterre France Aalto Univ Dept Signal Proc & Acoust Espoo Finland Univ Paris Saclay CentraleSupelec L2S Paris France

ISBN: (纸本)9781728155494

Covariance matrices usually exhibit specific spectral structures, such as low-rank ones in the case of factor models. In order to exploit this prior knowledge in a robust estimation process, we propose a new regularized version of Tyler's M-estimator of covariance matrix. This estimator is expressed as the minimizer of a robust M-estimating cost function plus a penalty that is unitary invariant (i.e., that only applies on the eigenvalue) that shrinks the estimated spectrum toward a fixed target. The structure of the estimate is expressed through an interpretable fixed-point equation. A majorization-minimization (MM) algorithm is derived to compute this estimator, and the g-convexity of the objective is also discussed. Several simulation studies illustrate the interest of the approach and also explore a method to automatically choose the target spectrum through an auxiliary estimator.

关键词： Covariance matrix

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A COMPARATIVE STUDY OF THE PERFORMANCE OF PARAMETER ESTIMATION OF A 2-D FIELD USING LINE- And POINT-PROJECTION sensorS 8

A COMPARATIVE STUDY OF THE PERFORMANCE OF PARAMETER ESTIMATI...

引用

8th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Gat, Shani Messer, Hagit Tel Aviv Univ Sch Elect Engn Tel Aviv Israel

ISBN: (纸本)9781728155494

The signal's attenuation measured over wireless Commercial Microwave Links (CMLs) have proven to be an effective tool for opportunistic environmental sensing. By nature, a CML senses the projection along the propagation path of the 2-D field it monitors. Inspired by this application, we present a comparative theoretical study of two families of sensors: in one case the sensors are point samples of the field, and in the other case they are projections along a line of an arbitrary length and of arbitrary orientation, centered at the same locations. The study is done by Cramer-Rao Bounds, used for evaluating the performance of estimating a Gaussian-Shaped field by N sensors in given locations. This study is relevant to IoT applications, where in the first case the sensors are designated, local sensors (e.g., rain gauges) and in the second case the sensors are opportunistic, e.g., CMLs. We present appropriate algorithm-independent theoretical tools to deal with such problems, and relate the characteristics of the monitored field to the potential performance gain when using CMLs.

关键词： Opportunistic sensors Fisher Information environmental monitoring rain field estimation

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Node Copying for Protection Against Graph Neural Network Topology Attacks 8

Node Copying for Protection Against Graph Neural Network Top...

引用

8th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Regol, Florence Pal, Soumyasundar Coates, Mark McGill Univ Dept Elect & Comp Engn 3480 Univ St Montreal PQ H3A 2A7 Canada

ISBN: (数字)9781728155494

ISBN: (纸本)9781728155494

Adversarial attacks can affect the performance of existing deep learning models. With the increased interest in graph based machine learning techniques, there have been investigations which suggest that these models are also vulnerable to attacks. In particular, corruptions of the graph topology can degrade the performance of graph based learning algorithms severely. This is due to the fact that the prediction capability of these algorithms relies mostly on the similarity structure imposed by the graph connectivity. Therefore, detecting the location of the corruption and correcting the induced errors becomes crucial. There has been some recent work which tackles the detection problem, however these methods do not address the effect of the attack on the downstream learning task. In this work, we propose an algorithm that uses node copying to mitigate the degradation in classification that is caused by adversarial attacks. The proposed methodology is applied only after the model for the downstream task is trained and the added computation cost scales well for large graphs. Experimental results show the effectiveness of our approach for several real world datasets.

关键词： Adversarial attacks Graph convolutional networks Semi-supervised learning

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Particle Flow Particle Filter using Gromov's method 8

Particle Flow Particle Filter using Gromov's method

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8th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Pal, Soumyasundar Coates, Mark McGill Univ Dept Elect & Comp Engn 3480 Univ St Montreal PQ H3A 2A7 Canada

ISBN: (纸本)9781728155494

Particle flow filters obtain impressive results in challenging high dimensional, non-linear sequential state estimation problems. In contrast to a particle filter, which uses importance sampling to approximate the posterior distribution of the state, the flow based algorithms solve a differential equation to migrate the particles from the prior to the posterior distribution. However, the particles after the flow are not true samples of the posterior distribution due to strong model assumptions required for the derivation of the flow and the approximations associated with the numerical solution. This affects performance adversely in many highly non-linear, non-Gaussian filtering problems. Particle Flow Particle Filters (PFPF) adapt the particle flow procedure to construct a proposal density inside the particle filter. These techniques can outperform the underlying particle flow algorithms by compensating for the approximations in the flow calculations via update of importance weights after the flow, at the cost of a negligible increase in the computational complexity. Most of the PFPF approaches have focused on using a deterministic particle flow. In this paper, we develop a PFPF algorithm using a stochastic particle flow based on Gromov's method. Numerical simulations are conducted to examine when the proposed method offers advantages compared to existing techniques.

关键词： non-linear sequential state estimation particle flow particle filter high-dimensional filtering

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INCORPORATING HAndCRAFTED FILTERS IN CONVOLUTIONAL ANALYSIS OPERATOR LEARNING FOR ILL-POSED INVERSE PROBLEMS 8

INCORPORATING HANDCRAFTED FILTERS IN CONVOLUTIONAL ANALYSIS ...

引用

8th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Crockett, Caroline Hong, David Chun, Il Yong Fessler, Jeffrey A. Univ Michigan Dept EECS Ann Arbor MI 48109 USA Univ Hawaii Manoa Dept EE Honolulu HI 96822 USA

ISBN: (数字)9781728155494

ISBN: (纸本)9781728155494

Convolutional analysis operator learning (CAOL) enables the unsupervised training of convolutional sparsifying autoencoders, taking advantage of large datasets to obtain high quality filters. In previous works, using CAOL within modelbased image reconstruction (MBIR) for ill-posed inverse problems significantly improved image reconstruction accuracy over existing MBIR using non-trained regularizers and generalized better than existing non-MBIR deep neural network approaches. This paper modifies the CAOL Procrustes filter update to allow some filters to be handcrafted. Doing so makes it possible to incorporate domain knowledge in the learning process and accelerates CAOL by learning fewer filters. We apply the proposed generalization of CAOL to MBIR for sparse-view CT. Numerical experiments show that 1) handcrafting discrete cosine transform filters can trade-off training time and reconstruction quality and 2) handcrafting filters based on finite differences can speed up training without sacrificing reconstruction quality.

关键词： Image reconstruction Training Computed tomography Discrete cosine transforms computational modeling Convergence TV

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PERFORMANCE ANALYSIS OF A LOW-RANK DETECTOR UndER TRAINING DATA CONTAMINATION 8

PERFORMANCE ANALYSIS OF A LOW-RANK DETECTOR UNDER TRAINING D...

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8th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Vallet, P. Ginolhac, G. Pascal, F. Forster, P. Univ Bordeaux Bordeaux INP CNRS IMS Talence France Univ Savoie Mt Blanc Polytech Annecy LISTIC Annecy France Univ Paris Sud Cent Suplec CNRS L2S Gif Sur Yvette France Univ Paris Sud ENS Paris Saclay CNRS SATIE Cachan France

ISBN: (纸本)9781728155494

We consider the problem of detecting a known M-dimensional target signature vector from an observation corrupted by an additive noise with unknown covariance matrix. In that case, standard statistical methods of detection usually assume that N "target free" observations are available to perform estimation of the noise covariance matrix. However, in several applications, the target signal may contaminate the training data, resulting in a deviation of the expected performance of the detectors. In this paper, we consider the performance analysis of two low-rank detectors under the assumption that N-c elements of the training data are contaminated by the target signal. More precisely, we derive the asymptotic false alarm and detection probabilities in the high dimensional regime in which both the dimension M, the number of training data N and contaminated data N-c converge to infinity at the same rate. Numerical simulations illustrate the fact that, despite the asymptotic nature of the analysis, the results obtained are accurate for reasonable values of M, N and N-c.

关键词： LR-ANMF Contaminated Training Data Random Matrix Theory

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AN EXAMPLE OF NON-STAndARD BEHAVIOR OF A MAXIMUM LIKELIHOOD ESTIMATOR IN THE LARGE SAMPLE REGIME 8

AN EXAMPLE OF NON-STANDARD BEHAVIOR OF A MAXIMUM LIKELIHOOD ...

引用

8th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Bacharach, Lucien Galy, Jerome Chaumette, Eric Vincent, Francois Renaux, Alexandre El Korso, Mohammed Nabil Univ Toulouse ISAE Supaero Toulouse France Univ Paris Sud SATIE Cachan France Univ Montpellier LIRMM Montpellier France Univ Paris Nanterre LEME Ville Davray France Univ Paris Sud L2S Gif Sur Yvette France

ISBN: (纸本)9781728155494

In this paper, the performance of a maximum likelihood estimator (MLE) for a signal model accounting for possible coherence of the signal sources is studied. It is done by combining a dynamical evolution model of the source amplitude, namely a Gaussian random walk, with the usual observation model, assumed to be Gaussian. This implies an underlying relation between the signal source parameters and both the mean and the covariance matrix of the observations, and hence leads to a more general model than those usually used in array processing, such as the conditional signal model or the unconditional one. The resulting so-called "generalized conditional MLE" can be expressed both in batch form and in recursive form, which is updated since a term was missing in a previous paper. In addition, we derive the Cramer-Rao bounds associated with a model of this type, accounting for the fluctuations of a radar target's backscattering coefficient. Simulation results highlight a non-standard behavior of the estimators: consistent but not efficient for frequencies, efficient but non-consistent for amplitudes.

关键词： parameter estimation conditional maximum likelihood estimators consistency efficiency Cramer-Rao bounds

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PROJECTION BACK ONTO FILTERED OBSERVATIONS FOR SPEECH SEPARATION WITH DISTRIBUTED MICROPHONE ARRAY 8

PROJECTION BACK ONTO FILTERED OBSERVATIONS FOR SPEECH SEPARA...

引用

8th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Araki, Shoko Ono, Nobutaka Kinoshita, Keisuke Delcroix, Marc NTT Corp NTT Commun Sci Labs 2-4 HikaridaiSeika Cho Kyoto 6190237 Japan Tokyo Metropolitan Univ Fac Syst Design 6-6 Asahigaoka Hino Tokyo 1910065 Japan

ISBN: (纸本)9781728155494

Many speech enhancement approaches generally estimate a source image on a reference microphone. For example, blind source separation approaches including independent vector analysis (IVA) usually determine the scales of the separated signals by using the projection back onto an observed signal at a reference microphone. However, when using distributed microphones, performance strongly depends on the selection of the reference microphone. Therefore, a reference microphone selection strategy or a speech enhancement method that does not depend on the reference microphone is required. Previously, [Araki et al., IWAENC2018, pp. 316-320, 2018] proposed a blind reference microphone selection strategy. In this paper, we propose a new projection back method that does not depend on the reference microphone. Instead of projecting back onto a reference microphone observation, this paper proposes projecting back onto beamformed observed signals. We also show an equivalent system that can easily realize the proposed projection back approach practically. Experiments with real recordings of meeting conversations show that the proposed projection back approach outperforms conventional projection back approaches.

关键词： Distributed microphone reference microphone selection independent vector analysis projection back (back-projection) meeting recognition

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